PELE is Silver Bulletin’s rating system for international soccer teams. Each team gets two principal ratings: a PELE rating describing its overall skill level and a Tilt rating indicating its propensity toward attacking or defensive play. Based on these ratings, we can evaluate past match results and forecast future matches. PELE ratings are updated continuously and backdated to 1872 (!).

PELE is also the backbone of our 2026 World Cup forecasts, set to be published later. We’ll update this article with any World Cup-specific adjustments once they’re ready.

We’re extremely proud of PELE, but it was a lot of work, and it’s not our simplest model. This article describes the system in detail.

Subscribe now

The basics of PELE

PELE stands for Predictive Elo with Lineup Equilibria. We know it’s a little bit nerdy, but this backronym captures most of the essential features of the system:

Predictive means that the goal of PELE is to probabilistically forecast the outcome of future soccer games. These aren’t the FIFA rankings: we’re not interested in which teams are most “deserving” of a particular slot. Rather, we’re looking for factors that have a predictive impact. International football teams play relatively few important games, and some of the most predictive indicators don’t derive from match results alone.

Elo means that PELE shares many properties with an Elo rating system — and indeed, PELE ratings are designed to be comparable to Elo ratings such as the FIFA rankings or the World Football Elo Ratings.1 As with other Elo ratings systems, PELE ratings are updated iteratively at the end of each match, and updates are zero-sum. (If Brazil beats Bolivia 4-1, whatever gain Brazil makes in its PELE rating is offset by a loss of points for Bolivia.) However, PELE deviates from traditional Elo ratings in other important respects, as we’ll describe below.

Lineup means we use player market values and age data from Transfermarkt to help anchor PELE ratings. We look at the market values for the top 23 nationals2 with their respective club teams, with some soft positional constraints. For years since 2005 (when Transfermarkt’s coverage begins3) team ratings are gradually “nudged” toward team aptitude as estimated by these player values. Player ages, weighted by market value, also affect the system — younger teams are expected to improve, while older teams are expected to decline. PELE also uses market values to help calculate whether a team’s strengths are oriented toward offense or defense.

In addition to market value data, we consider each country’s region, GDP, and football legacy. (The regions are not based on FIFA’s six confederations; we’ve developed our own system of 12 overlapping soccer regions, which we believe to be more predictive and more geographically accurate.) But these other factors are less important after the introduction of Transfermarkt data in 2005.

Equilibria serves as a catch-all to capture some other important features of the PELE system. PELE contains many parameters that work together to converge on (we hope) the best possible ratings. But these two mechanisms are particularly important:

• National team results essentially compete with the model’s expectations based on player market values, ages, GDP, region, and team history. The prior gradually pulls each team toward its long-run expectation based on these factors, while the match results push against this if a team consistently outperforms or underperforms PELE’s assumptions.

• PELE calculates two sets of ratings for each team: an Elo-like PELE rating that measures overall squad quality, and a Tilt rating that indicates whether the team tends to be attack- or defense-minded. Tilt ratings are based on (1) whether games involving the team tend to produce more or fewer goals than the model’s expectations, and (2) roster composition. PELE and Tilt ratings can be combined with global scoring trends to derive a score matrix for each game, i.e., the probability that Germany wins exactly 2-1 over Australia, or ties it exactly 0-0, etc. These can be used to estimate win/loss/draw probabilities for any given matchup, or to impute offensive and defensive ratings for each squad.

Scope and coverage of PELE

PELE covers international matches between teams that were both FIFA members at the time. There is an exception for teams that played widely recognized international matches before FIFA was formed in 1904. We think of these pre-FIFA countries as the “Original Ten” (analogous to the NHL’s “Original Six”). They are England, Scotland, Wales, Ireland, Argentina, Uruguay, Austria, Hungary, Belgium and France. Matches between “B-teams” or played under significant roster restrictions (i.e., the Olympics in recent years) are excluded.

Relying on FIFA membership dates essentially outsources decisions about when a national team reached “maturity” to FIFA, an organization we have mixed feelings about. Nonetheless, this provides a semi-objective basis for articulating “official” games among the myriad matches that have occurred over the past 100+ years between 200+ national or sub-national entities. We tested various alternatives to FIFA membership dates, but they only slightly increased the number of PELE-eligible matches while adding more subjectivity. FIFA membership dates are researched precisely.

Nonetheless, we had some decisions to make, particularly regarding which national teams are considered to be continuations of previous teams. Indeed, nearly every political dispute of the past century shows up in some form in historical soccer data. In making these decisions, we tried to be as consistent as possible based on the history, geography and economics of countries being reformulated. FIFA regards West Germany as having inherited pre-WW2 Germany’s football legacy, for example, and considers reunified Germany to be a continuation of West Germany. Our definitions are stricter and treat major changes in national boundaries as discontinuous, such as the split and reunification of Germany, the formation and breakup of the Soviet Union, and the breakup of Yugoslavia.

However, minor changes such as Timor-Leste splitting from Indonesia are tolerated. There are inevitably some judgment calls: we consider the creation and collapse of the United Arab Republic to be a discontinuous event for both Egypt and Syria, for example. In some cases, countries are considered dormant or hibernating and then “reincarnated” if they return to roughly their original boundaries: for example, the Baltic states before and after the formation of the USSR.4 In addition to the 211 current FIFA members, we calculate ratings for 17 nations that were FIFA members at some point but are now essentially defunct, e.g., Czechoslovakia — though defunct teams are excluded from most of the charts we display. Overall, almost 50,000 international matches since 1872 are included for PELE consideration.

PELE Phase 1: “basic” Elo-type ratings

Within our model, PELE is calculated in two phases. The first phase is “simple” and empirically establishes our parameters, such as the regional coefficients or the varying importance of home-field advantage. The second phase introduces mean reversion toward PELE’s expectations based on Transfermarkt player values and other data. This section describes Phase 1.

Some features of PELE are very Elo-like:

• Rating updates are zero-sum. Whenever a team gains ground from match results, this is offset by its opponent losing an equal amount of PELE rating points.

• By definition, the average PELE rating for the 211 active FIFA countries is 1500.

• PELE relies on contemporaneous information. In other words, Hungary’s rating on (for example) April 5th, 2012 is based on information that would have been available as of that date. We don’t go back and recalculate ratings based on post-facto information (i.e. Hungary lost to Norway, but it turns out that Norway was stronger than we assumed at the time).

Any Elo-type system also relies on a number of parameters that govern the overall behavior of the system. To the extent possible, PELE seeks to derive these empirically.5 The next section describes PELE’s approach to some of the most important ones.

Home-field advantage, match importance, and other parameters

We undertake highly detailed calculations for home-field advantage. In general, home-field advantage is very important for international soccer matches, and we think the impact of HFA is underrated by other systems. There are several components to our HFA calculations.

HFA varies over time, and this is derived empirically. In general, HFA increased after WWII, rose until the 1980s/1990s and has been declining since then, perhaps because travel accommodations for visiting teams are improving.

Travel distance impacts our HFA calculations.6 Traveling from Brussels to Amsterdam is less burdensome than flying from South Korea to Brazil. Travel distance is more important for neutral-site games.7

Altitude has a significant impact on teams like Mexico and Bolivia, who typically play their home matches at high altitude. These teams tend to have a significant home-field advantage because they are better acclimated to local conditions. Indeed, the formula is nonlinear. As you may have experienced yourself, engaging in intensive physical activity at 10,000 feet is more than twice as hard as at 5000 feet.

PELE also calculates customized HFA coefficients for each team. More precisely, these measure the spread in team performance, relative to PELE’s expectations, in home versus “nonhome” (neutral + road) games. Like the global HFA rating, each team's custom ratings evolve over time. In general, teams in far-flung and war-torn places tend to have larger HFAs, while richer nations in Europe and the Middle East tend to have smaller ones. Bolivia has the largest HFA, mostly because of its altitude. But the customized HFA adjustments are generally pretty conservative; this data is noisy.

Another important consideration is the importance of each match. A friendly will be taken less seriously than a World Cup knockout game. Some previous research, including my own work for ESPN’s Soccer Power Index, suggested that low-impact matches nevertheless provide substantial predictive value. But the actual situation is more subtle. Low-impact matches, such as friendlies, tend to predict performance in future low-impact matches, and most of the dataset consists of these. However, high-impact matches like the World Cup or the Euros tend to better predict performance in future high-impact matches. We considered developing ratings on two parallel tracks (i.e., a friendly match rating versus a “serious” match rating), but it wasn’t quite worth the added complication.8 Instead, we use these importance factors to weight the value assigned to each match. There’s roughly a threefold difference between the most and least important games.

• Friendlies: 0.5-0.7x9 multiplier

• Minor and friendly tournaments: 0.7-0.9x

• Regional tournaments and Olympics10: 0.7x (qualifiers)-1.0x (main tournament)

• Continental tournaments (e.g. the Euros): 1.3x (qualifiers)-1.4x (main tournament)11

• World Cup: 1.5x (qualifiers)-1.6x (main tournament)

There’s another subtle consideration in PELE: friendly matches tend to slightly collapse the difference in team quality. England will treat a World Cup qualifying match against San Marino with more urgency than a friendly against the same opponent. This is also accounted for by the model.

Differences between PELE and true Elo systems

There are also some important ways in which PELE deviates from traditional Elo ratings.

Some Elo-type systems adhere to a strict principle: winning always helps your rating. In these systems, if England beats San Marino 2-1, England will see some (typically very modest) rating improvement, while San Marino’s rating will decline. The polar opposite is to train the model on the score differential. For instance, if England is expected to defeat San Marino by 5 goals, the 2-1 scoreline will reflect that England underperformed the Elo expectation by 4 goals.

PELE basically strives for a compromise between these. Rating updates are based on what we call harmonic margin12 or “h-margin”. In h-margin, each additional goal has diminishing returns: the second goal in a 2-goal victory counts ½ as much as the first one, the third goal counts ⅓ as much, and so on. This is particularly important in soccer, where the current score substantially affects tactics: if you’re already ahead 3-1, whether to press for another goal or to collapse into a more defensive shell isn’t obvious. Matches won in penalty shootouts — there is some skill in penalties, believe it or not — are regarded by PELE as basically halfway between an outright win and a draw.

Initial ratings and the geography of international football

In Elo-type systems, each team or player usually starts with the same initial rating, typically 1500, before playing any games or matches. However, this can introduce some information loss. To a large extent, which teams are strong at football is predictable. From first principles, for example, you’d expect Argentina to defeat American Samoa. Even if you’d never seen a soccer game, you’d know that Argentina is much larger, has a much longer football legacy, and comes from a region where football plays a much more prominent role in the culture.

Technically speaking, PELE ratings also start out with a blank slate (everyone at 1500). However, we iterate the ratings dozens of times to converge on what we call a “GDP prior” for each country. The GDP prior is based on three factors:

• A country’s purchasing power parity GDP (adjusted for the standard of living) at the time it became eligible for PELE-rated matches. More specifically, we use the natural logarithm of a country’s GDP (there are diminishing returns to economic growth from a soccer standpoint). We use aggregate GDP, not GDP per capita, so both population size and living standards per citizen matter.13 Each country’s GDP is expressed as a fraction of world GDP at the time, so there is no bias introduced from when a country begins playing. Most of the GDP values are taken from the Maddison historical database, which is about 75 percent complete for the countries and years we care about. However, some GDP values are missing in Maddison: for example, Maddison lists the value for United Kingdom GDP rather than for the respective home nations (England, Scotland, etc.) while the home nations compete individually in football. The missing GDP values were filled in using Claude Opus 4.6, with careful oversight from yours truly. For the most part, this process is fairly straightforward. UK GDP can be divided between the home nations based on their relative contribution to the UK’s GDP, for example. Or we can estimate Estonia’s GDP prior to its being taken over by the Soviet Union based on the living standards at the time of comparable countries like Finland.

• A country’s “legacy year”: that is, the first year that it or one of its predecessors became eligible for PELE-rated matches. A longer legacy correlates with higher performance even years later, although this is the least important of the three factors discussed here. For the Original Ten, the legacy year is the year of its first widely recognized international match; for all other countries, it’s the year they became a FIFA member. Countries inherit legacy year status from any defunct teams that substantially overlapped with their territory: for example, both Northern Ireland and the Republic of Ireland inherit unified Ireland’s legacy year prior to the Partition of Ireland.

• Finally, we consider a country’s football region. PELE does not use FIFA confederations for anything: these do not correlate all that well with football performance, and can be influenced by political and other arbitrary factors. They can also be blunt instruments for continents as large as Asia. Instead, we crafted our own set of 12 regions, which deliberately contain some overlap. I’m going to be honest, we went through a lot of different versions of these.

Basically, we started out with the six populated continents and then carved out some logical geographic boundaries to create more precision:

• Oceania survives pretty much intact from conventional definitions. Note that Indonesia is considered a transcontinental country, belonging to both Oceania and Asia.14

• The Americas are also self-contained, but the conventional boundary between North and South America doesn’t adequately reflect the cultural distinctions within the region (and how this tends to map to football strength). Instead, we divide the Americas into three regions: 1) North America, defined as the continental territory stretching to the Darien Gap, and 2) the Caribbean, and 3) Latin America. Eligibility for Latin America is defined as any country in the Americas where the predominant language is Spanish or Portuguese. We would have included French also (it’s a Latin language), but there aren’t any FIFA members per se that would qualify on this basis.15 The decision to split off the Caribbean was important. It’s not a terribly important part of the world football-wise. But the idea of the regional groups is that there should be a coherent economic or cultural tie that can inform our priors. And the notion that, say, Haiti’s football performance tells you anything whatsoever about Canada’s or the United States’s rating felt like a big stretch. Even with the 3-way split of the Americas, there is deliberately some overlap: Mexico, for example, is both a North American country and a Latin American country. (We’re sort of implicitly creating a Central America region, in other words, which takes the average of the Latin America and North American values.) Note that Guyana and Suriname are not Latin American countries despite being in South America; instead, they’re considered Caribbean countries.16 Only Canada, the United States and Bermuda17 are purely in North America.18

• Next, we carved out a Middle East region based on its reasonably well-defined geographic boundaries. Most Middle Eastern countries are “taken” from Asia, which badly needs to be subdivided because it consists of so many unlike parts. However, the Middle East also has two FIFA members from Europe (Turkey and Cyprus) and one from Africa (Egypt, which is transcontinental due to the Sinai Peninsula). These countries are treated as hybrids between the Middle East and other regions.

• The other big carveout is Ex-USSR, and we’ll admit that this one is more debatable. The Soviet Union existed for 70 years and affected football culture and development pipelines in ways that are still persistent today. Former Soviet Republics like Moldova are historically weak at soccer as compared to Europe — as is Russia itself, really, given its GDP. But the Central Asian ex-USSR countries are relatively strong as compared with most of Asia. (And Central Asia doesn’t fit neatly into any of our other regions anyway.) The Baltics — Estonia, Latvia, and Lithuania — are considered hybrids between the Ex-USSR and Europe because they were the only former Soviet countries to have football teams prior to the formation of the USSR. We avoid making exceptions based on political developments: Russia is at war with Ukraine, but the war is recent, and Ukraine isn’t (explicitly) a NATO or EU member.

• That leaves Europe as everything in the continent outside the former Soviet Union. We considered some further divisions, e.g., lumping in some nations with ex-USSR into an “Eastern Bloc” region. But these definitions are fuzzy and historically contingent19 and footballing strength has historically been roughly even across Europe, especially once controlling for GDP. Both Western European countries, like England, and Eastern European ones, like Hungary, have strong football traditions.

• Asia and Africa, on the other hand, are large and warrant further division. We already carved out the Middle East and the Asian parts of the ex-USSR region (basically Central Asia) from the rest of Asia. The remaining portion of Asia nevertheless spans a large landmass and more than half the world’s population. The logical division is between South Asia and East Asia. South Asian nations are almost universally underachieving at soccer. East Asia, conversely, contains some of our bigger outliers — notably, South Korea and Japan have become very good at soccer in contrast to the rest of the region. But China is underperforming. Still, priors are priors for a reason: they’re good default assumptions that are sometimes violated. We considered creating an “Asia-Pacific” region that would also include Australia, but this went too far down the road to gerrymandering based on footballing strength. However, Southeast Asian countries are hybrids between South Asia and East Asia in our scheme, so this creates another implicit region along the lines of Central America.

• Finally, Africa is often treated as an undifferentiated mass by Westerners, but nearly all the stronger African sides you’d think of are either in North Africa or West Africa. These countries have different religious and cultural traditions and different relationships with colonial European powers that correlate with football strength. Therefore, Africa is divided into three regions: North Africa, East Africa, and West Africa, with some overlap. North Africa is reasonably well-defined by geographers, so we’re basically splitting the rest of the continent — Sub-Saharan Africa — into two parts. West Africa includes some of the continent’s strongest football countries. East Africa is a slight misnomer: it might be labeled East/Southern Africa if we were even more precise. But teams from that part of the continent don’t tend to reach the same heights.

In case you’re curious, the order of the regional coefficients in terms of how well they predict team quality is:

1. Latin America

2. West Africa

3. Europe

4. North Africa

5. Caribbean

6. East Africa

7. Middle East

8. Ex-USSR

9. North America

10. Oceania

11. East Asia

12. South Asia

This ordering might be surprising, but remember that these ratings control for GDP (and legacy year). Europe might be better than West Africa at football, but its teams have longer histories and it’s much wealthier; the residual coefficient for West Africa is (slightly) higher once you control for that. The Caribbean surprised us as a modestly high-scoring region, but it has many tiny countries that punch above their weight.

PELE Phase 2: advanced ratings with mean reversion and player market values

In traditional Elo-based systems, ratings only change after games/matches are played. Silver Bulletin’s other sports models, like COOPER, already violate this principle because ratings are partly reset or reverted toward priors at the start of each season.20 In international football, however, there’s no “season” per se. So instead, each team’s ratings are very gradually “nudged” toward PELE’s expectations every day based on PELE’s priors. The magnitude of the nudge was calculated by comparing teams’ Phase 1 PELE ratings with their PELE ratings two years later. This allowed us to determine which factors predicted changes in team performance.

However, this is a deliberately slow-moving process: it would take decades for an outlier like Japan to fully converge on its prior. Moreover, teams can push away from this pull toward the prior with consistently good match results. Elo-type systems use a “K-factor” to determine how much the ratings change in response to new results. In Phase 2, our K-factor is set slightly higher than in Phase 1; in other words, match results matter slightly more, giving teams an opportunity to offset the mean-reversion. In practice, the prior is relatively less important for countries that play many international matches, but matters more for teams that compete infrequently in important games.

Prior to 2005, this mean-reversion is just based on the GDP prior: in other words, a country’s GDP, its legacy year and its region. It doesn’t have much effect: regions and legacy years do not change at all, and with some exceptions like China, relative GDPs only change slowly. Thus, prior to 2005, the Phase 2 mean-reversion process barely has a discernible impact on the ratings.

Beginning in 2005, however, an extremely valuable set of data becomes available: player market values and ages as estimated by Transfermarkt. Transfermarkt is exceptionally comprehensive, to the point where we can basically estimate a snapshot of any national team’s market value for any given date since Jan. 1, 2005. These can have a big impact: Norway’s rating is clearly boosted by Erling Haaland’s presence, for instance.

The Transfermarkt data does require some work to process, however. While we could simply add up the aggregate market value for every player of a given nationality, this might introduce coverage bias.21 Furthermore, football matches are 11 players a side, so that France’s 200th-best player is better than Canada’s 200th-best player is irrelevant. Instead, we construct a 23-man roster for each past date. (World Cup rosters traditionally consisted of 23 players, although this has now been expanded to 26.) These rosters are populated in descending order of Transfermarkt values, with a hard constraint on goalkeepers (exactly 3) and some softer constraints on the other positions (a team can’t field a lineup consisting entirely of strikers). The starting 11 gets full credit for its market values, while the reserves (players #12 to #23) receive partial credit based on a sliding scale (the first several reserves get nearly their full market value; the end of the bench doesn’t). If a team doesn’t have enough players listed in Transfermarkt to fill out a 23-man roster, remaining slots are treated as having zero market value.

For the 2026 World Cup specifically, we’ll use actual team rosters rather than our guesstimates once they become available. These will account for injuries and player absences, and players who wind up on different countries than their primary Transfermarkt classification. For “regular” PELE, however, the rosters are constructed algorithmically.

We also compute team ages based on this data, weighting ages based on each player’s market value to their squad. For teams without full rosters, ages are adjusted toward a mean of 26.5 years. Younger teams are usually projected to improve, and older ones to decline.

One big question is whether Transfermarkt data is biased toward certain countries, and particularly toward Europe, since the most valuable club teams in the world are overwhelmingly concentrated there. We investigated this carefully and found little overall pro-European bias. However, through analyzing historical World Cup rosters, we discovered one important mechanism we need to account for. In certain countries, some players tend to “stay at home” or play for other teams in their home regions, even though they would be skilled enough to play for a top-flight European club if they wanted to. After careful investigation, we found that three groups of countries are affected by this:

• Latin America. Many strong players from Mexico and Central America play in Liga MX rather than going to Europe. To some extent, South American players can also elect to remain with their domestic clubs.

• Wealthy Asian countries: specifically the Gulf States and the five OECD members from Asia or Oceania (Japan, South Korea, Australia, New Zealand, Israel). These countries often have vibrant domestic leagues and can offer a high quality of life to local stars.

• Finally, six geopolitically isolated countries (North Korea, Eritrea, Cuba, Iran, China and Russia22) that impose harsh constraints on emigration.

Countries from these regions are underrated by Transfermarkt values, and so we correct for this. It’s not that the Transfermarkt values are necessarily “biased” against these countries but that players from these regions will sometimes opt not to fully maximize their market value, instead making a sacrifice for the comforts of home (or they won’t be given the choice in the case of a country like North Korea). Nearly all elite players from Africa and Anglo North America migrate to other countries for their club play, however.23

There’s one other subtle factor in calculating our historic ratings. Suddenly, a whole bunch of new data becomes available on 1/1/2005. Rather than easing into the new regime gradually, we found PELE performed considerably better in the early Transfermarkt era (~2005-2010) if we made a one-time step-function adjustment to team ratings on 1/1/2005 to account for the new data. Essentially, this step-function banks in 10 years of reversion toward the new, more informed prior. If you look very carefully at PELE’s historic ratings, you may see bigger changes in 2005 than in other years.

However, as valuable as the Transfermarkt data is, the “Phase 1” PELE ratings are already pretty smart based on match results and the GDP prior.24 The player data is certainly worth worrying about and aligns PELE with betting odds more precisely, but it’s an important factor rather than a dominant mechanism in the system.25

Tilt ratings and expected goals

So far, I’ve barely even described one of the most important features of our system: Tilt ratings.

When I created SPI for ESPN for the 2010 World Cup, our system had separate offensive and defensive ratings for each team, characterized as their projected number of goals scored and allowed per match. The offensive and defensive ratings could then be combined to project scores and calculate overall quality ratings.

While I think SPI was a smart system, I actually think this technique wasn’t well-suited to soccer. Unlike a sport like American football, offense and defense are fluid in the sport. There isn’t a distinct platoon on either side. And soccer is deeply tactical: a team might play completely differently with a 1-goal lead than a 1-goal deficit. A game between two quality opponents could easily turn into a tight match or a shootout.

However, we can evaluate which teams tend to be involved in high-scoring games. This is basically what Tilt does: it measures whether matches featuring the team tend to involve more combined goals for both sides (positive tilt) or fewer (negative tilt). So while PELE is our measure of overall team quality, Tilt is more a measure of mindset. In sporting gambling terms, you’d use PELE to set the point spread or the odds of a team winning, and Tilt to project the over-under.

However, having a positive or negative tilt rating isn’t inherently good or bad. Teams can succeed — or fail — with more attacking styles or more defensive ones. In fact, PELE and Tilt ratings are, by design, mostly uncorrelated (the cool kids would say they’re orthogonal):

We can, however, combine PELE and Tilt to project goals scored and allowed in each match. We do this in the round-robin table of the PELE landing page, for example, which simulates a round of matches on a neutral field between all of the 211 FIFA teams and all 210 of their opponents.26 A team’s projected number of goals scored and allowed against the round-robin are basically equivalent to SPI offense and defensive ratings, just with the process reversed:

• SPI: Offensive rating + Defensive rating → Overall quality rating

• PELE: Overall quality rating (PELE) + Tilt → implicit offensive and defensive ratings

This process can also be used to project the odds of a win, loss or draw between any two teams under any circumstance (home, road, neutral, etc.). Indeed, this is the process we’ll use for our probabilistic World Cup projections. But it is among the more complicated aspects of the system.

Our starting point is to create a projection of the number of goals in the match, derived from our database of nearly 50,000 historical results. The ingredients in this projection are as follows:

• By far the most important factor: our rolling leaguewide baseline of overall goal-scoring in typical matches. Soccer has gone from featuring 4-5 goals per game at its inception to just 2-3 goals (combined between both teams) per match now. Our model calculates this baseline by averaging the past 5 years of data from all international matches, with a trendline term (is overall goal-scoring rising or falling globally?) and a correction that downweights outlier matches (Australia 31–0 American Samoa).

• We also account for the difference in team quality, as measured by PELE.27 Matches between teams with large rating gaps tend to yield much higher scores.28

• The importance of the match: more important matches tend to play tighter and feature fewer goals. In general in soccer, higher quality of play is associated with fewer goals: it’s completely different from something like the NBA in this regard.

• And whether the game was played at a neutral site; neutral-site matches tend to play a little more wide-open.29

Next, we looked at whether there were systematic differences in scoring propensity, i.e. whether some teams tend to produce higher-scoring games beyond what their PELE rating might imply. Our Tilt ratings are the solution to this: Tilt is basically the difference between actual goals and expected goals based on our formula (regressed strongly toward a mean of zero because goals are rare in soccer and the raw signal is noisy). If matches involving Germany (canonically attack-minded) tend to produce higher scores than others, Germany will get a positive tilt rating, while a negative rating indicates a team whose matches tend to produce lower-than-expected scores like Senegal.

Tilt ratings have two subcomponents. Since Transfermarkt player data begins in 2005, we can sum up all the player values and allocate them to either offense or defense based on their positional assignments. As of mid-2026, for example, most of Norway’s value is concentrated on offense (Haaland) while most of Nigeria’s is on defense. The overall split is designed to be 50/50 based on these positional allocations:

The resulting effect on goal-scoring is what you might predict: teams whose strengths are concentrated on offense tend to produce higher scoring games.

The second component is tactical tilt. Figuratively, it’s whether a team prefers to play a more open/attacking style or a tighter, more defensive one, trends that can be persistent over decades based on the coaching regime and the soccer tradition in each country. More literally, it’s the residual number of goals scored relative to PELE’s baseline expectations.30 Tilt ratings are strongly hedged toward a mean of zero since measuring this attack/defense tendency is noisier than measuring differences in team quality.

The score matrix and future match predictions

From PELE and Tilt, we can project the number of goals for each team in any given match. Technically, the way the model does this is by first projecting overall goals based on leaguewide trends and each team’s Tilt rating, and then dividing the goals between the projected winner and loser.

But a projection like Spain 2.7-Finland 0.8 only tells you so much. Soccer is a low scoring game with a lot of draws, so the precise number of goals matters. Spain can score exactly 2 goals or exactly 3 goals or zero goals or some other integer, but they can never finish the game with 2.7 goals.

The Poisson distribution is designed to handle this sort of situation31 and is the traditional choice in soccer models. But it tends to have some problems. Particularly, Poisson understates variation too much, tending to underestimate both the number of draws (especially 0-0 draws) and the number of blowouts (Germany 7-1 Brazil). PELE’s solution32 is a negative binomial distribution with a correlation term. It’s not so important you know precisely what this means but that you capture this basic intuition: one team’s score affects the other team’s tactics. A 0-0 match tends to play tighter 75 minutes in than 3-2: it’s already been a low-scoring game but it will often play tighter still from that point forward. Our method does a good job of matching empirical goal-scoring distributions: e.g., there are about the right number of draws and blowouts in PELE.

PELE calculates a precise score matrix for each game (the chance Team A beats Team B by a score of exactly X-Y). It then sums up the cells in this matrix to estimate probabilities that the game ends in a win, loss or draw. For instance, here is the matrix for the United States’ first World Cup match against Paraguay on June 12, projected to be a low-scoring affair:

There are some complications introduced by extra time and penalty kicks, such as will be used in the World Cup knockout stage. Games eligible for extra time potentially increase the amount of gameplay by roughly 33 percent and empirically convey a slightly heavier advantage to favorites. PELE accounts for all of this. Historically, about 30 percent of draws after regulation are resolved in extra time before penalties, so PELE takes 30 percent of games projected to be draws in regulation and assigns a winner. The other 70 percent of extra-time games go to penalties. Based on our analysis of several hundred past penalty shootouts, there actually is some skill in them; better teams and home teams tend to win shootouts more often, though edges in most situations are rarely more than about 60/40.

World Cup adjustments

We’ll update this document with more detail on our official World Cup forecast once it’s ready. Two things we plan to account for are using actual World Cup rosters once they’re announced instead of our algorithmically generated rosters, and accounting for the incentives in each match. In matches where both teams would advance with a draw, for example, teams have shown a remarkable aptitude to conspire to secure one.

Subscribe now

If you’re an avid listener of the Bill Simmons podcast1, you might have heard a recent bit he did with Zach Lowe, where they pondered whether this was the NBA’s weirdest season ever. For what it’s worth, excluding global pandemics, that claim has a lot of merit. The Clippers started the season near the bottom of the league amidst allegations they paid Kawhi Leonard 28 million dollars under the table; a gambling scandal led to the arrest of a coach and a player on the third day of the season; Bam Adebayo scored 83 points in a game; and fully a third of the league was literally trying to lose games.

It’s hard to say whether the weirdness has undermined the NBA’s effort to rebrand the league in a forward direction. With the league moving most of its games to NBC and Amazon Prime, the presentation has focused less on its legacy stars — Steph, Durant, LeBron — and more on its insurgent young core. The guy everyone wants to watch in the playoffs is Victor Wembanyama, not LeBron. But the shift has been backed by an influx of talent. I spent a lot of time analyzing this while building out our NBA draft model, PRISM, and it was hard to miss just how much talent has entered the league over the last few years.

This year’s Rookie of the Year race has been one of the best arguments for that explosion of talent — and, fittingly, one of the weirder ones we’ve seen in a while. Kon Knueppel and Cooper Flagg, former Duke teammates and roommates, traded the #1 spot on NBA.com’s Kia Rookie Ladder all season long. Flagg, the No. 1 overall pick (and the namesake for our college basketball model) has the highlights — 51 points against Orlando, a 42-point game in Utah before he turned 19, the kind of explosive, load-bearing performances that typically lock up the award. Knueppel, taken three picks later, has been the quieter storm: he leads the league in three-pointers made while shooting 43 percent from deep, and played a leading role in Charlotte’s leap from 19 to 43 wins. Flagg is ahead in counting stats per game across the board, but Knueppel has the efficiency and wins.

Subscribe now

Is the stats case even close?

There have been attempts to flatten Knueppel's case into some version of "he shoots threes really well," which ignores the off-ball gravity, passing, defensive IQ, and the way he's made Charlotte's entire offense function differently. Flagg’s case is also bigger than “he’s dominating the box score on a bad team” and people should be accounting for poorer context — the chaotic situation in Dallas — which makes it harder for him to produce at the same efficiency as Knueppel.

This framing is not so uncommon in awards discussions — the player with higher counting stats against the player with higher efficiency — and this type of discourse tends to get fans riled up, which is probably why mainstream outlets love to play into it. Analytical types tend to frame basketball around impact, which cuts straight through the noise of trying to calibrate all of these stats. While I don’t think ranking players is as cut-and-dried as just taking some aggregation of their impact metrics, there isn’t really an argument for Flagg from a pure stats point of view. On a per-possession level, he lagged far behind Knueppel.

Voters aren’t answering the same question

Of course, there is an argument against advanced metrics here that isn’t just luddite pessimism — stats like EPM, part of a family of RAPM-type impact stats, don’t always do the best job of distinguishing between a player’s impact and the difficulty of their role. In fact, just the opposite: RAPM and its descendants measure how team-level point differential shifts when a player is on vs. off the court and implicitly tend to reward being on good teams. That’s genuinely useful, but it tends to measure how good a player is at the thing they’re currently being asked to do — as opposed to either how rare the thing they’re being asked to do is, or what they might be able to do in another context.

Knueppel’s role in Charlotte — moving without the ball, spacing the floor, catching and shooting threes within a functional offensive system — is considered an easier adjustment in the NBA. He averages under two dribbles per touch — whereas Flagg’s role in Dallas, which involves a lot of primary creation, high-usage shot generation, and running the offense as an 18-year-old on a team with no other reliable engine, structurally suppresses the efficiency numbers these models care about.

For what it’s worth, Rookie of the Year voters traditionally do care about how much of an advantage creator you are. Knueppel would have the lowest on-ball percentage of any Rookie of the Year since Karl-Anthony Towns won the award in 2016.

So, yeah, EPM can tell you that Knueppel's minutes are technically more productive than Flagg's, but that doesn't settle the vote the way some analytics people want it to. The strongest version of the Flagg case isn't really about counting stats or highlights. Rather, it's a reframing of what the award is measuring — that Rookie of the Year should go to the player who has adjusted to the NBA best, and under that lens, Flagg's case sharpens: he's 19 years old, carrying a bad roster as its primary creator, producing against elite defenders scheming against him every night, and doing it at a level only LeBron, Luka, and a handful of other all-time teenagers have matched.

Still, that framing is building in some implicit credit for potential — Knueppel would be a very valuable player with or without rookie status, while we can’t definitively say that about Flagg this year. And even if we adjust for playtype difficulty, Kon is still ahead of Flagg in efficiency.2

But some Rookie of the Year voters aren’t necessarily even answering the question of which rookie was the best player so much as taking their mandate to mean “who will be the best player?” — and under that criterion, Flagg takes the cake.

Advanced stats like eWINS are admittedly not for everyone. They expose some inconvenient truths: rookies usually stink on defense, and their shiny, high-volume box score production is often paired with middling (at best) efficiency. The arrangement is a win-win — the player gets seasoning and sneaker deals, while their teams remain comfortably within the lottery — so long as you don’t ask too many questions of the data. And most voters don’t. Jae’Sean Tate, for example, had the highest eWINS in 2020 and got zero first-place ROTY votes — most of his value came on the end of the floor voters aren’t paying much attention to.

This year’s race only halfway matches that template. Kon and Cooper have been roughly similar defensively by most advanced metrics — and both are rated as about league average, which is unusual and promising for a rookie. Yet Flagg carries the reputation of an elite defender because without enough signal in the box score, the media is defaulting to their priors about his long-term defensive projection.

There’s also a more subtle issue: when voters treat ROTY as a forward-looking judgment rather than a full-season retrospective, they naturally time-decay the regular season. Victor Wembanyama was clearly better than Chet Holmgren by the end of 2022-23, and voters treated that midseason leap as license to wipe away his early-season struggles.

All of this would be forgivable — maybe even commendable — if voters were actually good at picking the best future player. The problem is they aren’t: take a look at that table above. Tyreke Evans over Steph Curry? Or even Kyrie over Kawhi? ROTY voters have often systematically underrated the lower-drafted guy.

The prediction markets won't sit still

But what makes the race genuinely weird is a string of late reversals in the conventional wisdom — and an unexpected twist involving former ROTY Luka Doncic. When I first planned on writing about Knueppel, I was confident he’d more or less locked the award up, and as of a few weeks ago, the prediction markets agreed, giving him a 94 percent chance at the trophy. Then Flagg put together back-to-back statement games, and Knueppel’s odds cratered to as low as 21 percent.

Silver Bulletin has covered prediction markets extensively in the past.3 They’re often very smart, and they’re certainly not easy to beat. But as Nate wrote about the election of the new pope last year — Leo XIV had been trading at only 1 percent odds — they aren’t necessarily at their best when trying to anticipate the behavior of a cloistered group of insiders. Polymarket’s ROTY contracts have millions of dollars in volume, which don’t exactly approach something like its presidential election markets, but are also not the kind of market you’d expect to flip 50 points in a day.

The first thing worth flagging is that we actually do have information about how voters feel. Only a few days before Flagg’s big weekend earlier this month, ESPN conducted a straw poll of 100 media members, some of whom are in the pool that actually votes on the award. Knueppel received 80 first-place votes to Flagg’s 20. So we have a prior suggesting as of just two weeks ago, Knueppel had a decent lead over Flagg — a lead hard to eclipse even with a full week of regular-season dominance.

Tim Bontemps, the reporter who conducts the straw poll, said something interesting on the Hoop Collective podcast the other day — that an 80-20 split in a room of 100 voters doesn’t mean each individual voter is 80 percent sure. It’s more like a collection of 60-40 decisions that mostly broke the same way. That’s a good point, but let’s analyze it. The lower bound on what 80-20 means is that every single Knueppel voter was close to 51-49 and just barely tipped his way. The upper bound is that all 80 were completely certain. The vote count alone can’t distinguish between those worlds, and Bontemps is right that we shouldn’t confuse margin of victory with depth of conviction.

But we can actually test what happens between the straw poll and the real thing, because Bontemps has been running the MVP version of this poll since 2017. In seven seasons of MVP straw polls, the leader has won the actual award every single time. In 2021-22, Nikola Jokic’s straw poll lead of 62-29 widened slightly to 65-26 in the real vote. In 2020-21, his 89 percent straw poll share held almost exactly at 90 percent. The two most recent seasons did narrow slightly — Jokic went from 85 percent to 80 percent in 2023-24, and SGA went from 77 percent to 71 percent in 2024-25 — but neither came close to flipping.

The bull case for Flagg probably comes from 2022-23. That year, Joel Embiid led the straw poll, but it was essentially a tie: he actually had two fewer first-place votes than Jokic. When the actual ballots came in, Embiid won 73-15 in first-place votes. Despite the close straw poll, voters didn’t scatter — rather, they broke hard in the same direction.

Steve Aschburner, the NBA.com writer who runs the Kia Rookie Ladder and is himself a voter, published his final ballot on April 8th with Knueppel at No. 1, and directly addressed the weekend performances that swung the odds: the 96-point two-game stretch swayed oddsmakers, he wrote, but didn’t eclipse Knueppel’s body of work. Historically Aschburner’s Rookie of the Year ladder has correlated strongly with the actual vote share — his one recent miss being Evan Mobley over Scottie Barnes in 2022, which was decided by 5 first-place votes, the narrowest under the current format. Bill Simmons has also rallied around Knueppel. Two voters, neither of whom are necessarily advanced-stats truthers, have come out with their vote, and they’re sticking with Kon.

That raises another issue: Could there be some form of insider trading? It’s obviously not an out-of-bounds question. Still, the scope for it might be limited on this market — no single voter’s private knowledge of their own ballot moves the needle much in a 100-person electorate, and any conspiracy between media members to collude on voting would be difficult, especially before the ballots come out. Building sharp models in awards markets is also difficult — these are one-off events with small voting bodies, so there’s no clean way to build a model for an event where the criteria can shift with narratives and storylines.

The market has corrected multiple times since the initial flip, with Knueppel briefly reclaiming favorite status before Flagg snatched it back over the last few days. Kon didn’t help his case in the Hornets’ dramatic play-in win on Tuesday — he had one of the worst games of his career, capped by a late-game benching. Despite being the NBA’s leader in threes as a rookie, he was absent from the floor over the last seven minutes, even when the Hornets desperately needed a three to send them into overtime.

And yes, even though the play-in game isn’t technically part of the regular season — statistically, it exists in purgatory, since it isn’t part of the postseason either — it’s going to bleed into voter perception. Because of Luka Doncic’s minimum games appeal, ballots were delayed, and Knueppel’s performance on Tuesday might empower some of those wishy-washy 51-49 Kon voters to go with Flagg, even if that’s technically not within the criteria of the award. The market certainly believes that voters will be swayed — following Kon’s game, Flagg is now inching toward being almost a 3:1 favorite.

The prediction market feedback loop

It would be easier to map out this race if close Rookie of the Year races happened more often. Over the last twenty years there have only been two nail-biters — Scottie Barnes over Evan Mobley in 2022, which came down to a 15-point margin and is still the closest vote since the current format started, and Evans over Curry in 2010.

Everything else has been a blowout, or close enough to one that the discourse around it never really mattered. Wemby was unanimous, KAT was unanimous, Lillard was unanimous, and even the races that felt close at the time, like Ben Simmons and Donovan Mitchell in 2018, turned out not to be. Perhaps the closest precedent is 2007-08, when Kevin Durant took home the trophy despite significantly worse advanced stats than Al Horford, who helped lead the Hawks to the playoffs as an 8th seed. That would seem like bad news for Knueppel, but the voting body is different now, in that they’re more receptive to the use of advanced stats than in 2008.

So, historical analogue or not, when people look at the current market and say it's behaving weirdly, part of what they're picking up on is that we don't really have a reference class for what a close ROTY race is supposed to look like, especially between two players with such different profiles. Some of what looks like noise is just what happens when a binary outcome gets close enough that small inputs start to matter: every new game has more leverage in a way that doesn’t seem rational on the surface.

Of course, that’s really just another way of saying buzz is dominating a race that, on the merits, might not be that close. And maybe this is where the whole thing gets a little recursive. The “buzz” isn’t just a reflection of the race, it’s also an input into it. On one side you have a player who’s had a great rookie year, and on the other, a guy who looks like a future superstar, even if he was an outright negative earlier in the season. Those are different kinds of cases, and voters trying to be rigorous about their ballot are still going to get nudged by which story feels more compelling in a given moment. A 30-point Flagg game in March probably carries different weight than a 30-point Kon game because of what it implies about the next 5, 10 or 20 years.

Prediction markets potentially feed into the same loop. They’re aggregating information, but in a race like this, most of what they’re aggregating is the buzz itself — which podcasts said what, which highlights went viral. You know, the vibes. It’s not that prediction markets are broken per se, it’s that there’s just not much independent information for them to work with — so they end up measuring the zeitgeist and handing it back with the authority of a price.

Now, do I think Kon is still a good bet at 28 percent? I think so, because the vote trackers show a fairly close race, and the priors for him are pretty strong. Still, the ballot delay gives recency bias more room to breathe than it usually gets, and if Kon has another rough night tonight it’s going to get harder to shrug off his doubters. Two weeks ago I would’ve told you this race was over. While the underlying evidence from an 82-game season hasn’t changed very much, if Flagg is holding the trophy in a few weeks, I will no longer be surprised.

Don’t get me wrong: I’m excited about the NBA playoffs. But my interest in the NBA regular season has been flagging in a way that it hasn’t in a long time. There’s a simple reason: it’s the tanking. A full third of the league — five teams in each conference — basically gave up on the season at some point between October and February. The identities of the 10 playoff and play-in teams in each conference were practically locked in a few weeks ago — and they’re literally locked in now.

The NBA is acutely aware of the issue, though it’s unclear whether the league considers it a real problem or just a PR issue. (Here’s why it’s an actual problem: about half the games on any given night “feature” a tanking team. I’m not about to pay $200 a ticket to see a team that isn’t even trying to win play the Knicks at MSG.) And the solutions it has proposed are mostly tinkering around the edges with the current rules, full of the same kinks and quirks that will be exploited by future Sam Prestis and Daryl Moreys.

Maybe that’s because it’s not quite clear what the league wants. Losing isn’t the same thing as tanking, and perhaps it’s fine to reward the former but not the latter. Up to a point, you might want to help teams for sustained losing as opposed to a one-year fluke like this year’s Indiana Pacers, but maybe not if they’re perpetual basement-dwellers. Other things equal, you probably don’t want the same team to get a #1 or #2 pick several times in a short period or for a potential contender to luck into a top four pick. Meanwhile, the myriad of pick swaps and trade conditions creates weird cliffs in the system and perverse incentives. And while there’s a lot of focus on losing teams, it’s also not clear you want winning teams to be able to trade draft capital seven years out when ownership and management may turn over anyway once it’s time to pay the piper.

So what if I told you there’s an alternative that could accomplish all of the following?

• Introduce explicit penalties for tanking or for repeatedly failing to reach even the play-in game.

• Substantially simplify trade rules.

• Flatten the lottery odds to the extent desired by the league, especially toward the top.

• Protect teams against themselves by limiting the amount of future capital they can trade.

• Eliminate cliffs in the system, such as when a team would have to give away its pick if it climbs above a given position in the standings.

• Reduce the element of luck: bad teams would still be rewarded with better capital, but they wouldn’t be quite as subject to the literal bounces of the lottery balls.

• Reduce the chance of already good teams landing top picks.

• Put some curbs on the same team repeatedly winning top picks.

• Provide teams with more flexibility to suit team needs, such as carrying over capital from season to season or acquiring depth rather than a single highly touted prospect who isn’t a good fit.

• Usually guarantee a team the opportunity to call its shots if there’s a player it really likes in the rough vicinity of its board.

• And make “draft night” — now auction night — even more strategic and fun.

Sounds pretty good, right? But it does require one leap of faith. As I’ve been teasing at, we’ll be getting rid of the “draft” per se. No, I’m not advocating for all players to simply become free agents, although there are worse ideas. I don’t think it’s inherently a bad idea to “redistribute the wealth”. But every fantasy football nerd’s favorite solution, an auction rather than a draft, provides for a lot more flexibility to tweak the knobs to the league’s desired levels.1

Subscribe now

True, you could accomplish some of these things within a draft format. But once I started down this path, the auction route proved far more flexible. For instance, you can penalize teams in a proportionate way for tanking by deducting a little bit of capital instead of either giving them a slap on the wrist or employing a “nuclear option” like taking away draft picks entirely.

The basics of ARC

The gist of the system is this: the draft would be replaced with an auction-type system, where teams bid on eligible players each June with Acquisition Rights Capital or ARC. I thought about calling this something cheeky like “DraftBucks”, but a) there’s not really a draft any longer, and b) I wanted to go with the sort of technical term that could plausibly appear in the CBA. ARC, of course, can be traded, but the mechanisms for doing this are simpler than under the current draft format. A unit of ARC can alternatively be designated with an arc symbol ⏜.

Needless to say, the devil is in the details for any proposal like this. There are 21 rules governing the use of ARC. You could tinker with most of them without really messing with the overall spirit of the system, however.

Let’s start with the two most important rules:

1. At the end of each season, teams receive between 25 and 100 ARC based on their order of finish. As under the current system, playoff teams are slotted 1-16, (though the two NBA finalists would be ranked first and second2) followed by non-playoff “lottery” teams.

2. There are no more swaps or conditional picks. Instead, all trades involving future asset rights are made with ARC. All trades or player acquisitions involving ARC must be made in whole units; ARC cannot be fractionalized.

Rule #2 is, I hope, relatively self-explanatory. ARC is fungible, so there’s no benefit in trading, say, Miami Heat ARC for Minnesota Timberwolves ARC. So, instead of “the Rockets acquired Giannis Antetokounmpo for Reed Sheppard, Jabari Smith Jr. and [insert long and convoluted description of picks and swaps]” the transaction wire would read as “the Rockets acquired Giannis Antetokounmpo for Reed Sheppard, Jabari Smith Jr. and 50 ARC”. There’s actually a lot less to keep track of.

As for the allocation, here’s what I had in mind:

There are some subtleties here. Under the current system, the steepest part of the curve, in terms of the expected value gain from dropping an additional position in the standings, is roughly between the 20th and 23rd positions. For instance, the 20th-ranked team has just a 9.4 percent chance of lucking into a top-four pick; that nearly triples to 26.3 percent for the 23rd-ranked team. Teams in this region can usually play a little bit and have a plausible chance of at least making the play-in round. But the NBA’s current setup strongly deters them from competing; our ARC system would reduce the marginal gain from losing in this region of the curve by roughly half. Instead, under ARC, the steepest part of the curve is for teams that have made playoffs anyway; it’s flatter toward the top and the bottom. And the ARC allocation for the three worst teams would be completely flat, similar to how the league awards the same 14 percent chance of receiving the #1 overall pick to the bottom three teams now.3

Here’s how I estimate what each draft slot is worth. The metric we use to evaluate player outcomes in our new NBA draft model, PRISM, is based on Estimated Plus-Minus (EPM). We tallied the number of wins a player produced above replacement level in his first seven NBA seasons (when he’s most likely to be on some sort of cost-controlled contract with his original team4), rounding below-replacement-level seasons up to zero. I then drew a smooth logarithmic curve over this data based on a mix of the average WAR and the median WAR historically associated with each draft slot. Overall, we calculate that ~55 players in each draft class5 produce enough expected wins to be worth a guaranteed contract, which is a close empirical match for how NBA teams behave in reality.6

Under our system, there are 1860 ARC distributed to teams in any given season. (As we’ll get to later, teams can have their ARC allocation penalized under some circumstances, but any “taxes” the league collects are redistributed into the system.) If we divide ⏜1860 by the expected value above replacement level associated with each draft slot, we come up with the following:

As you can see, draft value becomes nonlinear toward the top: the #1 overall pick is worth about twice as much as the #7 pick. Having looked at a lot of other “draft charts”, I’m comfortable that’s somewhere in the right ballpark.7 But value tends to flatten out once you get out of the lottery. The first half of the second round can produce tangible value — sometimes even a lot of value (Nikola Jokic was drafted at #41). But the back half of the second round literally zeroes out; teams are usually indifferent toward keeping the players they select at all.

The chart does hint at one important feature of the system: there’s a ⏜100 cap on how much a team can bid on any given player. If several teams make the same bid, then the league goes back to the ping-pong balls to determine who gets him. So there is still some element of luck: typically, in any given draft, the top three picks will be worth more than the max 100 ARC allocation. You can’t just stockpile ARC and guarantee yourself a Wemby or a Cooper Flagg, in other words.

We’ll return to some of these details later, but first let me run one more comparison between ARC and the existing rules. This chart shows the expected value of each draft slot under the current system based on the lottery odds for first round picks and compares it to each slot’s ARC allocation under our new model. Importantly, the expected value estimates also include second-round picks, which provide a sneaky benefit to the worst teams since the first half of the second round is worth a lot more than the back end. (Under ARC, there’s no more second round because there’s no more draft; teams just keep bidding on players until certain criteria are exhausted.)

So yes, we’re taking a little bit from the poor and giving it to the rest of the league. Finishing in one of the bottom eight positions in the standings is now worth slightly less, especially in the bottom three. But it isn’t that big a shift, and the largest benefits convey to the league’s middle class.

Plus, even if we’re giving them less capital, we’re probably reducing variance for the worst teams. A typical draft features three players who are worth a ⏜100 max bid, and three teams each season who are guaranteed to receive 100 ARC. Surely, some other teams will acquire the capital for a ⏜100 bid via trade (or because they have some ARC left over from previous seasons; there’s a mechanism for that). But generally, teams will have more control over their own destiny. If there’s a player they really like — say, Steph Curry in 2009 — they can grab him, provided he’s not a max player. There’s also a rule (#10) that prevents the same team from repeatedly winning “mix bid” auctions over a multi-year period.

Share

The auction process

3. During the auction, teams take turns nominating players in inverse order of finish. They must bid at least ⏜1 on any player they nominate. A team may pass on its nomination and still bid on players nominated by other teams, but they can’t nominate players once their turn comes up again in the rotation once they pass.

4. When a player is nominated, all teams have 7 minutes to submit simultaneous bids (reduced to 5 minutes on the second day of the auction8). All bids are then revealed, and the player is awarded to the highest bidder.

5. The maximum bid is ⏜100.

6. If 2 or more teams tie for the highest bid, the winning team is chosen by lottery among the tied bidders.

7. Teams may acquire a maximum of 4 players through ARC in any given season.

8. ARC budgets and player trades are frozen 72 hours before the auction. A team cannot select a player and immediately trade him, though in line with the NBA’s current rules, he can be traded 30 days after he signs a contract.

9. The draft ends when all remaining teams have passed on their nominations, have acquired 4 players, or have exhausted their ARC budget. There is no more “second round” under ARC, although teams may sign rookies who aren’t selected through the system as free agents.

The most important rules are #5 and #6, but they’re the ones we’ve already covered. By design, the top three picks are usually worth more than the maximum bid of ⏜100; the average #1 overall pick has a value of about ⏜140, for instance. So in most halfway decent draft classes — maybe not the Zaccharie Risacher draft — there will be multiple max bids for the first few players. Teams will face challenging decisions as to whether to acquire enough ARC to make a max bid; what’s interesting is that for every additional team that plans to bid the max, the odds of winding up with the player decline.9

I’m less hung up on some of these other rules. There is probably some slight tactical advantage to having the right to nominate a player, but it likely isn’t much.10 We might see some unconventional strategies, too, like a team nominating the equivalent of a second-round pick and hoping to get him for a few ARC while holding their fire for a bigger bid down the line; there’s nothing that says the best players have to be nominated first.11 The action would be frenetic on auction night, with new information revealed at every turn. So frenetic, in fact, that as per Rule #8, I think it’s just too chaotic to allow teams to acquire ARC on draft night. Instead, front offices would lock in their budgets and have three days to make their best-laid plans.

Multi-year planning

10. A team may win a player with a maximum ⏜100 bid at most 2 times in any 3-year window and 3 times in any 5-year window.12

11. Teams must spend at least ⏜25 in any 3-year window. It’s use it, or lose it. If a team doesn’t meet this requirement, any outstanding ARC is deducted from its account.13

12. Teams can go into ARC debt, provided they’re guaranteed to meet the ⏜25 spend per three years requirement under Rule #11.

13. Unused ARC rolls over to the next season, but with an escalating tax schedule. Teams pay a 10 percent tax rate on the first ⏜10 of ARC savings, a 20 percent tax rate on the next ⏜10 and so forth.14

14. All ARC subtracted via fines or taxes will be reallocated evenly to other teams.15 Thus, the total amount of ARC in the system is preserved.

The rollover provision under Rule #13 is intended more for “spare change” than for teams to stash large amounts of ARC. Joseph’s work for PRISM found that there’s meaningful and somewhat predictable variation in the strength of different draft classes. Still, the taxes get punitive if you try to retain more than about ⏜20 or ⏜30. And even if you can’t find a good fit for your ARC in the auction, you can always trade it.

Rules #11 and #12 are probably the more important constraints. Under Rule #1, each team is guaranteed to pick up at least ⏜25 every season. Under Rule #11, they can borrow against this and go into ARC debt, but they’re still required under Rule #11 to have enough left over to spend at least ⏜25 in any given three-year span.

On the surface, this is more flexible than the current Stepien Rule, which requires teams to make a first-round pick every other season. Under Rules #11 and #12, a team has to make the equivalent of a late-first-round pick only once every three seasons instead.16 However, under ARC, a team won’t really be able to trade draft capital more than two years in advance17, whereas draft picks can be traded up to seven years in advance under current rules. And it’s the picks in the out years that tend to be most valuable, since acquirers hope that the contending club will play through its competitive cycle and crash out to the lottery by the end of the window.

This is a big change. A lot of NBA tanking discourse takes what you might call a “demand-side approach”, i.e. by focusing on the fact that teams have a lot of desire to acquire high future picks to the point where they frequently tank. What can be neglected is the supply side: there’s a lot of opportunity to acquire such picks because teams can trade draft capital seven years out and they often take an incredibly short-term focus. Reducing the potential for long-term, franchise-compromising moves would protect contending teams (like the Phoenix Suns when they acquired Kevin Durant) from themselves. But it would also decrease the amount of draft capital available to rebuilding teams from trading their own star players. With less draft capital available, they might have to “settle” for acquiring player talent in return, which would keep them more competitive. Or they might be more inclined to keep their own players since there won’t be as many too-good-to-refuse offers.18

Rule #10, meanwhile, is intended to slightly reduce the amount of luck under our system. A team can acquire a ⏜100 player — basically, a guy who is a legitimate top 3 pick — at most two times in any three seasons or three times in any five seasons. If you’re going to spread the wealth around to losing teams, you might as well spread it a bit more evenly.19

Anti-tanking measures

15. If a team has missed the playoffs and play-in game for more than 3 consecutive years, it’s subject to a 20 percent tax on its annual ARC allocation.

16. The league will deduct ARC from teams for tanking or failing to uphold the competitive integrity of the league. If the league suspects a high likelihood of tanking, it will publicly issue a team a warning that it’s been placed under heightened scrutiny. The maximum allowable fine without a warning is ⏜5. Once a team is under heightened scrutiny, the maximum allowable fine is ⏜20 for first-time offenders and unlimited for repeat offenses, with the clock resetting after seven seasons.

Two-thirds of the 30 NBA teams make either the playoffs or the play-in tournament every season. It just isn’t that hard to do. At the end of the 2025-26 season, the only ongoing three year play-in/playoff droughts will belong to the Brooklyn Nets, Utah Jazz and Washington Wizards.

So I have no trouble punishing these teams under Rule #15. More than three years in the wilderness is when season-ticket holders start to give up hope. Would Jaren Jackson Jr., Trae Young or Anthony Davis have been shut down if the Jazz or Wizards could reset their clocks by sneaking into the play-in game? Maybe not. Under this rule, a team with a 3-year drought would actually acquire more ARC if it made the play-in than if it finished with the 5th-worst record.

Rule #16 is likely to be more controversial because it’s more subjective. But the league makes subjective judgments all the time, such as when disciplining players. It’s also fined teams like the Jazz and the Mavericks before for tanking, although the amount of the fines is trivial for billionaire owners. Rule #16 would set up a two-tiered system so teams weren’t totally blindsided; taking away a few ARC and putting them under heightened scrutiny (so for instance, they’d need to see the receipts for purported injuries) would be the warning shot. Repeated, blatant tanking once teams were on probation would be punished more harshly.

Every half-sentient NBA fan (and absolutely every person betting on the games) knows exactly which teams are tanking; it can’t be that hard for the league to police this explicitly, whether the determinations are made through a Potter Stewart standard (“I know it when I see it”) or some sort of algorithm. All you really want is a deterrent: the expected value of making dubious, tank-tastic moves will be lessened if you might face real consequences for doing so.20

Player contracts

17. All players acquired through the ARC system must be offered guaranteed contracts, with salaries tied formulaically to the winning bid and the number of maximum bids.21

18. Teams retain NBA rights to players for 4 seasons once they’re chosen through the ARC process. The ARC spent on these players is deducted from a team’s account when the player is selected, whether or not the player ultimately signs a contract. Players may decline the initial contract and negotiate a higher salary after one year, subject to certain constraints.22 But they are not eligible to sign with teams who don’t have their ARC rights until the 4-year window expires. The rights to such players may be traded, however.

We’re getting into some of the less important mechanics, so I’ll be quicker here. A player selected during the auction must be offered a guaranteed contract. Currently, first-round picks get guaranteed contracts and second-round picks don’t, but about 20 of the 30 second-round picks wind up getting full-fledged NBA deals anyway. That implies that about 50 players will typically be selected in any given ARC auction. Teams can sign unselected players later, just like they can offer contracts to undrafted free agents now.

However, under current rules, teams sometimes do something else with their late picks: choose a “draft-and-stash” international player in the hopes of bringing him into the NBA after he’s picked up a couple more years of seasoning abroad. Rule #18 is intended as a replacement for that process, while also giving international players some leverage to negotiate for a higher rookie salary if their development goes well. It also plays a more subtle role in creating another sort of “asset class” that teams can move. Since there are no more second-round picks under ARC — essentially, the spare pennies under the current system — teams could trade the rights to draft-and-stash players if they want to “round up” their capital to have the right goods to make a trade.

Transition to the new system

19. Before the start of the first season under the ARC process, each team gets an additional ⏜10 above and beyond their standings-based allocation under Rule #1.

20. Expansion teams start with 100 ARC.

21. Teams may trade ARC in advance of the full transition to ARC provided they meet Rules #10 and #11. Alternatively, by mutual agreement with the Players’ Association, the league may seek to accelerate the transition to ARC by “cashing out” existing draft-pick trade obligations to their equivalent ARC values.

Rule #19 creates a bit more liquidity in the system, but I don’t think you need or want to have too much liquidity.23 Teams can already go into ARC debt, and there’s already going to be a lot of demand for winning teams to move their ⏜25 or ⏜30 allocation to losing teams so they’ll have the capital to make a max bid.

Expansion to Las Vegas and Seattle is probable but not quite inevitable. If you’re going to ask for $8 billion (!) for a franchise in mid-tier markets, I say go ahead and give them a full 100 ARC for their first auction.

And yes, one big critique you might make of ARC is that it’s hard to have a seamless transition when teams have already traded draft picks as far out as 2032 (!). But the current system isn’t working. The alternatives proposed by the league are uninspired and might create as many problems as they solve. It’s time to rethink things from the ground up.

Most draft models try to predict how good each player will be — some number representing future WAR, or a tier classification, or an over/under on career minutes. PRISM doesn’t quite do that. Instead, it asks a different question based on pairwise comparisons: given two prospects, which one will have the better NBA career?

This design choice matters for a few reasons. Gradient boosting regression to a target like WAR has a few limitations, one of them being that the projections tend to compress towards the training set. While pairwise rankings don’t necessarily change accuracy, they preserve the structure of a gradient boosting model while allowing us more interpretability into prospect volatility, as we’ll demonstrate later. They can also reward exceptional prospects that outperform the training set.

More technically, PRISM is a machine learning model: a CatBoost gradient boosted tree classifier. Each training example is the difference between two prospects’ feature vectors. Every prospect in a draft pool is compared head-to-head against every other, producing an N×N matrix of win probabilities. The PRISM score is a player’s average win rate across all of those matchups.

Stage 1: Role prediction

Basketball evaluation is inherently position-dependent. A high post-up rate is expected for a big but unusual for a guard. A 40 percent three-point clip means something different for a point guard than for a center. Before the main ranking model can evaluate prospects, it needs to know what role each one is likely to play.

PRISM’s role predictions assign each prospect a probability distribution across three offensive archetypes (creator, spacer, big) and three defensive archetypes (perimeter, help, anchor). A prospect with 45 percent creator, 35 percent spacer, and 20 percent big is meaningfully different from one at 90/5/5 — the first is a “tweener”, the second fits the archetype. Either way, the classification is potentially useful.

These role probabilities get used in various ways by the model. They enter the ranking model directly as features. They define positional baselines, so that a prospect’s playstyle can be evaluated relative to what’s normal for their predicted role. And they drive a “tweener score”, which measures how cleanly a player maps to a single archetype.

Playstyle versatility is often an indicator of tweener status — prospects who spread their possessions across isolation, spot-up, transition, and self-creation without a dominant mode tend to have less concentration in any one archetype. But tweener-ness isn’t about current production: tweeners and clear-role prospects have nearly identical impact metrics on average in college. Whether that ambiguity helps or hurts depends on the rest of a player’s profile. Among on-ball creators and bigs, clear roles predict stronger NBA development. The league still needs point guards who run offenses and centers who anchor defenses. But among spacers, tweeners can actually develop better — the modern NBA rewards shooters who can do a little of everything without being locked into one mode.

Stage 2: Preseason score

PRISM evaluates each prospect’s current-season college statistics. But for returning players — sophomores, juniors, seniors — there’s an additional signal available: how they performed last year. A player who was already producing at a high level as a sophomore and then improves as a junior is a fundamentally different bet than a junior who’s having a breakout season after two mediocre years.

Stage 3: Pairwise ranking

PRISM then takes the outputs of Stages 1 and 2 alongside dozens of features covering box score production, advanced impact metrics, physical measurements, shot creation data, playtype frequencies, and strength of schedule, and uses them to produce head-to-head comparisons for every pair in the draft pool.

Our training data spans the 2010 through 2021 draft classes, covering the BartTorvik era where comprehensive play-by-play college statistics became available. Rather than detailing every feature, it’s more useful to describe the assumptions behind them.

Bayesian padding

College statistics are built on small samples. A prospect who shoots 15-for-40 from three has a 37.5 percent clip, but that’s 40 attempts — roughly the same number an NBA player takes in two weeks. Raw percentages at that volume are dominated by noise. PRISM addresses this through Bayesian padding, which shrinks each player’s shooting and rate statistics toward a prior expectation based on two levels of information. Every player is pulled toward a weighted blend of role-group averages — a prospect who is 60 percent creator and 40 percent spacer gets a blend of both groups' shooting expectations.

For returning players, this role-based prior is further refined by their own prior-season stats: a sophomore who shot 30 percent on 200 attempts last year will have a prior anchored mostly to his own history, while one with only 20 prior attempts will stay closer to the role group. Freshmen, with no individual history, get the role mean directly. How much a player is pulled toward the prior depends on sample size: a player with 200 three-point attempts barely moves, while a player with 30 attempts moves substantially. This padding is applied to our box score and play-by-play stats. Importantly, it happens before any composite features are computed, so downstream metrics like expected points per 100 possessions from each shooting zone are built on stabilized inputs rather than raw noise.

Teammate context and player development

Team context affects development trajectory. PRISM incorporates two features designed to separate genuine talent from circumstantial production. The first is BPM share, which measures a player’s individual BPM relative to his team’s minute-weighted average. A player with a BPM share well above 1.0 is producing far above his teammates — he’s the engine of his team’s performance, and his stats are less likely to be inflated by surrounding talent. A player near or below 1.0 is producing in line with or below his teammates, which doesn’t mean he’s bad, but it means the model should weigh his raw numbers with more skepticism.

The second is BPM trajectory, which tracks whether he’s been improving over the course of his college seasons. The raw slope is then shrunk toward the expected development rate for his class year (sophomore, junior, senior). The reasoning behind this is simple: sophomores are expected to improve more than seniors, so a sophomore’s modest improvement is treated as less noteworthy than the same jump from a senior. Freshmen receive no trajectory value since there’s no prior season to measure against. The feature captures something intuitive: a junior whose BPM has climbed steadily from 2.0 to 5.0 to 8.0 across three seasons is a fundamentally different prospect than one who jumped from 3.0 to 8.0 in a single year, even if their current-season numbers are identical. The first pattern suggests sustained development; the second might be a statistical outlier.

Age does the heavy lifting

Age, along with advanced impact metrics, accounts for a lot of the differentiation between prospects. In combination, these features separate players who are far apart in quality and development. This is by design: these metrics have proven to be predictive of future impact metrics. Age is particularly important as a lever. Players who are younger are assumed to have more physical and skill development left, which would allow them to eclipse their older classmates. But this also depends on how offensively slanted a prospect is. Older offensive prospects are assumed to be closer to their offensive peak than younger players in their class, while that same premium doesn’t necessarily exist on defense. Even older defensive centers, for example, are considered to be valuable prospects, which makes sense, as defensive development tends to be less steep than offensive development early in players’ NBA careers.

Using EPM development curves, we can see that the steepest development actually happens at age 19. Obviously, this carries inherent bias (19 year olds in the NBA are drafted because they can develop that well, and therefore receive more opportunity), but consider that seniors are still behind the curve.

You’ll notice that teams often swing for very high-upside players — this is natural when you consider the NBA’s salary structure, which punishes high-commitment contracts for non-elite production. The difference in development between players who peak at +4.0 EPM and those who peak at +2.0 EPM widens over time, and elite players stay above replacement level far longer, facing a less steep decline than merely “good” players.

There’s an interesting thought experiment that might divide many in the draft community. Say Player A has a 100 percent chance of becoming a “good” player — peaking around +1.85 EPM by year 5. Player B has a 25 percent chance of becoming “elite” and a 75 percent chance of landing at “above average.” Who would you rather have?

On pure expected value, Player A looks better: +1.85 peak EPM versus an expected +0.96 for Player B. But there’s an additional consideration in a league that caps salaries at a certain level. Instead, teams are most likely considering expected surplus.

Both players are cheap for years 1 through 4 on the rookie scale. The decision point comes at year 5, when the max extension kicks in. Player A is good enough to demand a max in restricted free agency but never quite good enough to justify it. This is the Domantas Sabonis contract, the Ja Morant contract — players in the gap between +2.0 and +4.0 who get paid like stars and produce more like solid starters.

Player B profiles differently. In the 25 percent scenario where he becomes elite, you have a franchise cornerstone producing +4.7 EPM on a max deal — massive surplus, and the kind of player championships are built around. But if he’s "merely” above-average, you simply don’t (or at least you shouldn’t) extend him at the max. He’ll walk into free agency or re-sign at a discount, and you’ll lose nothing beyond the draft pick and rookie-scale investment.

This is exactly why teams often optimize for different outcomes in the NBA — combined with aging curves, it’s unlikely that older, creation-driven prospects will warrant the type of investment in the NBA that gives them positive EV on max contracts. Hence, those players tend to fall in the draft.

Of course, this makes selecting the right players at the top even more important. An important but sometimes overlooked part of development curves is how they begin — future “elite” players post +0.01 EPM as rookies, essentially neutral, while the next tier down is already at -0.93. The gap between tiers is visible from year one, even though few rookies are actually good in an absolute sense. The implication isn’t that teams should draft for immediate production — it’s that the signal is there early if you know where to look. PRISM is designed to find it: by anchoring on current college production adjusted for age and context, it can separate which young prospects are tracking toward elite trajectories versus which are merely projectable. The difference between those two outcomes, as the aging curves show, is the difference between a franchise-altering max contract and a roster-clogging one.

Custom features decide close calls

Where we hope PRISM’s edge comes from is a set of custom-engineered features that differentiate similarly-ranked prospects. In head-to-head matchups between players at similar impact levels, secondary features often decide the outcome.

One of the more predictive features, steals, has long been known as a cognition metric, suggesting an understanding of positioning, which becomes valuable for both creator archetypes and big men. That intuition is confirmed when analyzing EPM production: positive relative STL/100 players have steeper developmental increases as players continue deeper into their careers.

Position-adjusted playstyles measure how a player’s shot diet and play-type distribution compare to others in their predicted roles. A big who shoots more than the average big, or a guard who posts up more than the average guard, represents a meaningful deviation from positional norms. These deviations help the model identify stylistic outliers whose production might translate differently than their archetype would suggest.

Self-creation metrics from play-by-play measure how much of a player’s scoring comes off their own creation versus assisted looks. This is one of the clearest distinctions between scalable and role-dependent production. A player who makes 3 unassisted threes per 100 possessions is demonstrating a skill that is valued in the NBA; a player whose threes are almost entirely assisted is more context-dependent.

Still, perhaps too much emphasis is being put on self-creators in draft outcomes. The data bears this out — high self-creation in college doesn't particularly predict stronger NBA development.

Physical measurements are amplifiers, not drivers

Height, wingspan, length (wingspan minus height), and BMI are included in the model, but they function more as amplifiers on top of existing production rather than as standalone predictors. Fifteen years of data tells us that combine measurements and athletic testing are noisy as standalone signals. If you want an athletic guard because athletic guards get to the rim, a guard who already gets to the rim is a good bet, regardless of how he tests at the combine.

Position-adjusted physicals — how tall or long a player is relative to their predicted role — carry more weight than raw measurements. Being 6’9” matters differently depending on whether you’re projected as a guard or a big.

Wingspan is an interesting feature, particularly because its importance differs by position. For perimeter defenders, length (the difference between wingspan and height) matters more. For anchors, pure wingspan matters more. For helpers, neither has particular importance — instead, those players indicate value as defenders with activity, by generating stops, steals and blocks, and having high defensive impact metrics in college. Rebounding also generates positive outcomes and shouldn’t be ignored as a part of defense.

Rolling windows and temporal integrity

Prospects are scored within three-year rolling pools. The 2026 class is pooled with 2024 and 2025; the 2025 class with 2023 and 2024; and so on. This keeps PRISM scores interpretable across years by providing a consistent reference frame. The pooling approach also allows us to get around era effects. Since players aren’t going to be compared against players ten years in the past or future, we aren’t penalizing prospects from previous classes for less “modern” playstyles.

Training pair construction

Not all training comparisons are equally informative. PRISM distinguishes three types of pairs:

Survivor–Survivor: Both players earned meaningful NBA minutes. These are the most valuable comparisons and are included without modification.

Survivor–Bust: One player stuck, the other didn’t. These are downweighted. The reasoning: separating NBA players from non-NBA players is relatively easy and not the model’s primary job. We want capacity allocated to the harder problem of distinguishing between viable prospects.

Bust–Bust: Both players washed out. These comparisons are excluded entirely. The ordering between two players who never stuck in the league — did a guy provide below-replacement-level production for one season or two? — is noisy at best.

Why wins above replacement?

For our player comparisons, we use eWINS — wins created above replacement level according to EPM, setting negative seasons to zero — rather than a player’s rate of production as measured by EPM. This is among the more “controversial” design choices of the model, but we think it’s the right one. eWINS account for playing time and volume, without punishing players for below-replacement-level performance if they’re still learning on the job. The model’s primary job is ranking prospects against each other, and most of the separation between draft outcomes isn’t between good NBA players and slightly better NBA players. It’s between players who make it and players who don’t. Roughly half of all drafted prospects never establish a meaningful NBA career.

EPM can’t cleanly represent this. A player who never reaches the NBA has no EPM. You’re forced to either exclude him from training entirely or invent a penalty value and hope it lands in the right place. But you can calculate his eWINS: 0.

More specifically, we sum eWINS across a player’s first seven NBA seasons, with heavier weight on the later windows where minutes are earned rather than granted by draft hype. This means the target rewards sustained production, not just a single outstanding year. A player who is a solid starter for six seasons accumulates more eWINS than one who has two brilliant years and washes out — and that distinction reflects real draft value.

Scouting-based rankings serve as PRISM’s prior

PRISM has no access to combine athletic testing (vertical leap, agility, sprint times), interview or character intelligence, medical evaluations, or coaching assessments. This is partly a data limitation and partly by design. The model is scoped to statistical production and measurable context instead.

But to compensate for this missing information, we incorporate a prior based on consensus scouting rankings, and this is rated more highly toward the start of the season when statistical metrics of production aren’t reliable either. In early November, a prospect may have played three games. His statistics are essentially noise. But consensus draft boards — aggregated from scouts, media, and market signals — contain real information about a player’s talent level, even if imperfect. As games accumulate, the model’s evaluation gradually takes over, and by roughly early February, the model’s statistical measures dominate.

The mapping from consensus rank to expected PRISM score is not linear. The gap in expected outcome between the consensus #1 and #5 prospect is far larger than the gap between #30 and #35 — the tails are steep. We calibrate this mapping empirically using a non-parametric method that captures the shape of the tails.

We also adjust the prior to account for variation in class quality. The consensus #10 prospect in a historically loaded class represents a meaningfully different talent level than the #10 prospect in a shallow class. If we applied the same anchor to both, we would systematically undervalue prospects in strong classes and overvalue those in weak ones. To address this, we compute draft strength scalars for each class using the pairwise matrices, which compare every prospect against every other prospect in the same rolling pool. The scalar measures how often prospects from a given class beat prospects from adjacent classes in the model’s head-to-head evaluations. Lottery-range and late-class prospects receive separate scalars, since a class can be deep at the top without being strong overall, or vice versa.

PRISM vs. consensus

PRISM isn’t afraid to take big swings. In backtesting, among players who earned NBA minutes, PRISM has a significant edge over consensus rankings in pairwise accuracy. Consensus rankings, which correlate strongly with draft capital and therefore opportunity, are better at “finding” players that stick in the league. But among the players who do stick, consensus is barely better than a coin flip at telling you who will be better.

PRISM’s biggest raises have surfaced real value — typically productive college players whose translatable skills get discounted because they lack the pedigree or physical tools that scouts fixate on. Multiple players that ranked significantly above their consensus position became quality NBA contributors with positive impact metrics.

Volatility scores, trajectories and draft simulator

Using our pairwise probabilities, we can assess how a player's feature profile translates against different tiers of competition. A player's volatility score — their average win probability against the top K prospects vs against the bottom K prospects — captures how consistently they separate from the better players in their draft class. A prospect whose ranking is driven primarily by dominating lower-tier prospects, rather than competing with the top of the pool, may have a lower ceiling but a higher floor.

Additionally, we measure which players peak earlier or later. These features, along with PRISM scores and role projections, give us a good starting point for something fun: a draft simulator. Using roster composition, player labels (franchise player, core, expendable, rotation), and team designations (blank slate, rebuilding with assets, rising, contending, aging contender), we can develop a better sense of which players a team would logically target. Generally, rebuilding teams will draft the best player available, while teams on a contending timeline would prefer prospects who would make an immediate impact and fit within their current roster scheme.

PRISM can’t account for everything

PRISM is best treated as a signal, not a verdict. The model has no access to medical or injury information, interview intelligence, or coaching assessments.

Draft projection is inherently imperfect. NBA decisions and outcomes reflect more than college production alone, even if college production is the strongest indicator.

Last updated 3/15/2026. We’ve added information on the women’s version of COOPER, as well as how our NCAA tournament foreacst work. As we’ve been working on these, we’ve also made a few minor tweaks to the parameters of the men’s version. Although the differences are hard to notice, the text below reflects the current values for all parameters.

What’s new in COOPER

COOPER — named in honor of Naismith Award winner Cooper Flagg and two-time NCAA champion Cynthia Cooper — is Silver Bulletin’s new college basketball rating system.1 As part of a long Nate/Silver Bulletin tradition, it is a goofy backronym: Collegiate Outcomes with Opponents, Pace and Expert Ratings. The name hints at the system’s essential features:

• The first “O” is for “outcomes”. COOPER is mostly based on margin of victory, but simply winning games matters, too.

• The second “O” stands for “opponents”. The model adjusts for the strength of opponents through an Elo-like system, which is essential when rating college teams with their wide disparity in quality. Furthermore, games between more evenly-matched teams are weighted more heavily in the system.

• The “P” for “pace”. We now account for how high-scoring a team’s games tend to be and derive both offensive and defensive ratings for each school. Higher-scoring games tend to introduce more variability.

• Finally, “ER” for “expert ratings” means we revise each team’s rating at the start of the season using human opinions in the form of the preseason AP and Coaches’ polls. A team’s initial rating to start the season is based on a combination of these polls, its year-end rating from the previous season, and the strength of its conference.

COOPER represents an evolution of the SBCB ratings that we used in 2025, and COOPER and SBCB share some of the same programming, but we’ve dug pretty deep under the hood and there are some important changes:

• The most noticeable one is that we now calculate separate offensive and defensive ratings for each school, which we call PPPG (projected points per game) and PPAG (projected points allowed per game). Essentially, these ratings represent how many points we’d expect a team to score against an average NCAA opponent. For instance, a team with a PPPG of 81 and a PPAG of 74 would be expected to win 81–74. By subtracting PPAG from PPPG, we can also derive a net rating for each team: in this case, it would be +7.

• But net rating doesn’t tell the whole story because teams that play to higher scores (both scoring and allowing more points) tend to have higher variance. Instead, a team’s Elo rating accounts for this property and is its best representation of its likelihood of victory against an average opponent.2

• Although it’s less visible, perhaps the most important change is that we’ve removed the constraint from SBCB that required a team to always gain in the ratings when it won and always lose points when it lost. For instance, even if Duke was a 35-point favorite against Cal St. Bakersfield and won the game by 1 point at the buzzer, our previous system would have viewed this as a positive for Duke. But that’s pretty clearly an unreasonable assumption from a Bayesian standpoint — you wouldn’t think more highly of Duke after this game — and this was causing a fair amount of information loss.3 So this is no longer the case in COOPER. Teams are awarded a bonus for winning games, regardless of the final margin.4 But they can now lose ground if they significantly underachieve COOPER’s expectations even after a win — or gain ground following a loss where the final score is impressive relative to the model’s expectations.

• COOPER tends to show a wider spread between the best and worst teams than SBCB did. This is partly because there’s now slightly more carryover in the ratings from season to season. Previously, our view was that the increasing likelihood of star players defecting to the NBA after just a season or two was diminishing the advantage for blue-blood teams. However, NIL is helping the most elite programs remain as dominant as ever. Also, as described above, COOPER tends to put more emphasis on margin of victory as compared with SBCB and doesn’t punish teams for runaway margins in the same way. This is a bit less “politically correct” in the sense that it potentially gives teams more credit for beating up on weaker opponents. But it increases the fidelity in distinguishing good teams from great teams. Predictive accuracy is what we’re after here.

• However, each game now has what we call an “impact factor” that reflects how reliable a signal it provides. Basically, games that are projected to be lopsided5 matter less in COOPER than those that the model expects to be close. Conference games and especially NCAA tournament games also have higher impact ratings. Teams tend to compete at full effort in these games, and the results are more reliable than for early-season, non-conference matchups.

• Although COOPER continues to account for travel distance — an East Coast team playing in California will often have a rough time — we’ve found that these effects are diminishing over time as travel accommodations improve to a larger extent than SBCB was accounting for. However, we’ve retained one of my favorite SBCB features, which are customized home court advantage ratings for each school.

• SBCB calculated both a “Bayesian” version of the ratings that adjusted each team’s rating every offseason based on preseason polls, and a “pure” version that applied purely objective data. We’re continuing to do this, but the “pure” version now receives less emphasis. The poll-adjusted version should be considered the official or flagship6 version of COOPER.

More about how COOPER works

Just so I don’t get accused of self-plagiarism, note that some of this text is copied from the SBCB methodology page.

• COOPER is a profoundly Bayesian model in the sense that ratings are adjusted on an iterative basis as new information becomes available: namely polls at the start of the season, and then game results throughout the season. We don’t go back and revise past COOPER ratings based on information that wasn't available at the time the game was played.

• Rating changes after each game reflect a combination of:

  • The margin of victory or defeat as compared with COOPER’s prior expectations of the “point spread”. Unlike in previous versions of SBCB/Elo, there are no diminishing returns to higher scoring margins. In practice, both NBA and college basketball teams tend to lay off the gas pedal once they already have a big lead, so the differences in the final score can actually understate the intrinsic gap in team quality. A linear formula works perfectly well for predictive purposes.

  • But we also account for whether a team wins or loses. Winning a game by any margin is essentially equivalent to 6 points of scoring margin. However, this “bonus” is compared against COOPER’s prior for each team’s probability of victory. Therefore, it has little effect if a team was heavily favored because the expectation of a win was already baked into the system. Wins in closely contested matchups — or upsets in games with clear favorites — matter far more.

• Home court advantage is factored in. In fact, we calculate a separate home court rating for each team, based on how much it underperforms or exceeds its COOPER projection in home games. Generally speaking, teams that are reputed to have larger home court advantages based on difficult playing conditions or more enthusiastic fan bases actually do. Teams that play at high altitudes often have especially large home court advantages in basketball, as in other sports. These home court ratings move very slowly, taking advantage of data from previous years. (They fully carry over from season to season.) However, having a larger home court advantage isn’t helpful in the NCAA tournament, which is entirely played at neutral sites. Teams like Purdue, whose home court is worth an additional ~2 points of victory margin compared with the NCAA D1 average, may be overrated by other systems that don’t account for this factor.

• Travel distance also matters and, for the 2025-26 season, is equal to 5 * m^(⅓) worth of Elo ratings points, where m is the distance in miles from the visiting team’s campus. For home games, be sure to add the travel distance factor to the team-specific home court rating to calculate a team’s overall advantage. But note this is a considerably smaller advantage from travel than SBCB had assumed. We found that SBCB had been overrating home teams in recent years in games where the opponents traveled a long way to play. On the other hand, the effect of travel distance was much larger up through the 1980s. This almost certainly reflects improving travel accommodations and sports medicine and the general professionalization of college sports.

• COOPER ratings, like most other Elo systems applied to sports, partly carry over from season to season, with a discount factor applied that reverts the ratings toward the mean in between seasons. To be absurdly specific, a team’s rating is reverted by 30 percent toward the mean at the start of each new season. This is actually less mean-reversion than had been incorporated into SBCB. While it’s true that elite college talent tends to go pro sooner, top-tier programs like Duke or Kansas have plenty of other ways to perpetuate their success by investing more in their programs or through superior recruiting. While the introduction of NIL several years ago was “disruptive” to some degree — for instance, in boosting the basketball profile of the SEC — recent tournament and regular-season results suggest a recalibration toward a new steady state.

• More precisely, teams in COOPER are reverted toward the mean of other teams in their conference — not toward the global average (with the exception of the few remaining independent teams). When a team changes conferences, the rating change is based on its new conference rather than its old one, as this can indicate where a program fits into the college basketball pecking order. Interconference play, especially in recent NCAA tournaments, is self-evidently important for this purpose. In essence, a team that exceeds expectations in the NCAA Tournament will then redistribute those gains toward the rest of its conference in COOPER’s off-season recalibration process. The default/Bayesian version of COOPER also accounts for preseason polls in its initial ratings to start the season, while the “pure” version does not.

• However, this introduces various complications because the polls only provide a truncated list: that is, only 25 teams are ranked.7 The process for imputing human ratings quite literally applies Bayes’ Theorem in the sense that it relies on a prior about how likely a team is to be ranked. For instance, a team with a 2000 Elo rating would typically expect to be ranked somewhere in the top 25 in the next preseason poll — so if it isn’t ranked, that provides a lot of information that its performance is expected to decline, usually because of a loss of key talent. However, a team that ended the previous season with a mediocre rating would rarely expect to be ranked, so this tells us little. Teams ranked specifically #1 overall receive special treatment to ensure that truly dominant clubs like the late 60s/early 70s UCLA Bruins are not punished.²

• I haven’t yet mentioned what is perhaps the most important parameter in any Elo-derived system, which is called the k-factor. This governs how much the ratings update after each game. A higher k-factor implies more sensitivity to recent play but also more volatility. Statistically speaking, the goal is generally to minimize autocorrelation. That is, you want to avoid both a too-high k-factor where ratings zig-zag around (i.e. teams usually decline after gaining and vice versa) and a too-low one where a team with a recent ratings gain can predictably be expected to follow that up with further gains because the system is too slow to account for what soccer fans call a change in “form”. Specifically, we use a k-factor of 55; this number has no intrinsic meaning and is derived empirically. Generally speaking, COOPER ratings are more aggressive than other college basketball systems about accounting for recent play and tend to ride a winning hand while discounting ratings for teams that have been on a downward trajectory.8

• However, the k-factor is up to 2x higher (so, it goes up to 110) for early-season games, diminishing until a team plays roughly the 15th game of its season. The intuition behind this is that early-season games reveal a lot of information as compared to COOPER’s crude preseason estimates. By the middle of the season, conversely, teams mostly are “what they thought we were” and each subsequent game tells us less.

• Newly this year, each game receives an impact factor. Basically, games between teams that are closely matched in COOPER matter more. We’ve also found that what we call “high-stakes” games — namely, conference games and NCAA tournament games — tend to provide more signal, and these are weighted more heavily also. For NCAA tournament games specifically, there’s also a hard-coded boost to the impact rating. We’ve found that success early in the NCAA tournament — i.e. if a team blows out tough opponents in the first two rounds — tends to predict success for the rest of the tournament.

• For calculating margins of victory and net ratings, one point in a basketball game equals approximately 28.5 Elo points. Thus, a team with a 100-point Elo advantage, after accounting for home court and travel distance, would be roughly a 3 or 4-point favorite. However, newly for COOPER, this exchange rate varies slightly based on whether COOPER projects the game to be high-scoring or low-scoring.

• Also new this year, COOPER calculates a rolling “pace” factor for each team. This is a slight misnomer, because in basketball analytics, “pace” generally refers to the number of possessions in a game. For COOPER, because possession-by-possession data is unavailable until recent seasons, it instead reflects the overall number of points scored by both schools in games involving the team. In addition, we calculate a Bayesian expectation of the NCAA average points per game: 2025-26 has been a particularly high-scoring season, for instance. This leaguewide rating is designed to adjust more aggressively at the start of each season; changes in rules or style are often evident relatively quickly.

• The combination of a team’s net rating and its pace rating essentially allows us to back into an offensive and defensive rating for each school.9 Higher-scoring games tend to introduce more variability. Both empirically and theoretically, a marginal point matters more in an environment where points are scarce. For instance, an uptempo team that projects to beat an average opponent by a score of 90-80 will win less often than a downtempo team that projects to win 65-55. Conversely, teams with offense-oriented mindsets tend to pull off a few more upsets when they’re underdogs, such as by winning the battle for 3-point shooting.

• Teams that are new to Division I begin with a rating of 1300 at the start of their first D1 season, adjusted slightly upward or downward based on the strength of their new conference. That is to say, they are usually considerably below average since the average Elo rating is 1500. Preexisting D1 teams’ ratings are adjusted slightly upward such that the global average remains at 1500 when new teams join.

• Our database contains many games between Division I and Division II teams, especially in recent seasons. However, rather than calculating a rating for individual D2 teams, we instead lump all D2 teams together into a single “divtwo” rating. Essentially, this makes them the equivalent of the Washington Generals, barnstorming around and usually getting obliterated.10 A separate “divtwohome” running tally is calculated for D2 teams that host home games as opposed to paying on the road or at neutral sites — but this has become rare as D1 teams generally don’t want to decline an opportunity to sell tickets. Overall, however, D2 teams are patsies, with D1 teams winning upwards of 99 percent of the time at home in recent years against D2 opponents.

• COOPER and our NCAA tournament model estimate win probabilities for each game by forecasting a game score and estimating a standard error of its forecast. The standard error is higher in lopsided matchups and in “low stakes” games (i.e., nonconference matchups outside of the NCAA tournament). The statistical distribution we use is slightly “fat-tailed”, meaning that outlier scores occur somewhat more often than one might predict from a normal distribution, possibly indicating unusual circumstances (e.g., the entire road team gets food poisoning). (Added 3/14/2026).

Differences in women’s COOPER

The women’s version of COOPER essentially uses the same code as the men, but with a few parameters tweaked to reflect the nuances of the women’s game.

• There tends to be more dominance among women, i.e. the top teams win more often against average ones, and by larger margins. This mostly emerges organically from the rating-generation process, although there are a few small changes. Division II teams and new Division I teams start with lower default ratings than in the men’s version, and there is a considerably wider spread introduced in the Bayesian version of the ratings.

• There is less mean-reversion from season to season, probably because women are not eligible to join the WNBA until age 22, leading to greater team continuity. Thus, 80 percent of ratings carry over from season to season instead of 70 percent for the men.

• Home court advantage and travel distance effect tend to be slightly less in the women’s game. The top women’s programs draw crowds in line with men’s teams, but obscure schools may literally play in gyms in front of a few hundred fans. As for the men, home court advantage is calculated on a team-by-team basis. Note that Round of 64 and Round of 32 NCAA tournament games are often played on home court sites in the women’s tournament.

• The conversion rate between Elo points and game scores is slightly different for women. One point in a basketball game equals 25 Elo points for women, as compared 28.5 points for men. This likely reflects the fact that women’s games are typically more lopsided, so a team gets slightly less credit for running up the score. As for men, these are default values that are adjusted based on a team’s pace.

• Women’s ratings are based on the 2002-03 season onward rather than 1949-50 for the men. Data is also somewhat less complete: for instance, more games against D2 opponents are missing, and data on game locations for neutral-site games is much less comprehensive.

NCAA Tournament forecasts

After it spent many years living in an exceptionally complicated EXCEL spreadsheet that still contained some code I wrote when trying to win my office pool back in ~2002, I’ve finally transitioned our tournament forecasts into some actual code.

But our NCAA tournament forecasts aren’t really all that complicated. They account for differences in team strength, game location, and on the women’s side — since top seeds usually host games in the first two rounds — home court advantage. COOPER already calculates a probabilistic forecast for each game as part of its ratings, so basically, we’re just applying that process going forward to the most important 67 games of the season.

There are, however, three important differences to plain old COOPER. One is that the simulations run “hot”, meaning that we account for the effect on COOPER ratings from simulated games in earlier rounds. Conditional upon winning, a team’s rating is likely to improve, i.e. if a #12 seed defeats a #5 seed, the 12-seed is probably better than we originally expected, and that should be accounted for in forthcoming games. However, this effect is stronger for underdogs because they’re not expected to win, meaning that they require a larger Bayesian update. If and when a 12-seed reaches the Final Four, it was probably the case that they were underseeded to begin with. Thus, the late rounds of the tournament can actually be less chalky than the earlier ones.

The next difference is that our tournament forecasts account for injuries, whereas regular COOPER does not. The injury information is probabilistic; we have to make some judgment calls on translating sometimes subjective injury reports into probabilities that the player is sidelined for forthcoming rounds. For instance, if we list a player as having an 80 percent chance of playing, he’ll appear in the game 80 percent of the time and be sidelined the other 20 percent. Once a player is simulated as returning to his team’s lineup, he’ll remain with the team in that simulation for all subsequent rounds.11 (We don’t attempt to forecast new injuries.)

The model also accounts for significant players who missed time during the regular season but are now available. Thus, some teams will have an upward injury adjustment for having gotten healthier.

For what it’s worth, we also put a fair amount of time into double-checking the injury logic this year, including reevaluating the magnitude of the effect they had our projections, and I think our previous models were probably understating their impact. Top college players, like the top NBA players, can impact the final margin by as much as 7-10 points versus a replacement level player12

Historically, data on women’s injuries is sparser and we haven’t accouxnted for it, but we’re including whatever information on women’s injuries we can track down.

The injury adjustment accounts for the importance of the player, as measured by sports-reference.com Win Shares, adjusted for strength of schedule. We project a player’s replacement based on an historical analysis of players who played 5 MPG or less, adjusted for team strength. In other words, even a scrub on Duke who fills in for an injured player is likely to be pretty good, whereas one on Southeast Missouri State might not be. Thus, the most devastating injuries are when a middling team loses one of its few stars.

Finally, our tournament ratings combine COOPER with other leading systems, namely the Pomeroy ratings for men and Her Hoop Stats for women. In our tournament forecasts, COOPER gets 5/8ths of the weight and the other systems get 3/8ths.

Previously, we’d used a composite consisting of several other rating systems, so paring down to just Pomeroy and Her Hoop Stats reflects a simplification. However, there are several reasons why we prefer this pared-down approach:

• Having spent a lot of time looking at different ratings systems, I believe that these are the most rigorous ones (alongside COOPER). They also tend to be highly correlated with COOPER.

• Pomeroy and Her Hoop Stats calculate offensive and defensive ratings in addition to overall ratings, which makes them more directly comparable to COOPER because COOPER does that do. Most power ratings do not do this.

• Reducing the number of third-party systems reduces the workload on Selection Sunday and allows us to turn around the tournament forecasts more quickly, while reducing the probability for errors.

Pomeroy and Her Hoop Stats ratings are adjusted to give them the same mean and standard deviation as COOPER. They’re also adjusted for team pace. The overall ratings listed by Pomeroy and Her Hoop Stats are actually efficiency ratings, i.e. a measure of how many points a team is expected to outscore its opponents per 100 possessions. However, because some teams play at a faster pace than others, they’ll average a different number of expected possessions in a game. A team that is expected to outscore its opponents by 0.2 points per possession but averages 70 offensive possessions per game might be more efficient than one that averages +0.19 points per possession but plays at a pace of 80 possessions/game, but the latter team will actually outscore its opponents by a wider margin than average because it has more opportunities to press its advantage. This accounts for some of the difference between Duke and Michigan this year in different ratings systems; Duke is slightly more efficient on a per-possession basis according to most systems, but Michigan plays at a considerably faster pace.

Like in COOPER, the rating composite used in our model adjusts after every game using an Elo-like formula. In fact, the formula is identical to the COOPER formula, other than that our tournament version also accounts for injury status at the time of the game. An outcome that is out of line with the model’s expectations — a big underdog wins outright, or a 10-point favorite wins by 30 points — will cascade into a team’s rating for the rest of the tournament. Note also that tournament games have a larger impact factor in COOPER than regular-season games, so the early rounds of the tourney can potentially have a big impact.

We’ll update this document if we catch any bugs or make any further changes. Thank you for being a subscriber to Silver Bulletin and for your interest in COOPER!

Subscribe now

A year ago, after a trade so shocking that it literally made me wonder whether I was hallucinating1 sent Luka Doncic to Los Angeles, I ranked all 30 NBA teams by a deceptively simple criterion: their likelihood of winning future championships over the next 10 NBA seasons. The Future of the Franchise rankings have since turned into one of my very favorite Silver Bulletin features. In fact, we repeated the exercise in July, inviting two additional judges, Jeremias Engelmann and Joseph George, to the panel. (Joseph is Silver Bulletin’s Assistant Sports Analyst; Jeremias is the developer of Real Plus-Minus and a former analyst for the Mavericks and Suns whose excellent work can be found at xRAPM.com and the highly recommended 5x5.)

The timing is perfect for another round of FotF. The NBA will return to action tonight after an All-Star break that featured more discussion about tanking than basketball. To be honest, we’ll probably invite ourselves to the tanking party at some point. (There’s an anti-tanking take halfway done in the drafts folder.) But today, let’s talk some hoops.

For most of the league — there are roughly a half-dozen exceptions — we can find at least something to feel optimistic about. But let’s get this out of the way: we’re unapologetically ringmaxxing here. Consistent with NBA’s ring culture taken to its logical extreme, titles are literally all that matter for FotF, not mere pretty-goodness.

More specifically, we’re looking at the span from this year’s championship (2026) all the way through … 2035. I used the plural of “championship(s)” because multiple titles count more.2 Although there have been seven distinct title-holders in the past seven NBA seasons, that’s, historically speaking, an outlier. We’re looking at which teams have the ingredients to build a potential dynasty.

The talent on the roster matters, obviously — especially high-end talent. But so does draft equity, the cap sheet, managerial acumen, the allure of each market in attracting future talent in the player empowerment era — and the timelines for getting all of these things to perfectly align when the default chance of winning a championship is just 1 in 30. Indeed, I don’t think there’s any strictly algorithmic way to do this3, and there’s quite a bit more disagreement among the panel than during the summer, especially on teams like the Pacers and the Hawks. You’ll see a few consistent themes in these differences: I (Nate) tend to weigh franchise prestige more heavily than Jeremias or Joseph; Jeremias puts more of a premium on management, and Joseph is very high on this year’s draft and so is more forgiving to teams that are tanking at all costs.

Teams are ordered by the average of our rankings, with ties broken by the median. The arrows indicate changes from the July version, with thicker arrows (⇧ or ⇩) signaling moves of at least 5 positions. The fancy charts we tried last time didn’t quite work, so we're going for a more retro look this time, emojis and all. (Yes, we’re aware that a 🦩 is a flamingo, not a pelican; please register your complaints with the Unicode Consortium.) In line with FotF tradition, we’ll start from the bottom of the league and count up. That means we’re going to force you to endure a few paragraphs about the Sacramento Kings before we get to the good stuff; the biggest disputes usually tend to be about teams that rank somewhere between #15 and #7 on our list.

30. 👑 Sacramento Kings ↔

(Nate: 30 ↓, Joseph: 27 ↑, Jeremias: 30 ↔)

Jeremias: The Kings remain a laughingstock — they’ve appeared in the playoffs once in 20 years. This year’s annual attempt to make the postseason landed so short that they essentially fell backwards into doing what it takes to get better: losing for a better draft pick. In fact, the Kings have the NBA’s worst record and best lottery odds.

I’d give them more credit if this were planned, but it’s basically a function of two things: a lousy roster and an injury to Domantas Sabonis. Given the owner’s rather ham-handed use of analytics and questionable decisions about who to put in power, the Kings will probably continue down the path of NBA irrelevancy.

Joseph: I’m slightly more optimistic on the Kings than consensus. Yes, they’re the worst team in the NBA — but that’s precisely the point. As long as they keep tanking, the floor is the fifth pick, and this draft isn’t the big three that mainstream outlets keep pushing. It’s more like a big five. (Our NBA draft player ranking model, PRISM, will launch soon — Nate.) The drop-off in expected value doesn’t hit until pick six or seven, which means Sacramento is in line for a great prospect regardless of lottery luck.

That alone should put them ahead of New Orleans, who doesn’t even own their pick, and Chicago, who seems hell-bent on stacking guards and making the play-in tournament every year.

29. 🦩 New Orleans Pelicans ↓

(Nate: 29 ↔, Joseph: 30 ↓, Jeremias: 27 ↑)

Jeremias: The Pelicans caught a lot of flak for their 2025 draft-day deal, giving away a future “superfirst” — the best of their own and Milwaukee’s picks in 2026 — to Atlanta in exchange for Derik Queen. While Queen’s rookie season has given rise to cautious optimism, he still doesn’t represent nearly the expected value of the pick the Hawks are about to receive, which lands in the top three in the NBA lottery in 50 percent of my simulations. And this is an especially talented and top-heavy draft class.

But this predicament is just a symptom of a bigger, underlying problem: the grandiose confidence and ineptitude of Pelicans management.

And with Zion Williamson’s impact fading and rookie Jeremiah Fears shaping up as one of the NBA’s worst defenders, the Pelicans have no true superstar talent to build around. Add it up, and it’s hard to imagine New Orleans winning a playoff series in the foreseeable future, much less a championship.

28. 🧙 Washington Wizards ↓

(Nate: 25 ↓, Joseph: 28 ↔, Jeremias: 28 ↓)

Nate: It was, I think, at a Super Bowl party where I encountered a Wizards fan who was excited about the Trae Young/Anthony Davis combination. It does improve the floor, but FotF is explicitly about the championship upside, and both Young and Davis are probably negative-value contracts.

It doesn’t negate the Wizards’ other assets — Alex Sarr is eventually going to be a solid rotation player, and possibly well above that. And in addition to Sarr, you’ve got … well, I’m not sure. This is typically the point at which I’d highlight an unheralded asset. But apart from Trae and AD, the Wizards don’t currently have any regular player with a positive EPM. OK, let’s round up on this one: Tre Johnson has an exactly league-average EPM (±0.0), and is shooting 38.6 percent from 3. That’s promising, offsetting perceptions that he was one of the more likely lottery busts. Still, I’m not sure there’s a plan here. Speaking of which…

27. 🐂 Chicago Bulls ↓

(Nate: 26 ↓, Joseph: 29 ⇩, Jeremias: 26 ↓)

Jeremias: I agree with Bulls GM Arturas Karnisovas, who said earlier this month, “It’s being in the middle. That is what we don’t want to do.” But the Bulls’ moves have not indicated much deviation from their multiyear approach of chasing a low playoff seed. When it comes to winning titles, the stated goal in Chicago, that’s probably the worst strategy to follow.

The Bulls did something peculiar at the deadline, trading for three of the worst defenders of the past three decades, according to 30-year Adjusted Plus-Minus.

Perhaps bad defense is part of the plan to become a truly bad team. But these players probably aren’t quite bad enough, in total impact, to sink the Bulls sufficiently in the standings. So the treadmill continues.

26. 🦌 Milwaukee Bucks ↔

(Nate: 27 ↓, Joseph: 23 ↑, Jeremias: 29 ↓)

Jeremias: Since the Bucks’ 2020 trade for Jrue Holiday — a successful move that led to an NBA title — their strategy has been to go all-in over and over again, mortgaging their future with win-now moves designed to placate Giannis Antetokounmpo.

But now it’s time to pay the fiddler. The Bucks are no longer even a likely play-in team, with the roster beyond Antetokounmpo sporting very few positive-impact players.

Giannis seems to perpetually have one foot out the door, with many expecting the Bucks to finally trade him this offseason. But even if he stays, that doesn’t augur an especially bright future, as he’s already 31 and now rather injury-prone. At least a Giannis trade would mitigate what appears to be a brewing disaster: The Bucks look sure to bottom out, and yet their high draft picks will keep going elsewhere, as they don’t control their first-rounders from 2027 to 2030.

Nate: For what it’s worth, I think the decision not to trade Giannis at the deadline was logical. Cap sheets and 2033 draft picks will free up in the summer. And currently-injured players present an asymmetric information problem that makes realizing fair value harder. But there’s been a paradigm shift around the league under the new-ish de facto hard cap toward recognizing that veteran max players with injury histories and/or obvious “fit” issues very much present franchise-crippling downside risk (as well as upside). You saw that in the low price for KD last summer. The apparent disinterest in Giannis from the likes if the Spurs, Rockets, Thunder4, Hawks, et. al., suggests the Bucks might suffer from a lesser version of the same phenomenon.

25. ⛵ Los Angeles Clippers ↔

(Nate: 28 ↓, Joseph: 24 ↑, Jeremias: 24 ↓)

Read more

You can find ELWAY’s Super Bowl odds at the ELWAY landing page here. But today’s newsletter provides a lot more detail about the game. To be honest, today’s newsletter is a bit overstuffed — rather than hitting your inbox two or three times amid a busy political news cycle, I’m sending one comprehensive Super Bowl preview considering:

• Where the Seahawks and Patriots would rank among the most unlikely Super Bowl champions;

• ELWAY’s retrospective ratings for every past Super Bowl;

• QBERT ratings for every past Super Bowl quarterback;

• A deeper look at the Patriots’ schedule;

• Is Sam Darnold too mediocre to win the Super Bowl? And is Drake Maye too young or too injured?

• Why Super Bowls tend to be high-scoring — just in case you’re thinking about betting the over/under line;

• And, the most likely exact final scores and the best Super Bowl squares based on 30,000 ELWAY simulations, including a downloadable EXCEL spreadsheet.

The Patriots and Seahawks previously faced off in Super Bowl XLIX in Glendale, Arizona. That game ended spectacularly when Russell Wilson was picked off by the Patriots’ Malcolm Butler on 2nd-and-goal from the 1-yard line with 26 seconds left, clinching a 28-24 victory for the fifth of Tom Brady’s seven championships.

The game is sometimes regarded as the best Super Bowl of all-time. But watching from the opposite end zone (I was lucky enough to attend on the company dime back when FiveThirtyEight was held in high esteem by my bosses at ESPN1). I remember actually feeling a bit deflated by how quickly it ended.2 According to Sports-Reference.com, plays from the opponents’ 1 result in an interception less than 0.5 percent of the time3 (fumbles are the more common problem because teams usually run rather than pass4). Instead, game over. There were about 2.5 seconds between the snap and when Butler fell to the ground with the ball secure in his hands; even a walk-off home run provides more time for contemplation.

But that game 11 years ago had to live up to a lot of hype. It was a matchup of giants. The Tom Brady Patriots were the Tom Brady Patriots. And the Seahawks had decimated the Broncos to win Super Bowl XLVIII in New Jersey and entered the season as favorites to repeat.

On Sunday, Super Bowl LX — that’s #60 if you don’t speak Roman Numeral — will feature the same uniforms, but there are no players on either roster left over from the 2015 game. And instead of a battle of juggernauts, it’s one of overachievers. At least according to Vegas, whichever team prevails will be one of the least-likely champions ever, right up there with the Rams’ stunning turnaround into the Greatest Show on Turf in 2000 and Brady’s first title in 2002.5 The Seahawks began the season with +6000 odds (60:1) to win the Super Bowl. The Patriots were +8000.

I’d love to tell you that ELWAY gave the Seahawks and Patriots better odds. But I can’t say that because we didn’t get the model up and running until Week 7. I can tell you that it’s been bullish on each team ever since we began publishing our forecasts. Not that ELWAY got everything right (let’s not talk about the Lions, please). But those various ELWAY-inspired Seahawks futures bets I made earlier in the season might be enough to save my year.

Still, the truth is that, as much as it might be “fun” to gamble ELWAY’s reputation on a Super Bowl pick that defied the conventional wisdom, this is one of those games where the model is fairly well-aligned with Vegas. The system has been higher on both participants than the consensus. But since it likes both of them, that sort of cancels out. Still, there’s a lot to consider, including a couple of “X-factors” that aren’t accounted for explicitly by the model.

The history of the Super Bowl, according to ELWAY

Since ELWAY ratings never entirely reset but carry over partly from season to season, that means technically speaking, the very first game in our database from 1920 affects the system’s ratings for every subsequent game. So, we can see how ELWAY would have rated every Super Bowl matchup — and how the Seahawks and Patriots line up against past participants. What I’ll show you here is a simplified version of our retrospective ratings. They include a team’s baseline rating plus an adjustment for the starting QB in the game. There are various other adjustments that ELWAY makes: the most important of these is for injuries. That’s an important one this year, since both the Seahawks and the Patriots are notably healthy (unless Drake Maye’s shoulder problem proves to be something that can’t just be shrugged off). However, we only have injury data dating back for the past few years so I’m not including those adjustments in this table. The other adjustments that ELWAY makes don’t tend to matter much for the Super Bowl.6

Read more

It’s safe to say that this has been a weird NFL season.

Before the year began, Vegas odds posited what were essentially six Super Bowl co-favorites: in alphabetical order, the Bills, Chiefs, Eagles, Lions, Packers and Ravens. Let’s throw in the Commanders here too, since they had the next-best odds and played for the NFC Championship last season.

Of those seven teams, only three even made the playoffs. And only one, the Eagles, is hosting a playoff game this weekend.

True, the standings might have been upended if a few balls had bounced differently. The Chiefs were 1-9 (!) in games decided by seven points or fewer after having been 10-0 (!!) in those games last season. Conversely, the Broncos were 1-6 in such games last season — but 9-2 this year. And that’s before getting into untimely injuries.

ELWAY, the new NFL model that we launched early in the season, didn’t necessarily have a lot of hot takes that bucked the consensus — but it wasn’t a great year for the consensus. Nevertheless, we think ELWAY can offer some real insight on the postseason. By being plugged into QBERT, it recognizes how much of a difference Drake Maye has made for the Patriots, for instance. It can account for the impact of injuries — a big deal in the case of the 49ers, Chargers and Packers — and has more detailed calculations than other systems about the effects of weather, home-field advantage and rest.

And because ELWAY ratings change iteratively over the course of the season, it can weigh preseason expectations — which still matter in a sport where the season is just 17 games long — against who’s coming into the playoffs hot. So here’s how each playoff team’s ELWAY rating has evolved over the course of the year:

(For a more detailed version with non-playoff teams included, see the ELWAY landing page.)

ELWAY started out the year skeptical of the Jaguars, but it has since considerably revised its view of Trevor Lawrence and the rest of the team. The Seahawks and Patriots basically kept looking better and better over the course of the season — and that had been true for the Rams too, until they scuffled a bit in the final few weeks.

Other teams, like the Eagles and Bills, are basically what ELWAY thought they were, though it’s more bearish on both teams than other systems. Speaking of the Bears, ELWAY still stubbornly insists they’re a slightly below-average team, which reflects a combination of lingering preseason skepticism and a defense that gave up 6.0 yards per play, tied for third-worst in the league.

But to answer the question in the headline, who is the Super Bowl frontrunner? (There’s no “favorite” per se since every team’s chances are well below 50 percent.) Well, the photo gives it away: it’s Seattle. But I have a lot more detail for you on how all of this works, with a capsule summary considering the strengths and weaknesses for every remaining team.

The version of the team ratings you see in that pretty chart above doesn’t account for quarterback and injury adjustments, whereas ELWAY’s simulations do. So let’s take at those. Non-QB injuries are often neglected by casual fans, but they go a long way in differentiating positive and negative surprises.

The way ELWAY works, basically every team gets points marked off for injuries. (Our model also accounts for mid-season trades, which can have either a positive or negative impact.) But it’s the relative impact that matters. In the table, the injury/trade adjustment is tantamount to a point spread: the Seahawks project to be only 0.5 points per game worse than if they’d had their entire preseason roster healthy. Other positive surprises like year like the Broncos and Jaguars are also notably healthy.

For the Packers and Chargers, however, injuries take about 4 points off their rating per game. Usually, a team like that is going to struggle to make the playoffs at all: look what happened to the Lions. The 49ers are also coming into the playoffs in poor shape.

One more chart before we roll through the teams. I’ll admit this one is a little busy, but it gets at something critical in a 17-game regular season: the role of skill versus luck.

The Bears had a +22 turnover differential this season, which is highly impressive given that teams are much stingier about turning the ball over than they once were. (The New York Jets literally didn’t have a defensive interception this year.) There is, however, a large body of research suggesting that turnovers are mostly a matter of luck, especially on the defensive side of the ball. Efficiency data — things like yards scored and allowed per play, completions and first downs — tends to have more predictive power. Sacks are a more persistent indicator than turnovers, too.

Four of the 14 playoff teams actually allowed more yards per play than they realized themselves, whereas the Seahawks’ differential was the best in the league. ELWAY’s formulas for calculating all this stuff are somewhat more complex than what I’ve shown above — but the chart gets the basic idea across. An advantage built from a positive turnover differential may be a house of cards.

OK, then, let’s run through the 14 teams who are lucky enough to still be playing football, listed in ascending order of their ELWAY Super Bowl odds.

14. Carolina Panthers (0.2% chance)

Read more

Through an action-packed month, the Houston Rockets are blistering hot. At 10–3, with the NBA’s best offense, they’ve validated their loudest optimists and, so far, silenced their biggest skeptics, with their biggest test since their opening-night OT loss to OKC set to come tonight against Denver.

The Rockets were the NBA’s “surprise team” last season, blowing by Vegas’s preseason win total of 43.5 and climbing to second in a crowded Western Conference before getting bounced in a bruising seven-game series against Golden State. By my estimation, they’ve played their rebuilding hand quite optimally: tanking when necessary, stacking prospects, signing veterans, and capitalizing on their players’ development at the right moments.

Zooming outward, this is really as much as you can ask for since James Harden decided to force his way to Brooklyn five years ago. While some teams that lose a major star spend years bouncing around in NBA purgatory, the Rockets have parlayed a series of smaller moves into a relatively quick path to contention. Houston even made a mediumish-risk, highish-reward splash this summer, trading Dillon Brooks and Jalen Green for Kevin Durant. Durant hasn’t been spectacular, but he’s been healthy and good, and along with Alperen Şengün and Amen Thompson, gives the Rockets three players who rank in the top 35 in the league in EPM.

The Rockets have also embraced some unconventional tactics. Last season, they leaned heavily on Steven Adams’ offensive rebounding to generate second-chance points — the team grabbed an astonishing 43.9 percent of offensive rebounds when Adams was on the floor. This year, they’ve doubled down on size, playing Sengun and Adams in more minutes together. They’ve even supplemented the double bigs with Durant and Jabari Smith, creating further supersized lineups. The result is a 40.6 percent offensive rebound rate so far, a multiple-standard-deviation outlier relative to the rest of the league, and on track to be the highest rate in NBA history.

It’s hard to believe that just five years ago, the Rockets were running full-on Micro Ball with Russell Westbrook and James Harden. But reinvention has basically been their trademark for two decades.

Subscribe now

Some of Houston’s experimentation has been born out of necessity. They don’t exactly have a surplus of shooters, and losing Fred VanVleet to an ACL tear this summer only made that more apparent. On paper, this should be the kind of team built to withstand a rash of injuries — after all, their depth last season was so strong that even No. 3 pick Reed Sheppard couldn’t find consistent minutes. But that depth was concentrated in the frontcourt — the guard rotation is trickier.

Reed Sheppard, sneakily the weirdest player in the NBA

Sophomore guard Reed Sheppard started the year a little flat, but he’s been on a tear over his last ten games — putting up 21 points per 75 possessions on a scorching 69.2 percent true shooting and an even 50 percent from three. Still, the questions about his size and his ability to generate his own offense haven’t gone away. Sheppard is small, but he was not drafted to be a traditional lead guard — he’s much more in the mold of an off-ball player who can add the elite shooting that the Rockets badly needed.

This isn’t coming out of nowhere: Sheppard shot 52.1 percent from long range in his lone season at Kentucky, the sort of eye-popping number that the editor of this story (Nate) initially assumed must be a typo. Although Sheppard mostly came off the bench, it wasn’t a small sample with 144 attempts from three and remarkable consistency across catch-and-shoot attempts, off-the-dribble 3s, guarded and unguarded shots, you name it.

Does the shooting efficiency come at a defensive price? Not necessarily in this case. In fact, much of the intrigue around Sheppard’s draft profile was because of his stocks (steals and blocks) rate of 7.1 percent, abnormally high for someone of his size and (lack of) speed.

The broader question is whether steal or block rates are good measures of defensive impact. There was a point not so long ago when steals seemed like the most important stat. An old FiveThirtyEight article by Benjamin Morris, for instance, found that steals were worth the equivalent of 9.1 points in predicting player impact.

How can steals have such a large impact? Some of it is direct: they end the opposing team’s offensive possession and start a fastbreak. Fastbreak possessions are considerably more efficient. Furthermore, many steals occur on passes that would lead to open shots, or in close-to-the-basket situations where the offense’s expected value is high. So, a steal can be worth several points on its own.

But also, steals are a proxy for overall, harder-to-measure, defensive peskiness and activity. Most of the time, players with high steal rates are also good defenders as measured by on-court/off-court impact stats. So, yes, there’s a lot of empirical evidence that steals are super valuable — but practically, there are some issues with using steals as a measure of perimeter defense, the same way blocks are often used to signal the best rim protectors.

For example, in 2016, Stephen Curry led the league in steals — but how high did he actually rank in defensive impact within the Warriors’ rotation?

Steph certainly wasn’t as impactful as Draymond Green or Andrew Bogut — the Warriors’ defensive anchors — and probably not as valuable on that end as Klay Thompson or Andre Iguodala, who regularly took the tougher backcourt assignments. Curry was typically matched up with off-ball shooters, which freed him to roam passing lanes and rack up steals, even if he wasn’t necessarily the player creating the most defensive “value”.

According to EPM, Sheppard is at −0.3 points per 100 possessions defended so far on the season. EPM is measured relative to league-average — so Reed’s been average, basically. That might feel a little disappointing when you consider his prodigious steal rate (3.3 steals per 100 possessions). But average is more than acceptable for a short, relatively unathletic 21-year-old guard, a profile that typically implies a (massive) defensive liability.

Sheppard is filling big shoes. Why did analytics geeks, including me, think VanVleet would be so hard to replace? It’s a bit of a weird question. FVV last made an All-Star team in 2023.

The easiest answer is that he did some very important “small” things for this very unconventional team. VanVleet has been a positive defender for his entire career. His ability to navigate screens and play up to the ball handler without losing any space has made him one of the best point-of-attack defenders1 in the NBA for a decent stretch of his career, and he’s mostly aged gracefully in that regard.

Sheppard, on the other hand, is playing primarily off the ball defensively because he’s neither very strong nor laterally quick. Still, although he’s limited as a point-of-attack defender and exploitable due to his size, he does have good hands and can play within the gaps while passes are attempted to shooters.

I wonder, though: can Sheppard ever advance from average to being a good or even great defender? It probably depends on his system. It’s telling that the Rockets have already logged 221 possessions of zone coverage through 13 games — the most in the league by a wide margin. (This is becoming a theme: if you look up a given statistical category, the Rockets are probably near the very top or the very bottom.) The zone lets Houston lean more heavily on its double-big lineup. Without VanVleet at the point of attack, they’ve shifted toward a more balanced defensive scheme instead of letting opponents hunt individual matchups. Zones have traditionally been rare in the NBA because they concede more open threes, but they also offer a team-based defensive approach that helps Sheppard play directly to his strengths as a turnover creator.

The curious case of Amen Thompson

The Rockets also boast an electrifying talent in Thompson, who, by my estimation, is the most athletic player in the NBA.

This is not my most controversial take: Thompson is probably the only player in NBA history who can putback dunk his own miss from a standstill and sprint at a top speed of 23 miles per hour. P3, the sports performance firm, categorized him as a “Specimen” — a label reserved for the NBA’s elite athletes. Still, even within that group, Thompson has posted outlier numbers. His athleticism isn’t just raw or flashy; it’s functional. He uses it to sky for rebounds and cover ground without the ball in his hands. That last part —his ability to consume space — is arguably the defining trait of Amen’s game.

We talked a bit in my last piece about “gravity”. So let’s return to the question of why spacing has become so important in the NBA. Many casual fans claim that it’s just because 3-pointers are “worth” more than two-pointers, but let’s think a little bit about that. Is a team’s main objective on every possession to shoot a three? What is the most efficient shot in the game?

Unsurprisingly, rim attempts are still the most valuable shots in basketball. The more important trend, however, is a spike in rim efficiency over the last 10 years.

Raise your hand if you’ve been in this situation. You’re peacefully trying to watch a basketball game, and that one guy — maybe a couple beers down at this point — goes on a tirade about how the NBA is getting softer:

“All players do is shoot threes now! None of them want to get to the rim! Back in the 90s, players played defense!” — Random Guy In Bar2

A simple data dive dispels most of these narratives. We see that rim attempts have been fairly stable since 2001. It’s really just long midrange jumpers that have declined.

It turns out rim efficiency and 3-point attempts are highly correlated, and for good reason: the function of spacing isn’t just that 3 > 2. Rather, spacing makes offensive interactions, such as the pick-and-roll, less congested. In theory, the more functional 3-point shooting a team has, the more space there is to operate, and the offense as a whole becomes more efficient.

These trends also shed light on why many fans think defense is a thing of the past. Offense seems to be “winning” because reliable 3-point shooting now stretches defenses to their breaking point. In terms of pure talent, defenses are probably better than they’ve ever been — it’s just that modern rules and offensive spacing have made it harder for even elite defenders to bend the game in their favor.

I admit this was a long detour, but it leaves us with an essential question for a player of Thompson’s archetype. What happens when a non-big has no hope of becoming a good shooter?

In the mid-2010s, Tom Haberstroh, searching for a way to quantify this, created “respect rating”. Respect rating combined two measures, gravity and distraction, to capture the degree of defensive attention a player commands. In essence, it quantified floor spacing. High scores usually belonged to shooters who stretched out defenses, while most low scores went to those who didn’t draw extra coverage.

The results, for the most part, were not surprising. Stephen Curry, Kyle Korver, Klay Thompson, and other shooters were consistently at the top. However, one name popped up that didn’t belong with the rest: Dwyane Wade.

Why did defenders pay attention to Wade so much, even if he was a pretty famous example of a poor 3-point shooter? You would think, based on some of the narratives about the modern NBA, that non-shooters can’t function outside of a few offensive actions. In D-Wade’s case, it was his cutting. He had a knack for executing his bursts to the basket at the right moments — sometimes unseen and sometimes just too fast — and that required his defender to stay attached to him.

Basketball is a game of attention, and while shooting is a great way to garner it, it’s not a universal requirement. For example, in this play, the Clippers run a pick-and-roll action for Ivica Zubac. Christian Braun camps himself in the paint while Kris Dunn sits at the 3-point line (Dunn is a 32.7 percent lifetime 3-point shooter, despite a diet of mostly wide-open shots). This means the Nuggets have a clear advantage, right?

Well, it turns out the Clippers can scheme around this — Dunn times the cut perfectly, and even if he doesn’t receive the ball, the Clippers are left with an advantage. But Dunn is an okay cutter at best. What happens when the cutter is bigger, faster, and leaps over everyone? When that cutter is Amen — 44-inch vertical, 7-foot wingspan, elite contact finisher — the geometry becomes even more warped. Thompson isn’t D-Wade (yet), but the impact metrics have him as a significant net positive on offense (not to mention a DPOY contender) largely because of that cutting.

Şengün is pressing all the right buttons

Part of Thompson’s upside depends on the personnel around him, particularly Şengün. I’ll be up front: I came into this season as a Şengün skeptic. Some of this was perhaps grounded in aesthetic bias: I’m not opposed to post players so much as doubtful of their ultimate ceiling.

Furthermore, the “Baby Jokić” comparisons weren’t exactly positive to me. Nikola Jokić is a player who fundamentally shouldn’t work — but he’s synergized skills that stretch the imagination. When most fans think about Jokić, they’re probably reminded of the ridiculous post-up scoring and passing. But is that the only way to describe his uniqueness? Geometrically, Jokić also provides value from his ability to occupy areas other than the low block. It’s not just that he can score from the post, it’s that he allows possessions to flow downstream without disrupting the tempo of his teammates’ cuts.

This ability has only manifested in a few players in NBA history — Jokić, Chris Webber, and Vlade Divac are among them. Despite being a good post player, Şengün had struggled with the rest of that skill set. Last season, he didn’t grade out as efficient across most play types, especially those that depended on athleticism. As a roll man and cutter, he was one of the worst centers in the NBA:

Granted, the sum was somewhat greater than the parts: the Rockets had a +7.5 net rating with Şengün on the floor last season. But this year, that’s exploded to a staggering +13.4 with him on the court.

Is this early-season noise or the result of real development? One notable finding is that Şengün’s share of possessions as the roll man in PNRs has declined significantly — from 18.2 percent in the 2024-25 season to only 6.4 percent now. While some of this is the result of their offseason moves3, it’s possible the Rockets are pivoting entirely towards a Şengün-ball offense.

And defenses have reacted with more attention. Last season, the defense committed to or double-teamed Şengün on 65.1 percent of his post-ups4 — a solid rate, but nothing in comparison to players like Jokić or Giannis Antetokounmpo, who saw rates above 75 percent. This season, Sengun’s double-or-commit rates have skyrocketed to 80 percent in an admittedly small sample size. The efficiency of his postups and isolations has increased as well, suggesting real development.

What’s the final verdict?

In some ways, the story in Houston isn’t that complicated: young players typically improve, whether incrementally or through bigger leaps. As we’ve covered, Şengün, Thompson and Sheppard are unusual players — but they’re increasingly making the most out of their roles. The odds for that sort of positive development are much more favorable for younger players, but it also speaks to the good work of the front office and coach Ime Udoka.

There are also more red flags here than for a typical 10-3 team. It’s early in the season, and the league will find counters to the Rockets’ unusual tactics — if not now, then in the playoffs. Nobody on the roster is a safe bet to make one of the three All-NBA teams. VanVleet’s injury is obviously not ideal, and Houston might be best served by taking a more egalitarian approach to ball-handling this year. Letting the offense take shape organically while players continue to grow into their roles feels like the right move for a roster still figuring itself out.

Even so, this is one of the league’s best teams. I don’t think they’re equipped to beat Oklahoma City just yet — to be fair, who is? But they’re moving toward the inner circle of contenders, and there is the shape of even better things to come — a leap that should have H-Town buzzing.

This article is free. But if you’re an NFL nerd, I’d strongly encourage you to sign up for a paid subscription for full access to ELWAY and QBERT. They’re a lot of fun, especially as we’ve continued to add new charts and data. And fingers crossed, but so far ELWAY has also been ahead of schedule at keying in on the success of teams like the Seahawks and Patriots.

Subscribe now

I’ve never exactly not been an NFL fan. When I was 15, I even ran the equivalent of a neighborhood football betting pool.1 It would have been nice if those Lions of my youth had given Barry Sanders more than one playoff win.

Because of the league’s ubiquity in American culture, though, the NFL is the background hum that pervades every sports fan’s life. You’re always hearing it, seeing it — at the bar, the airport, the poker room — without necessarily really listening to or watching it. But suddenly, because of ELWAY and QBERT, I’m an avid fan of the NFL again. As with visiting a city where you lived as a kid, there’s an uncanny familiarity — that pizza place is still open? — but you also notice all the little changes.

For instance, with a fresh set of eyes, it’s easier to see why the NFL remains such a popular product with the modern, episodic viewer2 even as other sports have struggled to hold their audience. But it’s also a much different league than my teenage archetype. Take, for instance, the proliferation of final scores that read like high school locker combinations. For example: 36-29. 44-26, 44-22 (twice!). And that was just last week alone. These are not the scores of our pastoral football childhoods: the familiar 28-24s, 20-17s, and 21-13s generated piously by some sensible linear combination of 7-point touchdowns and 3-point field goals.

Of those scores last week, only 36-29 was officially a Scorigami, the term invented by the sportswriter and humorist Jon Bois for the first appearance of a score in NFL history. That’s because Scorigamis are a nonrenewable resource. What were once vast, untapped deposits of Scorigamis have gradually been strip-mined away, particularly since the introduction of the 2-point conversion in 1994. The remaining uncharted territory on the Scorigami map now requires dedication and creativity to reach, such as the 40-40 tie between the Cowboys and Packers or the Bills’ spectacular 41-40 comeback win over the Ravens in Week 1.

Nevertheless, I’m here to expand and revise the definition of Scorigami. 36-29 is still a scorigami even if it occurs again next week, I decree. You’ll notice I used a lower-case “s” there. A Capital-S Scorigami is the first instance of a particular score in NFL history. But a scorigami is any sufficiently weird, profane score, whether 2-0, 39-26 or 40-40.

And there are more and more of these. In the early days of the league, 77 percent of final scores were one of the following: 0, 3, 6, 7, 10, 13, 14, 17, 20, 21, 24, 27, 28, 31 or 34 points. Those represent every combination of zero to four touchdowns (with a successful 1-point conversion) and zero to two field goals. Now, only slightly more than half of final scores are one of those totals. The number 68 might have been the yardage on Jaguar kicker Cam Little’s recent record-setting field goal. But if an NFL game ends with a final score of 68-11 this Sunday, it honestly wouldn’t feel that weird.

Thus, I’m here to dub 2025 as the dawn of the Scorigami Era in the National Football League.

Share

There is, for one thing, a lot of scoring. The new NFL is prolific. At 46.6 points per game3, this year is tracking to be the third-highest-scoring season in NFL history (and, for what it’s worth, offenses generally improve as the season wears on). Everybody is getting in on the fun. While passing yardage has declined from its peak, quarterbacks are contributing more from the rushing game than ever. Kickers are making the impossible look routine. Rushers are averaging 4.4 yards per carry this season, close to the all-time high of 4.5 YPC from 2022. And kickoff returns are a thing again.

But as with origami, the second half of Bois’s portmanteau, the new NFL is precise. Coaches are finally nailing intricate 4th-down and 2-point-conversion decisions, long the bane of every NFL stat nerd’s existence, and strategy throughout the drive is changing in anticipation of a conversion attempt. Meanwhile, teams are stingier than ever about turnovers, and drives are longer as offenses efficiently string together a series of low-risk, medium-reward plays.

And yet, the net effect of the Scorigamified NFL is a little peculiar, as we’ll see.

NFL offenses are even more prolific than they seem

In working on projects like QBERT, which tracks all QB performances since 1950, and ELWAY, which dates back to 1920, you develop a sense for when there are phase changes in the sport. Some of these are even hard-coded into the models in various ways. When you’re working on a model, there’s often a trade-off between drawing the window further back because it gives you a larger sample size, or essentially chucking out the “old” data because it describes a regime that no longer exists in quite the same form.

One cute little thing we do in ELWAY, for example, is simulate discrete final scores. The raw form of the algorithm, for instance, might draw from a probability distribution to simulate a “score” like Chiefs 29.1, Broncos 22.6. Obviously, you could just round those numbers off: Chiefs 29, Broncos 23 is a perfectly plausible score. However, even in the Scorigami Era, scores like 30-23 or 28-24 or 28-21 are considerably more likely. (Not to put too fine a point on it, but these small differences matter when projecting the likelihood of a given point spread being hit, for instance.)

I won’t bore you with all the details, but essentially, ELWAY solves this problem by drawing from a database of 1 million simulated games played under our best estimates of modern NFL conditions. Each team gets a discrete number of drives, which might result in 0, 3, 6, 7 or 8 points — or occasionally, 2, 6, 7, or 8 points for the other team in the event of a safety or a defensive TD.

This is trickier than you might think because the home and away teams’ scores are interrelated. Especially late in the game, the current game score profoundly affects strategy: teams with leads run out the clock while trailing teams go for broke. The object of the game isn’t strictly to maximize the number of points scored, but to score at least one more than the opponent. In our simulations, a team trailing 21-13 late in the game will never kick a field goal on its final possession, for instance, and it will always go for two if it’s lucky enough to score a touchdown.

Still, the backbone of ELWAY’s simulations is basically this:

Excluding possessions where the clock runs out on the half, these are essentially the eight ways that an NFL drive can end. So it’s important to know the relative frequency of these outcomes. And that’s been changing a lot. As compared with the start of the millennium from 2001-2005, teams are scoring considerably more touchdowns and field goals (but missing slightly fewer FGs as kicking accuracy improves). They’re also punting less and committing fewer fumbles and interceptions — although they’re turning the ball over on downs more often because of a much higher propensity to go for it on fourth.

Overall, the average drive so far in 2025 is producing slightly more than 2.1 points, as compared to 1.6 points over the 2001-2005 window. That’s roughly a 33 percent increase. And that’s huge. Larger than the 28 percent increase between when the NBA bottomed out at 91.6 points per game in its 1998-99 season to its high-wattage 117.0 PPG (!) so far this year.

But there’s a “trick” that keeps this offensive explosion hidden. In football, successful drives run more time off the clock: a 17-play, 88-yard scoring drive might take up fully half the quarter, while a four-and-out can occupy less than a minute of game time, especially since incomplete passes stop the clock while completions usually do not4. So far this year, each team is averaging 10.5 drives per game, compared to 12 drives per game as recently as 2013. Thus, even though overall scoring has increased, the net increase in final scores considerably understates the gains in offensive efficiency.

Where do these gains in scoring efficiency come from? One important factor is that the field has essentially been shortened on both ends.

So far this year, following opposing kickoffs, teams have taken possession of the ball a tick downfield from their own 30-yard line on average. And this is the best starting field position they’ve had in years. The reason is the NFL’s dynamic kickoff, introduced last year. The new kickoff was designed to incentivize returns while reducing the possibility of injuries. In 2023, the last year of the traditional kickoff, 73 percent of kickoffs went for touchbacks because most modern kickers can boot the ball deep into the end zone, and returning teams generally wouldn’t bother with the marginal cases anyway lest they risk an injury.

Last year, 64 percent of kickoffs resulted in touchbacks all the same, even though the touchback was moved to the 30-yard line. But this year, the touchback has been moved out further to the 35-yard line, reducing the touchback rate to just 17 percent. Interestingly, this further tweak has only improved field position by about half a yard on average; kickers are becoming more adept at finding the corners of the landing zone. Still, possessions following kickoffs are starting around 7 yards further downfield than they did a decade ago.

If you’re starting at, say, the 33-yard line, you’re now really only two first downs away from being within field goal range because field goal range basically starts at midfield. As I’ve learned the hard way — I’ve, uh, lost a few bets recently this way — kicking off at the end of the half with say 20 seconds left on the clock is suddenly quite dangerous. The offense starts with the ball somewhere around its 30 … two quick completions … 59-yard field goal attempt … boom, game (probably) over.

Kicking efficiency has been improving for years, but relaxed restrictions around so-called K-balls this year — kickers basically get to use a fresh-out-of-the-box ball instead of one scuffed up by game action — has been an accelerant. If kickers like Little (who also kicked a 70-yard FG in a preseason game this year) and the Cowboys’ Brandon Aubrey are exceptions, it’s only by a few yards’ worth of distance. The rate of field goal attempts of 55 yards or more has increased by 135 percent compared to just four seasons ago, in 2021. And the number of makes from 55-plus has tripled in just four seasons. I’m guessing we’re in for some mean reversion — but so far on the year, kickers are actually converting 64 percent of their attempts from super-long-range.

But let’s not give too much credit to those kickers. I’m of the view that the value of quarterbacks, if anything, is a little bit understated by the conventional wisdom.

Quarterbacks aren’t quite putting up the gaudy counting statistics that they did a few years ago. Despite the 17-game schedule, no QB has passed for 5000 yards in a season since 2022, and none are on pace to do so this year.

Part of that is that QBs are opting for shorter, safer routes with exceptionally high completion percentages and little risk of interceptions. The average number of yards per completed pass is 10.1 this year, which is tied with 2023 for the lowest in NFL history.

However, advanced statistics like QBERT suggest that this risk aversion is probably smart for once. The league’s collective unadjusted QBERT rating is 89.6 this year, which is the second-highest ever behind an outlierish, pandemic-affected season in 2020.5 And in traditional NFL passer rating, quarterbacks are tied for their highest-ever rating at 93.6.

QBERT and passer rating differ ever-so-slightly in how they approach moving the ball downfield versus efficiency — QBERT is actually slightly more forgiving to old-school, gunslinger-type QBs — but it’s not a big difference. However, QBERT also accounts for a QB’s rushing contributions, as well as fumble and sack avoidance, whereas traditional passer rating does not.

Having, at the very least, some scrambling ability is now the norm rather than the exception. Thus far in the 2020s, starting quarterbacks have averaged 316 rushing yards6 per 17 starts, nearly twice the yardage they accumulated in the 1980s and 1990s. For the first time in NFL history, quarterbacks as a collective are gaining enough rushing yards to outweigh the yards they lose from sacks (even though this has also been a good era for pass rushers).

QB rushing touchdowns and first downs have increased proportionately. The latter category is particularly important; QBERT really likes quarterbacks who run for first downs. (Which helps to explain its Jalen Hurts fetish, for example — in 2023, he was second among all players in rushing first downs, including running backs.) The reason is that these plays produce a big gain in expected value. They often come on third down, and successful scrambles often reflect bailouts from what would otherwise have been negative-yardage sacks. The recent going rate of about 1.4 QB rushes for first downs per start might not sound like a lot, but those are often drive-saving plays.

Strategy is becoming more precise, especially on 4th down

In 2013, the New York Times launched its 4th Down Bot. It dutifully analyzed every 4th-down play and found, in line with a long lineage of work by academics and independent analysts, that teams were much too conservative, punting and kicking field goals when the rewards outweighed the risks if they’d simply entrust their offense to pick up a few more yards. 4th Down Bot last tweeted in 2017, claiming it was “tired” and moving on from the project.

Honestly, I’m relieved that our robot overlords still need days off. But I can’t exactly blame 4th Down Bot for feeling frustrated. Up through 2017, there had been no increase at all in 4th-down attempts despite all this research; if anything, they’d been declining slightly, perhaps due to some high-profile “go-for-it” calls that didn’t work out so well.

But like an artist who only becomes famous posthumously, 4th Down Bot, if it were resurrected today from its final resting place in a GitHub repository, would find that it hadn’t been wrong, only ahead of its time.

In 2017, NFL games averaged 1.8 4th-down attempts and 0.8 successful conversions. However, so far this year, those numbers have ballooned, roughly doubling to 3.1 and 1.8, respectively.

Note that this data is for both teams combined, so each team has approximately one successful 4th-down conversion per game in today’s NFL. Still, that goes a long way toward explaining why teams are stringing together longer drives. The success rate on 4th downs is also increasing. After consistently hovering around 50 percent for most of the league’s history, the 4th-down conversion rate has improved to 59 percent over the past two seasons.

The increased success rate is partly due to the tush push and other QB sneak plays. On fourth and very short — one yard to go or less — teams are opting to run about 75 percent of the time, and they’re converting 75 percent of those rushes as compared to 55 percent of pass attempts.

The data strongly suggests that teams should be running more; rushing plays are also converting at a higher rate than passes on 4th-and-semi-short (2 or 3 yards to go) and even at longer distances through the occasional draw play (although the sample sizes are small). At the same time, the precision of the short-yardage passing game is still enough to prevent the defense from keying in too much on the rush.

Other than mixing in even more runs, there may not be all that many gains left to be had from improved 4th-down strategy. In Week 10 games as analyzed by ESPN Analytics — basically the modern successor to 4th Down Bot — teams made the algorithmically correct decision 84 percent of the time on 4th down last week on plays where there was a material amount of difference in win probability7. And a handful of the “incorrect” decisions — mostly by the Philadelphia Eagles — were actually disliked by the bot for being too aggressive rather than too conservative.

Planning ahead for 4th-down attempts can also make the rest of the series more efficient. Compared to historical baselines, teams are now passing more on 2nd down at all yardages, but are actually running (slightly) more on 3rd down.

This makes sense if you work backward through the decision-making process. Take a typical 2nd-and-6 or 2nd-and-7 situation. If a team has only two downs to play with because it’s going to kick on 4th, it may figure it has to run or else it’s basically taking the running game out of the equation for the rest of the series (6-7 yards is a lot to ask for from a rushing play).

From a game theory standpoint, it’s always costly if your opponent knows what type of play you’re going to run. But if you’re planning on going for it on 4th, running on 3rd-and-semi-long situations (3rd-and-6, 3rd-and-7) can potentially set up a 4th-and-short. Meanwhile, on 2nd-and-short, you now have basically a free play downfield since you’re almost certain to convert given two opportunities to do so.

That’s especially true for teams like the Eagles that succeed in short-yardage situations at exceptionally high rates. They’re now basically facing a 1st-and-9 rather than a 1st-and-10 at the start of every series; that’s a huge advantage, although maybe one that ELWAY and QBERT are having a little trouble grasping.8

The rate of 2-point conversions has also increased, although progress in this direction has come in fits and starts. When the 2-point conversion was first introduced in 1994, teams played with their new toy fairly often. But the number of 2-point attempts per game fell from 0.25 per game in the debut year to just 0.07 by 2006.

Moving the 1-point conversion (extra point) line back to the 15-yard line in 2015 inspired more aggression because an extra point was no longer as much of a sure thing. Since 1994, 2-point conversions have had a 48 percent success rate (and this hasn’t really changed much9), thus producing 0.96 points per play. In terms of expected value — granted, not always the most important metric since the particular scoring margin matters a great deal for optimal strategy — the 2-point conversion produces fewer points if a team converts 99 percent of its extra point attempts. But if it only makes 95 percent of its extra points, about the rate since the rule change, the 2-point conversion results in slightly more points on average.

Why are there more weird scores?

The combination of more 2-point conversions and occasional missed XPs has a profound effect on the incidence of scorigamis. So far this year, 14 percent of touchdowns produce either 6 or 8 points, rather than 7. With 5.3 touchdowns per game combined between both teams, that means the typical game produces at least one TD that doesn’t result in adding 7 points to the scoreboard.

Granted, “typical” is doing a lot of work there, because unconventional conversion situations can come in bunches. If a team misses an XP and thus trails by 8 points prior to scoring, it will be much more likely to go for two following a touchdown. Still, this can contribute even further to funky scores. Say a team trails 17-9 and scores a TD. It will probably go for two. But if it fails to convert, it will be sitting on 15 points and is then basically past the scorigami event horizon for the rest of the game, with final scores like 18, 22 or 29 points becoming likely.

Even without any of this 2-point conversion business, the proliferation of field goals has contributed to scorigamis. So far this year, there have been 36 games where a placekicker made three field goals in a game, along with 18 instances of 4 FG games and six where they made 5 FGs. Overall, teams now score three or more FGs about 20 percent of the time, and the resulting scores are inherently scorigami-friendly. Three field goals produce 9 points, which is the same as a TD (with a plain-vanilla extra point) plus a safety.

A final factor in the scorigami singularity is simply the overall increase in scoring. Nearly all the capital-S Scorigamis that have filled in since 1994 have been high-scoring games. From the dawn of professional football through the start of the Super Bowl Era in 1966, the most common final score was actually 0 points. In fact, it was more common for a team to finish with exactly 2 points — so a safety and nothing else — than relatively common modern scores like 29 points or 40 points. Since 2000, only 10 Scorigamis — the exceptions are 58-0, 28-2, 36-3, 47-3, 52-3, 24-5, 31-5, 37-5, 38-5 and 52-6 — have been produced by the losing team scoring 7 or fewer points.

On the higher end of the range, however, new NFL overtime rules are providing additional Scorigami opportunities as each team is now guaranteed at least one possession under most circumstances.10 A game that ends 35-35 in regulation was once very likely to result in either a 38-35 or 41-3511 final score, neither of which is a Scorigami. However, the new rules open up additional possibilities like 43-42 or a 42-42 tie.12

Let’s compare four distributions of final scores:

• The empirical distribution in what I’ll call Early Pro Football from the founding of the APFA (the predecessor of the NFL) in 1920 through 1965;

• The Classic Super Bowl Era from 1966 through 1993;

• The Modern Super Bowl Era from the introduction of the 2-point conversion in 199413 through today;

• And finally, the simulated distribution of final scores from ELWAY, which is calibrated based on our best estimates of current scoring rates, strategies and overtime rules.

In Early Pro Football, 77 percent of final scores were one of the canonical scores I mentioned earlier — 0, 3, 6, 7, 10, 13, 14, 17, 20, 21, 24, 27, 28, 31 or 34 points, everything that results from 0-4 TDs (with an extra point) and 0-2 FGs. The rate of canonical scores declined only slightly to 72 percent in the Classic Super Bowl Era. But it has been just 62 percent so far in the Modern Super Bowl Era, and we expect that to decline further to 55 percent going forward based on ELWAY’s simulations of current conditions. There is an even more profound effect on both teams finishing with unusual scores.14

So what part of Scorigami Territory is likely to be conquered next?

The most likely unrealized Scorigami is 36-23 – honestly, not that weird a score — which has about a 1-in-1400 chance of occurring in any given game according to the ELWAY sims. Among scores where the losing team scores a touchdown or less, the most likely options are 27-8, 39-6 and 29-8. Among ties, your best bets are 25-25 and 22-22.

Overall, the chance of a Scorigami is only 2.7 percent in any given game, which means the over/under line is roughly 3 Scorigamis for the rest of this season. But in the Scorigami Era, every NFL game has the potential to produce feats of prolific and peculiar scoring that you’d never have imagined in your childhood.

Tomorrow is Election Day! You can find my latest take on the New York mayoral race here, and then Eli’s stories on Virginia here and New Jersey here.

Since my article was published, AtlasIntel, Silver Bulletin’s highest-rated pollster, released a survey showing Zohran Mandani with only a 7-point lead over Andrew Cuomo in New York. The average of all surveys conducted since Oct. 22 is Mamdani +14.6, however. There isn’t consistent movement toward either candidate, and Mamdani’s lead in other polls is as large as 26 points. So I don’t think anything major has changed — other than that some pollsters could have egg on their faces depending on the outcome. A Cuomo win wouldn’t be entirely unprecedented, since NYC mayoral polling has not been especially accurate. But it would be a significant upset.

Whatever happens, Eli and I will have a Substack Live chat about the outcomes with Ross Barkan of Political Currents at noon on Wednesday. We’re also hoping to get SBSQ back on schedule to post in the early part of the month, which means it should run late this week or early next week. You can submit questions for SBSQ #26 in the comment thread to edition #25. But for today, you’re getting something a little different.

Subscribe now

You probably don’t need me to tell you that Mexico City, which I visited last month, is a bustling and vibrant metropolis: great food, excellent museums, interesting architecture, and fun people-watching. I like pretty much everything about it except the altitude and traffic.1 There’s a reason more and more U.S. expats are moving there, sometimes to the annoyance of the local population.

And although it’s probably not the first thing most tourists notice, there’s a ubiquity of American sports logos in Mexico: unis, caps, even promotions at fast food restaurants. At the small airport I visited in Oaxaca, one guy even had a Blue Jays jersey on and an anxious look on his face which suggested he was white-knuckling it to make his connection on time to arrive in Toronto for Game 3 of the World Series that night.

I hope that fan will someday be able to see a World Series game in Ciudad de Mexico instead.

Share

Expansion in the “Big 4” sports leagues has largely stalled out. Major League Baseball hasn’t added a team since 1998, the NFL since 2002, or the NBA since 2004. The NHL has been more aggressive, with two-and-a-half recent expansion franchises in Las Vegas, Seattle, and Salt Lake City2, in part because it’s the only league to fully embrace its North American identity with seven teams in Canada.

Although owners may be short-sighted about not wanting to share lucrative media rights deals with new franchises, there’s also a good reason for this: the leagues are running out of viable markets. By metro area GDP — the metric I prefer because the reality in professional sports today is that teams are heavily dependent on wealthy patrons and even wealthier corporations to generate the truly big bucks — the biggest outlier is the lack of an NBA franchise in Seattle, the 9th-largest market in North America by GDP. Bring back the Sonics, please.

But after that, potential expansion candidates tend to prompt as many questions as answers. There’s no NHL franchise in Houston, which has a larger GDP than Seattle, but the performance of NHL teams in major Sunbelt metros has been uneven. Austin is growing quickly and is the largest U.S. market by GDP without any “Big 4” franchises. But a future expansion team — the Austin Armadillos? The Austin Bats3? — would have to compete against other Texas megafranchises like the Cowboys. San Diego has a larger GDP than you might think, but both the NFL (recently) and NBA (not so recently) abandoned it, opting instead for second franchises in Los Angeles. And sure, there are options around the margin, but they’d mostly populate the leagues with new “small market” teams. Charlotte, a candidate for MLB expansion, is larger by GDP than eight current Major League cities, but smaller than the other 22:

Enter Mexico City. It might be out of mind to most of us U.S. Americans, but it’s about as close to the East Coast as Phoenix or Salt Lake City. And there’s undeniable upside there. Mexico City is both the most populous city proper and metro area in North America. No, it doesn’t have U.S. standards of wealth, although you wouldn’t know it when strolling through wealthier neighborhoods like Polanco, where you could squint and tell yourself that you were in Miami or a more walkable version of Los Angeles. Per-capita GDP in the Valley of Mexico was about $13,000 in 2021, though it’s closer to $18,000 today given healthy GDP growth. However, the huge population makes up for it. Do the math, and Mexico City is the 15th-largest metro area in North America by GDP, in the same vicinity as Phoenix and Toronto. But Phoenix and Toronto have three “Big 4” teams each, when Mexico City has none.

True, this is something of an apples-to-oranges comparison. (Or if you prefer, peras con manzanas.) The San Francisco and Silicon Valley metro areas, which I’ve combined into one market for purposes of this analysis4, has roughly 3x the GDP per capita as the poorest markets with major league teams, such as Milwaukee, Las Vegas, New Orleans and Memphis. That’s a bigger difference than you might assume. But even New Orleans is 3-4x wealthier per capita than Mexico City. There aren’t any great points of comparison, and there are no guarantees that a CDMX franchise would find financial success.

Still, in the NBA, for instance, there’s nearly a one-to-one correspondence between metro-area GDP and franchise value. This data suggests that a Mexico City NBA franchise would be worth on the order of $4.5 billion, right in line with the expansion fees that the league is reportedly seeking for new franchises.

Mexico City is already turning out big numbers for U.S. sports

Mexico City has shown a great deal of enthusiasm for the U.S. major leagues whenever it has had the opportunity to experience them. The largest crowd for an NFL game in history was in Mexico City, when 112,376 fans watched a preseason game between the Cowboys and Houston Oilers at Azteca Stadium in 1994. More recently, the NBA sold more than 20,000 tickets to its annual Mexico City game on Saturday night, while charging typically expensive prices.

The Mexico City Capitanes — who, full disclosure, Silver Bulletin Assistant Sports Analyst Joseph George consults for — also led the NBA G League in attendance in 2023-24, an especially impressive feat given that they’re the only independent G League team (all of the others are affiliated with NBA franchises). With a roster featuring players from the Dominican Republic, Argentina, Brazil, Puerto Rico, Chile and of course Mexico, the Capitanes are a repository for Pan-American talent, but probably don’t have any budding NBA superstars on the roster. Still, they averaged around 6,500 fans per game last year and drew more than 19,000 for a game against the South Bay Lakers.5

Which sport should go first?

This is not necessarily meant to imply that the NBA should be the first league to make the leap to CDMX. Mexico has not exactly been a bastion of basketball talent; there are only four Mexican-born players in NBA history, none of them are currently in the league. However, the highest-profile Mexican prospect ever will soon join the league in the form of Karim Lopez, a lanky, mobile wing and a projected lottery pick who has drawn comparisons to everyone from Deni Avdija to — more flatteringly — Dr. J.

Based on Google Trends searches, the Big 4 league where interest is highest relative to the United States is Major League Baseball, where searches in Mexico are 50 percent of the volume as in the U.S. The comparable figures for the NFL and NBA are 36 percent and 28 percent, respectively. I’ll grant you that none of these numbers is extraordinarily impressive, but keep in mind that Mexico doesn’t currently have any Big 4 teams and they’d almost certainly increase if it did. Canada, by comparison, is fairly close to Mexico in search volume for the NFL, where it also doesn’t have a team. But it’s at par with the U.S. — indeed, exceeding it for MLB6 and greatly so for the NHL — in the leagues where it does.7

Personally, if you made me Czar of Sports for a day, I’d go with an NFL team there first. Put a Mexico City team in the AFC West — the Broncos, Chiefs, Chargers and Raiders all have fairly big followings in Mexico — and a Toronto team in the NFC North to help rekindle some old NHL Norris Division rivalries. It’s more of a logical stepping stone for the NFL than a London expansion franchise, which would create far more travel and logistical hurdles. (Our research for ELWAY suggests that time zone lags produce a massive difference in team performance.)

Why the NFL? For one thing, it’s by far the biggest league in Mexico City itself, with Google search volumes that exceed those for the other Big 4 leagues combined.8 And you can see this on the ground. When I stayed up to watch Game 3 of the World Series from my sad airport hotel room, the Spanish-language version of ESPN I was watching immediately switched to a recap of the Chiefs-Commanders Monday Night game once the baseball was over, including an interview with Patrick Mahomes.

With a relatively short season and low volume of games, the NFL could also be an easier sell to players who are hesitant about living year-round in Mexico, whether due to security concerns or other reasons. It’s also probably the sport least affected by altitude. At 7,349 feet, a Mexico City ballpark would make Coors Field look like a pitcher’s paradise, and with the cardiovascular demands that the NBA places on players, the Denver Nuggets consistently enjoy a massive home-court advantage. (ELWAY finds that altitude slightly helps the Denver Broncos, as well as adding yards to the kicking game, but the effects are more marginal.)

A CDMX team would find fans throughout Mexico

And in case you have any remaining doubts, keep in mind that a Mexico City expansion franchise would almost certainly find fans throughout a country of 130+ million people and not just in the capital.

We can state with some confidence by looking at the numbers for the Toronto Blue Jays and Toronto Raptors, the two singleton Canadian franchises in their respective leagues. Over the past five years, Google search volumes for the Blue Jays are about as 36 percent as high in other provinces as in Ontario. By contrast, the average U.S. MLB team achieves search volumes of only 5 to 6 percent of those in their respective home states.

For the Raptors, the numbers aren’t quite as impressive — and the NBA data is slightly weirder because teams can have comparatively high out-of-state search volumes either because the team has a strong national brand or because the team isn’t that popular at home (New York still hasn’t yet really taken to the Brooklyn Nets, for instance). Still, the Raptors perform relatively well. Their search volume is 21.5 percent as high outside of Ontario as within it, compared to 10.3 percent for the average NBA team outside of their home states.

Drawing in any material number of fans from outside CDMX would make the market truly massive. Excluding the Valley of Mexico, Mexico has a GDP of $1.44 trillion. If you give a Mexico City expansion team even 30 percent credit for that — so similar to the Blue Jays and Raptors — the total effective size of the market would be about $840 billion, larger than anything in the U.S. but New York, Los Angeles, San Francisco and Chicago.

Finally, although I’m mostly trying to give you a break from our politics-heavy coverage lately, there is one final part of the pitch. Personally, I’m a big fan of inter-American cooperation, and I think the United States is fortunate to have two peaceful and (increasingly for Mexico) relatively prosperous neighbors. The 2026 World Cup will be shared among the three countries, whose relationship has grown increasingly rocky lately. An NFL, MLB, or NBA team in Mexico could help in some small way to restore the longer legacy of friendly rivalry.

We’re just four days into the NBA season, and there have already been a lot of fun headlines, like Steph Being Steph, Victor Wembanyama looking like an MVP, and a double-OT thriller in a 2025 Finals rematch on Thursday night. But whenever your sports league makes front-page news in the New York Times, it’s probably bad news.

I love the Times (and used to work there). But it reserves its front-page real estate for stories it considers of profound international importance (so, the World Cup, yes, but the Super Bowl, no). It takes something truly monumental for an American sports league to receive the Page One treatment three days into its season. Like, say, a sports betting scandal involving the mafia, two federal indictments, and the arrest of an NBA player and head coach.

I have lots of takes on this, informed by my experience covering gambling and being a sports bettor myself. In fact, much of the alleged suspicious activity occurred during the 2022-23 NBA regular season, the year I bet the league every day as part of a sort of experiment/side hustle for my book. After a great start to the season, my trajectory was rocky; I finished in the black, but not by much. Betting the NBA regular season turned out to be a grind, mainly for one reason that also figures prominently in the indictments: the constant ambiguity surrounding star players’ availability due to minor injuries, “load management,” or tanking.

When betting the NBA, a difference of a single point — say, Vegas has the Miami Heat favored by 3 points, but you think they should be favored by 4 — is enough to turn a losing bet into a winning one, or vice versa. But the availability or lack thereof of a LeBron, a Steph, or a Jokic can shift the point spread by 6 points, 8 points, or even more. It is extremely valuable to have inside information about who’s actually playing — the sort of info that the alleged conspirators had.

Without that, your choices are between making a negative-EV bet, sitting the game out, or trying to read the tea leaves of where the insider money is flowing. The detailed notes I kept in 2022-23 show that I often did find something suspicious with betting lines for games mentioned in the indictment, though I didn’t always make the right bets. Sometimes I made the classic gambler’s mistake, being tempted by a line that looked too good to be true — and it was, because the insiders knew something that I and the rest of the public didn’t.

The second federal indictment involves poker, another subject I obviously know well. I actually have less to say here because the illegal underground games described in the indictment don’t overlap so much with my part of the poker world. But I can at least walk you through some of the distinctions between your friendly neighborhood home game and what the Feds alleged occurred in Manhattan, Las Vegas and other places. Hint: you probably shouldn’t be eager to play in poker games featuring a random athlete or two, and also people with nicknames like “Black Tony” and “Albanian Bruce”.

Share

Some obligatory disclaimers

News coverage of this story is also fraught because not only are the sports leagues in bed with the sports betting companies: so are many of the media outlets that cover them. ESPN has its own branded sportsbook, for instance. The Athletic (owned by the NYT) and The Ringer have sponsorships with sports betting companies, too.

As for Silver Bulletin, I’ll proudly acknowledge that one of the use cases for models like ELWAY, our new NFL projection system, is for people considering betting on the action.1 We explicitly recommend against using our models to bet without also having strong knowledge of both the sports themselves and the practical mechanics of sports betting — the margins are slim, and the books take a cut of every bet, so you need to get pretty much everything right to beat the house.

But I obviously have no ethical qualms with people who want to beat the lines the right way, i.e., with smart modeling and general sports knowledge based on publicly available information. And as I’ve said, I bet on sports myself. The bets are a small fraction of my income, partly out of choice (I have better ways to make more money with less risk) but also because even if I wanted to bet more, I’m limited in some capacity by most of the US-facing sportsbooks. There’s nothing special about me: they don’t like people who are even trying to be sharp. The combination of these limits on the one hand, and already-small edges being chipped away at by bettors with inside info on the other hand, makes the dream of living a good life as a sports-betting sharp off limits to all but a small fraction of people.

It’s early in the life of this story, so the rest of this is going to take the form of a series of bullet-pointed thoughts. I’m reserving the right to reprise this into a narrative-type story once more of the facts are known.

Overarching thoughts

1. Number 1 with a bullet is that I’m not that surprised by any of this. And I’m not just saying that to sound cool or world-weary. Because individual players have such a significant impact, the NBA is particularly vulnerable to cheating based on inside knowledge of player availability. Player prop bets — will Terry Rozier have more or fewer than 4 rebounds?— are another known vulnerability because of the possibility of collusion between gamblers and players.

2. I’m also not surprised by the growing backlash against sports betting, as seen in public opinion surveys. Gambling in general, and gambling on sports in particular, has usually existed in a liminal space. It’s been tolerated at certain times and places, whether explicitly or via some degree of “don’t ask, don’t tell”. But sports gambling has rarely been as “in your face” as it has been in the major American sports leagues for the past few years. There’s a lot of prudishness in the media about sports betting, which isn’t to say there aren’t also real harms. Still, ultimately, states are responsible for regulation, which means that public opinion matters. The continued expansion of sports betting into more states isn’t necessarily something I’d bet on — and it wouldn’t surprise me one bit if the dial moves the other way.2

3. You can make a pretty good defense of the licensed sportsbooks in that they’re better equipped to catch cheating than their shadier offshore competitors or traditional bookies. They have KYC practices — they know, more or less, who’s betting on what — and they can aggregate a lot of data together to look for suspicious betting patterns. And they have both incentives and requirements to work with regulators and law enforcement. It’s likely that some of this stuff has been going on for years, and now it’s easier to detect.

4. Still, I don’t find myself with much sympathy for the big US sportsbooks. While I respect their right to exist and have friends who work for these companies, I think they’ve been short-sighted in various ways, such as by offering too broad a menu of bets (some of which are more subject to cheating and manipulation), by so aggressively limiting skilled bettors, and through advertising that is so widespread as to understandably produce a backlash, especially on college campuses.

5. While there has always been a penumbra of gambling in the NBA — poker games in the back of the team plane, big wagers on golf, legal gambling in some states and illegal bookies in others — I also assume that the explicit endorsement of legalized gambling in the NBA and other leagues creates more of a permission structure for players and team personnel who might otherwise think twice before doing something foolish.

6. There are no free lunches here. Sensible constraints and regulations are very likely to reduce the profitability of the licensed sportsbooks.

7. However, there’s also another obvious trade-off. Restrictions on options in the regulated American market will push some of the action toward offshore sites, which are harder to monitor than ever because deposits are often funded by crypto. I don’t expect the trade-off to be 1-for-1, because the ease that fully legal, online betting provides reduces friction. But you might face a situation where, say, 70 percent of bets that would have been placed on FanDuel or DraftKings or Caesars wind up in the gray market instead with the other problems that causes.

8. Finally, before we get into the substance of the indictments, I need to remind you that these are just allegations. I’ve read several of these gambling-related indictments in the past. My personal view — an informed view, I’d like to think, but I’m not a lawyer — is that there are fewer open questions here than in, say, the case involving Shohei Ohtani’s interpreter, Ippei Mizuhara. However, the feds can be theatrical in how they present the evidence, and federal prosecutors aren’t necessarily gambling experts. The attorney Jim Trusty — good lawyer name — has questioned the allegations against his client, Terry Rozier, one of the players named in the indictments, and the NBA cleared Rozier after the league’s investigation.

The perils of player prop bets

In the sports betting indictment, there are basically two sets of allegations. One is centered around player prop bets, and particularly Rozier, who is accused of asking out of a Charlotte Hornets game on March 23, 2023 after the first quarter due to a “supposed injury” to ensure he’d hit the “under” on his projected number of points, rebounds, and other statistics.

If you’re only modestly familiar with sports betting, you might not be aware of the ubiquity of these prop bets. Traditionally, sportsbooks offered lines based on team performance — i.e., point spreads, moneylines, and over-unders (a.k.a. totals). Prop bets emerged partly because the immediate predecessor to full-blown sports betting under U.S. law, daily fantasy sports (DFS), was solely about player performance. However, in DFS, you’re competing against other players rather than the house.

9. Player props are inherently more subject to manipulation because only one player needs to be involved in rigging the outcome. In team sports, having only some players working to tank the game may not be decisive. The infamous 1919 Chicago White Sox actually won 3 of their 8 World Series games, for instance, in part because not all players were involved in the conspiracy. Likewise, a number of bets that the mob placed during the 1978-79 Boston College point-shaving scandal wound up being losers because most of the players weren’t participating.3

10. Even as someone more sympathetic to gambling than most people you’re probably reading on this story, I’m not sure I’d really care if player prop bets were banned entirely. They produce abusive behavior toward players. They also often put team and individual performance into tension with one another: you might be rooting for a guy to take a 3 so he’ll hit the over on his points prop, when passing to the cutter for an easy layup would be better for his team.

11. Furthermore, player prop bets tend to be targeted toward recreational gamblers, which is part of why the sportsbooks like them. They have lower limits, because if you’re betting hundreds of thousands of dollars on a mediocre player like Terry Rozier doing much of anything, that’s inherently somewhat suspicious. They also tend to have a larger “hold”, meaning that the sportsbook takes a larger cut of each bet.4 So while, in theory, a sharp bettor could probably build a good model of, say, how many rebounds individual members of the Boston Celtics were likely to obtain in a game, it’s often not worth their time because they’ll face relatively low limits, and the sportsbooks will probably restrict their accounts further if they show a propensity to win these bets.

12. In the indictment, you’ll see that quite a few different co-conspirators are involved in placing bets on Rozier, in amounts ranging from as little as $800 to as much as $107,000. Again, this likely reflects the fact that sportsbooks place lower limits on player props than on traditional bets like point spreads, so you’ll need to get a number of people involved to get the money down that you want.

13. Still, while the smaller bets probably reflect some degree of loose lips within the operation — someone’s brother’s cousin catches wind about something — the bigger bets are large in the context of player props. (If I walk up to the betting window at the Wynn with a suitcase of $100s, they’re certainly not going to let me bet $100K on the Terry Rozier rebounds under.) Some of these accounts are probably from customers that the sportsbooks have flagged as “VIPs” or “whales” because they have a history of what the sportsbooks believe to be negative-EV wagering for large amounts. The flip side of limiting the stake factors for sharp players is that the sportsbooks will let VIPs bet obscene amounts with few or no questions asked. Whale/VIP accounts are valuable because these accounts can be used as “beards” by sharp players whose action has been limited.

14. Offering the same limits to every player, or at least reducing the spread in the stakes offered, as some states are considering, would make the process fairer for all involved but less profitable for the sportsbooks. Fine, let people who have the misfortune of watching the Hornets make a $100 or $500 bet on Rozier. But when you’re letting some bettors place tens of thousands on such a bet, you’re asking for trouble.

15. As an aside, states that license sportsbooks probably ought to demand more transparency from them about how much money they’re making from different sorts of bets. I can’t find reliable estimates of what percentage of sportsbook action comes from player props. ChatGPT estimates 10-30 percent, but with low confidence, in part because these bets are often popular parts of parlays (i.e., the Chiefs beat the spread, Travis Kelce scores a touchdown, and Patrick Mahomes throws for at least 275 yards).

16. I don’t want to be too didactic here. But at a minimum, player props should probably be limited to prominent players in professional sports. Rozier is one thing — at least he was a starting point guard. But why on earth would you want to bet a substantial amount on an obscure reserve like Jontay Porter, who was permanently banned from the NBA after participating in a similar scheme to the one Rozier is accused of, unless you had inside information of some kind?

17. And while there’s some sort of protection from professional players colluding with gamblers because of their handsome salaries, that isn’t true for college athletes.5 As I’m writing this on a Saturday morning, for instance, FanDuel is offering bets on how many receiving yards that Yamir Knight of the SMU Mustangs will have in his game against Wake Forest. Nothing against Knight, but he isn’t even an NFL prospect, averaging just 44 yards per game so far this year.

Why are multimillionaire athletes getting involved in this?

Before we move on to the next section of the indictment, let’s pause to ask a bigger question. Rozier is not a star, but he’s made $135 million so far in his NBA career. So why are players like him (allegedly) participating in these schemes? That’s not clear, but here is an incomplete list of plausible explanations:

18. A player could think it’s easy money and/or that there’s little risk of detection. I’m of the view that professional athletes are mostly quite intelligent. But they receive exorbitant salaries at a young age, many don’t complete college, and they’re notoriously bad at managing their financial affairs.

19. They may also think they’re invincible, especially if they get the sense that the league isn’t interested in exposing their behavior.

20. They could fall in with the wrong crowd, start out thinking they’re doing a solid for their buddies, but this could spiral out of control.

21. They could be involved in other forms of (illegal) gambling and owe money as a result.

22. And/or they could be threatened by the mafia or illegal bookies, who say they will expose their gambling or other illicit or embarrassing activities.

23. And sometimes, con artistry is its own “sport”. As my podcast cohost, Maria Konnikova, has covered in her book, pulling off cons is not always rational in a narrow financial sense. People do it because it’s fun to elude detection and get away with it. And professional athletes are highly competitive. Throughout their lives, they’ve won most of the “games” they’ve played.

Inside information on NBA player availability is often reflected in betting lines

I’ve been betting the NFL this year for really the first time in my life. I’m not betting enough to make a significant profit, and while I’m off to a good start, I’d be happy to finish in the black at all because NFL lines are notoriously tough to beat. But it’s fun to see how ELWAY is doing, and I think it makes me a better modeler to see how our numbers square up against Vegas.

So no apologies for this. But honestly, the NFL has been kind of a relief, because there’s much less ambiguity about whether players will be available. Officially, both the NBA and NFL have diligent injury-reporting practices; NBA injury reports are updated once every hour, even in the middle of the night. In the NFL, though, there’s just one game a week, and players practice throughout the week. Furthermore, there’s far less tanking and “load management”. It’s rare for a quarterback to be a “game-time decision” or for him to sit the game out unexpectedly. And while quarterbacks have similar impacts on point spreads to NBA stars — Lamar Jackson versus Cooper Rush might impact the Ravens’ line by a touchdown or more — that isn’t true for non-QBs, whose availability will generally make a difference of just 1 or 2 points at the most.

24. The indictment describes several instances where information about player availability was known to insiders before the general public. For example, that “Co-Conspirator 8”, almost certainly Portland Trail Blazers coach Chauney Billups (who was also named in the poker indictment), informed his co-conspirators ahead of time that the Blazers would rest Damian Lillard and other star players in advance of their March 24, 2023 game against the Bulls because the team was tanking. I don’t know the ins and outs of how player availability information circulates to the league office, but there has to be some room to tighten up the protocols. Once you’ve made a decision on who’s playing, you ought to have an hour to report it to the league — and if you don’t, you should face fines and suspensions.

25. However, sometimes you can detect this from betting lines just because it’s so obvious that there’s private information impacting them. My records show, for instance, that I bet on the Bulls in the late afternoon on 3/24/23 and then again shortly before tipoff.6 The line had steadily been moving against the Blazers in a way that suggested that Lillard and other starters were highly unlikely to play, even if they were officially listed as “questionable”. Moreover, I was routinely betting against the Blazers late in the 2022-23 regular season because I suspected them of tanking both before and after they officially fell out of playoff contention. If a team is telegraphing its intentions so clearly, what does that say about the competitive integrity of the sport?

26. The 2022-23 season was also the year in which the Dallas Mavericks blatantly tanked in order to avoid having to give up their first-round draft pick to the Knicks. They were fined $750,000, but that’s a drop in the bucket for then-owner Mark Cuban — and a first-round pick is worth far more than that anyway. One of my largest bets of the season was against the Mavericks on April 7, 2023, their penultimate game of the year, in which Luka Doncic played only 13 minutes in a game the Mavs ultimately lost by three points. In the notes column on my spreadsheet, I wrote “Just a fucking weird one, what is DAL doing? Luka 1Q?” about their plan to limit their star’s action, which they’d been explicit enough about but wasn’t clear to some of the fans in the arena.

27. Although the Mavs’ game wasn’t mentioned in the indictment7, the Orlando Magic’s April 6, 2023 game was cited by the Feds. In that game, the Magic basically rested all their good players, and some of the co-conspirators had been tipped off in advance. I’d bet against the Magic in that game, writing in my notes “both teams trying to lose”.8 Much of the final third of the NBA’s schedule is compromised by these perverse incentives.

28. Another co-conspirator mentioned in the indictment is Damon Jones, an 11-year NBA player who was a close confidante of LeBron James and allegedly had access to inside information about him. Nobody is accusing LeBron himself of wrongdoing. But James was often listed as “questionable” for Lakers’ games on injury reports, which is supposed to imply roughly a 50/50 chance of playing. Betting lines often told a different story. Because he’s such a high-impact player, you could basically impute what the market thought about his actual prospects of playing. For instance, if the Lakers would typically be 10-point favorites at home against the Hornets with LeBron, but 4-point favorites without him, a spread of Lakers -5 would strongly suggest that he’d miss the game. I often bet against the Lakers in 2022-23, both because our model thought the public overrated them and because their intentions were often telegraphed by betting lines. But on Feb. 9, 2023, a game mentioned in the indictment, I was apparently too tempted by a line showing the Lakers as 2:1 underdogs at home against the Bucks and placed a small moneyline bet on them, writing in my notes “Just an incredibly weird one”. The line didn’t make any sense unless LeBron was almost assured of not playing. But, of course, he was DNP’d with a “sore left ankle” and the Lakers lost.

29. The 82-game NBA regular season is incredibly tough on players, so it’s usually easy enough to come up with some pretense of an actual injury. But as I hope these examples show, the pretense is often wafer-thin, especially when a number of players on the same team just so happen to get “injured” at once. If a player is going to be rested for “load management”, that’s fine by me, and perhaps the season should be shortened. But the league should just be more honest about why players are missing games.

30. And they should make this clearer in advance. Instances of load management should be announced at least 12 hours in advance, or 24 hours in advance if the team didn’t have a game the previous day. If there’s a new, acute injury, fine — but the league should investigate teams if this happens unusually often.

31. I’ll just leave this here as an accusation for now, since the league strongly denies it. But ESPN’s Brian Windhorst has reported that Rozier received what was essentially a paid suspension late in the 2022-23 season as the league investigated gambling-related accusations against him, but this was listed as a foot injury on official league injury reports. I don’t know who’s right here, but regardless, both the disclosure of a player’s unavailability and the reason for it should be tightened up. A chronic injury, an acute injury, a “personal matter,” and a load-management day all have different implications for a player’s availability for future games.

32. One option to reduce the impact of inside injury knowledge would be to make bets contingent on key players participating, as some sportsbooks do for baseball with starting pitchers.

And what about those rigged poker games?

While at first glance, the poker and betting indictments might seem relatively unrelated, some defendants were named in both. And that doesn’t count Billups, who was named in the poker indictment and is almost certainly “Co-Conspirator 8” in the other one.

33. Poker and sports betting, as the canonical forms of skilled gambling, are birds of a feather that flock together. At many casinos like the Bellagio, for instance, the sportsbook is located right next to the poker room, and it’s pretty common to see players sweating out an NBA game while they’re waiting for the flop.

34. The large majority of people playing poker are not cheaters, and poker games played at licensed casinos in the United States are generally quite safe. However, it’s probably also safe to assume that players who engage in underhanded tactics at sports betting are also more likely to do so at poker.

35. Although one could say that there’s a blurry line between the $20 home tournament you play with your buddies every Thursday and the high-stakes, mafia-backed games described by the indictment, I don’t think that’s actually the case. The key distinction is this: Is the person hosting the game making a profit from it? I don’t mean a shared collection for pizza, beer, a third-party dealer, or other mutual expenses. Rather, is the house taking a rake or otherwise making a profit? This is not legal advice, but home poker games without a rake are explicitly legal in many states and aren’t likely to be a target of law enforcement even where they aren’t, except perhaps for very high stakes or if they’re a nexus for other illicit activity.

36. I’ve played in plenty of home games across a wide variety of formats and stakes, but I’ve never played in a raked game outside of a casino, and I’d strongly advise against doing so. The mere fact that such a game is almost certainly illegal raises questions about the ethics of the people running it. Even if there aren’t physical forms of cheating such as rigged decks, the dealer might palm chips (take more rake than he’s supposed to). Or you might have trouble getting paid out if you win. Or opponents might be colluding against you.

37. It’s not surprising that the mafia is involved in illegal raked games. You need to fade law enforcement, facilitate the exchange of relatively large amounts of money off the books, and so forth: the mafia is good at that kind of stuff.

38. Other precautions to take when considering home games: first, you’ll want to personally know many of the people involved, especially the host. And you’ll want to have some sense of why you’ve been invited. Often, the explanations are benign (they see you as a fish — or a fun guy to hang out with even if you might be a small winner). But if you get a last-minute text from your poker buddy telling you about a juicy, high-stakes game running on the other side of town, be wary.

39. The magnitude of cheating in the alleged Billups games wasn’t very subtle. There were rigged shufflers, marked decks, and hidden communication devices: basically, every way that you can think of to cheat, these guys were (allegedly) using. But cheats are often greedy, which is actually a blessing to those of us trying to play it straight. The cheating was also so obvious in the Billups game that the poker player Matt Berkey called Billups out by name on his podcast two years ago.

40. In the podcast, Berkey said that some professional players suspected they were being cheated, but returned to the games anyway because the other players were so bad that they couldn’t imagine how they were losing so much money. But from a Bayesian standpoint, this is precisely the 180-degree wrong heuristic. The more implausible it is that you keep losing money and taking bad beats because the other players are such fish, the more likely it is that you’re being cheated.

41. Professional athletes aren’t necessarily such bad poker players. In the WSOP, for instance, I’ve actually played against some superstar athletes, including Neymar Jr. (!) and the boxer Ryan Garcia. They tend to be aggressive and not afraid of losing money, which is more than you can say for most amateurs. But the World Series of Poker is one thing, and an underground game is another. Athletes seem like fun hangs, are presumably pretty bad at poker — but also add an air of legitimacy to the games. To get an invite to play cards with someone like Billups might seem like a win-win. But gambling situations that seem too good to be true usually are.

42. As for Billups’s potential motivations, I’d refer you back to the above section on “Why are multimillionaire athletes getting involved in this?”, but add a couple of additional considerations. First, the amount of money alleged to have been scammed from players in the poker games is large compared to the Rozier prop bets: according to the indictment, “at least $7,150,000.” And second, poker is a hard game to play well. You can imagine a professional athlete who’s used to being a winner being frustrated by losing money to a bunch of nerdy tech bros and poker pros when playing in straight poker games, and eventually finding other ways to come out ahead at the end of the night.

43. Poker is a great game. I’d argue that it is very plausibly a net good for society, which I’m not sure I’d say for sports betting. It teaches discipline, analytical skills, people reading, risk management, and lots of other useful life skills. It fosters camaraderie among people from different walks of life. And it’s not actually a major profit center for casinos, typically constituting somewhere around 1 percent of overall gambling-related profits. So it’s a shame that poker so consistently makes negative headlines when it breaks into mainstream news. In general, I don’t think the community of above-board professional poker players (and “serious” amateurs, which I guess is how I’d describe myself) goes far enough in ostracizing and shaming scammers and cheaters. But the Billups games are from a different part of the poker world.

Subscribe now

ELWAY is Silver Bulletin’s exclusive NFL team rating and forecasting system. It’s based on our analysis of every game in NFL history (since 1920), with a greater emphasis on recent seasons as more data has become available over time. QBERT is our quarterback rating system, which plays a prominent role in ELWAY.

Despite its official backronym (Elo with Lineup Weights and Adjusted Yardage), ELWAY shares only some elements with Elo rating systems, such as the legacy FiveThirtyEight NFL model. Each team gets a rolling offensive and defensive rating, which corresponds approximately to the number of points it would be expected to score and allow against an average opponent. These can be combined with other factors to project win probabilities, margins of victory, and the total number of points scored in future NFL games. ELWAY ratings are updated at the end of each week based on a team’s performance, adjusted for the strength of the opponent and other factors.

ELWAY has five distinguishing features:

1. In measuring team performance, ELWAY considers a lot more than just the final score. We evaluate which factors are more predictive of future performance. Offensive and defensive efficiency are generally more predictive of future results than game scores alone, particularly when adjusted for the score and after backing out factors like turnovers that can be more random.

2. ELWAY ratings are recalibrated at the start of each season based partly on roster turnover. Essentially, we calculate projected wins over replacement (WAR) for every player on the start-of-season roster based on recent performance, age, and, for younger players, draft position.

3. QBERT ratings are calculated in parallel with ELWAY and can significantly impact our overall projections. In line with Vegas point spreads, the effect of a QB change can be a touchdown or even more in extreme cases. We maintain a quarterback depth chart for every team, and the model accounts for the possibility of future injuries and benchings. We also directly account for current QB injuries.

4. Apart from the quarterback adjustment, ELWAY adjusts for several factors that reliably affect performance: non-QB injuries and trades, travel and rest, various forms of home-field advantage, and even weather and coaching changes.

5. ELWAY simulates the remainder of the NFL season thousands of times and plays out tiebreakers to calculate projected season-ending W-L totals and playoff and championship odds. Although simulation is obviously not a novel technique, ELWAY takes it a step further to mirror real-world behavior. For instance, in our simulations, a team is more likely to bench its quarterback, replace its coach, and keep more players on the injured list if it has a losing record. The simulations run “hot”, meaning that within each simulation, a team’s performance affects its projections further down that particular branch of reality.

Each of these components is described in more detail below.

1. ELWAY team ratings

Most NFL power ratings are based on the margin of victory or points scored and allowed in previous games. However, some statistics are more predictive of future performance than plain ol’ points.

Thus, ELWAY assigns each team an implicit offensive and defensive rating for each game based on a variety of box score statistics and factors related to the score. Essentially, ELWAY prefers teams that consistently move the ball. In general, these factors are most predictive of future performance on both offense and defense and receive a lot of emphasis in ELWAY:

• Yards per play

• Completions

• First downs

• Sacks

Conversely, these factors can substantially affect the outcome of any given game, but can be pretty random, and therefore have less of an impact in projecting future performance:

• Turnovers

• Special teams

• Third-down conversions

• Penalties

Points scored and allowed are somewhere in the middle of the spectrum. In a game like Super Bowl LIX, for instance, the final score (40-22) did not fully reflect the Eagles’ dominance. Still, scoring and preventing points is the object of the game, and there is some persistence in these factors. There is also a small bonus embedded in ELWAY for simply winning or losing games. You might think of this as a “clutch factor”. We believe that clutch play is generally overrated — but when aggregated over tens of thousands of past NFL games, there is a modest amount of signal.

One tricky factor in football is that offense bleeds over into defense and vice versa because a better offense creates improved field position and only one team possesses the ball at a time. The notion that “the best defense is a good offense” is true; if you have the ball, the opposition is less likely to score on you than against the 1985 Bears. ELWAY accounts for this.

ELWAY also calculates what would be called a “pace factor” in the NBA. Passing plays take up considerably less time of possession than running plays. In addition, some coaches prefer to burn more time on the play clock. (Playing at a faster pace is generally indicative of a higher-quality offense.) Basically, ELWAY separately projects the number of points scored and allowed per play, and the number of offensive and defensive plays it expects each team to have per game, and combines these to create its overall ratings.

ELWAY also considers factors related to the score and how it affects strategy. Teams with substantial leads late in the game have less efficient offenses by design1 — they’re trying to run out the clock — while trailing teams improve in yards per play. ELWAY accounts for this based on a log of point scoring: teams with substantial leads late in the game essentially receive an upward adjustment to their offensive statistics.

ELWAY also calculates a rolling expectation of league-average point scoring, with aggressive adjustments early in the season. Leaguewide point scoring and offensive efficiency can be affected substantially by rule changes. A team’s offensive and defensive ELWAY rating should always be considered in context relative to the league environment. An ELWAY offensive rating of 25.0 is pretty good in 2025, for instance, but it would be superlative in the 1970s.

In traditional Elo ratings, changes in team ratings always net out to zero for a given game. For example, if the Chargers gain 15 Elo rating points in defeating the Broncos, the Broncos lose 15 points. This is not true for ELWAY. Instead, both teams may wind up with net-positive or net-negative ratings for the game. Generally, efficient offense is an indicator of higher team quality, so a 38-35 game with no turnovers might be favorable for both teams in predicting future performance. Conversely, following a terrible game like this one (which Nate had the misfortune of attending), both teams’ ratings may decline relative to the rest of the league.

On the ELWAY landing page, we list three sets of ratings that you can scroll between:

• Ratings for the forthcoming week, accounting for the projected starting QB for each team and current injuries (and trades). Although these are not the only adjustments that ELWAY considers, QBs and injuries tend to be the most important ones.

• Ratings with injury adjustments (but not trades) backed out, and each team with its preferred QB1. Essentially, this reflects a team’s projected performance when all players are fully healthy. Teams may revert toward this injury-free rating later in the season — although we list the ratings with a team’s preferred QB1 even if he’s out for the year.

• Finally, a “raw” version of ELWAY based purely on team statistics, not accounting for quarterbacks (or injuries) at all. This rating is most comparable to other team rating systems. It’s also the starting point for ELWAY’s game-by-game projections: technically speaking, the identity and projected QBERT rating of the starting QB is an “adjustment” to these baseline ratings.

We also list an Elo rating for each team for comparability with past FiveThirtyEight NFL projections. However, it is not a separate rating system; rather, we derive it based on a team’s offensive and defensive ELWAY rating. Each point of projected net margin is worth approximately 21.5 Elo ratings points. But this varies somewhat. Offensive-minded teams have slightly more variance in their game-to-game results than defensive-minded ones, and our Elo formula (and our ELWAY simulations) reflect this.

2. Preseason projections and roster ratings

At the beginning of each season, a team’s ELWAY ratings are based partly on its ratings at the end of the previous season and partly on roster ratings2, which are calculated by essentially adding up WAR projections for each individual player on the roster. This process implicitly builds in some mean reversion, so the spread in ELWAY ratings tends to be wider at the end of each season than at the start.

Roster ratings are based on a series of calculations involving QBERT and Football-Reference.com’s Approximate Value (AV). Essentially, we calculate a projected AVAR — Approximate Value Above Replacement — for every player. However, this requires several transformations from the Football Reference version of AV.

• For QBs, we use QBERT as a substitute for AVAR.

• In the Football Reference version of AV, nearly all players have a positive AV, such that even the worst teams in NFL history have substantially positive aggregate AVs. So we incorporate a replacement level calculation, which is equivalent to the 25th percentile of players3.

• AV dramatically understates the value of quarterbacks4, who are assigned approximately 6 percent of AV — when, in fact, they are responsible for roughly 30 percent of total marginal value generated by players, we estimate.

For positions other than QB, value assignments are mainly based on the recent proportion of league cap hit above the minimum salary, with a slight upward adjustment for running backs, given that they’ve historically been valued in the league more than they are currently. Our overall estimates of the share of AVAR generated by position since 19605 are as follows. Values do not add up exactly because of rounding:

The overall value assignments are deliberately skewed toward offense (61-62 percent of overall value) versus defense (36-37 percent) and special teams (2 percent). In general, offense is more predictive of future performance than defense.

Our roster ratings cap the amount of value that can be derived from each position. For instance, if a team that already has a good quarterback also acquires a backup who is projected to have a high QBERT rating, our formula recognizes that he probably won’t get much playing time.

AVAR projections are based on AVAR generated over the past three seasons, age, and (for younger players) draft position. ELWAY also projects each player’s expected number of snaps taken based on these statistics, plus his snap count from the previous season.

Different aging curves are incorporated for each position. We estimate that running backs peak the earliest, around ages 24-25, while quarterbacks peak the latest, at 28-29.

For rookies, we project AVAR based on their position and draft slot. The coefficient for draft slot is nonlinear: there is a considerably bigger difference between the 1st and the 11th pick than between the 250th and the 260th pick. We also account for long-term trends in the performance of rookies. In general, rookie QBs and other offensive rookies have contributed more value in the past couple of decades than they once did.

3. QBERT and the quarterback “adjustment”

ELWAY projections are substantially adjusted for the projected QBERT ratings for the starting quarterbacks for each game. For more details on QBERT, see here.

However, this is trickier than it might seem because QB performance is also implicitly baked into the team ratings. (As mentioned, we estimate that about 30 percent of overall NFL player value is provided by QBs.) Essentially, we calculate a rolling average of the QBERT rating that a team has achieved in recent games and compare it against the projected performance of the expected starting quarterback for the next game.

These effects are most pronounced when a QB is injured or changes teams. But sometimes they can be counterintuitive. Whoever took over for Tom Brady on the Patriots once he went to Tampa was very likely to be worse than Brady, for instance. Thus, the post-Brady Patriots would have a negative QB adjustment for some period of time, even if the replacement was also projected to be above average.

Even if there’s been no recent QB change, the adjustment can have an impact if the QB is on a rising or declining trajectory, especially because of age. Quarterbacks improve substantially in their first ~20 starts as they gain more experience. A team with a rookie QB will generally have a downward QB adjustment in Week 1, but it could be positive by the end of the season.

The QB ratings punish quarterbacks for missing starts; both benchings and injuries are negative indicators for future performance.

ELWAY also evaluates each team’s QB carousel for the rest of the season based on manually updated depth charts. The program simulates benchings, and quarterbacks are much more likely to be benched following losses, poor performances and/or a poor ongoing QBERT rating. Conversely, young quarterbacks who are high draft picks are considerably less likely to be benched, holding other factors constant, as are quarterbacks who have already accumulated a number of wins during the season.

We also incorporate a subjective rating on each team’s depth chart that reflects the tenuousness of a given starter’s hold on the job. QBs designated as “rock solid” are very unlikely to lose their jobs, other than due to injury, while teams have an itchier trigger finger for QBs designated as “tenuous”. In rare instances, the starter might not be clear for the upcoming week; the model handles these cases probabilistically.

When a QB is benched, he’s generally dropped one slot in the depth chart to QB2, but the QB is rotated to the bottom of the depth chart in a minority of simulations.

Each starting QB also gets a long-term injury grade, which corresponds to a 1.25 percent (“Iron man”) to 4.5 percent chance (“High risk”) per game of missing at least one future start due to an injury. There is also a small chance of reserve QBs being hurt in practice. As of 2025, these grades are subjective, but we may look to automate them in future seasons.

We also account for current QB injuries based on an assessment of publicly available information on injuries. Players designated as “questionable” on NFL injury reports play in the subsequent game ~70 percent of the time, while players designated as “doubtful” play only ~7 percent of the time. However, we sometimes incorporate additional categories beyond this when the situation warrants it.

For injured quarterbacks, we also project a return date. This is usually probabilistic: for example, a QB projected to return at some point between Week 7 and Week 10 will randomly be assigned one of these weeks in each simulation.

QB adjustments are larger for teams where the QB plays a larger role in the offense.6

4: Non-QB injuries and other adjustments

Beyond incorporating QBERT ratings, ELWAY makes several other adjustments to its forecasts of each game.

Having short weeks, long weeks (due to the presence of Thursday Night or Monday Night games) or bye weeks can have a significant impact. A team coming off a bye (when the other team isn’t) gains ~1 point in expected victory margin. An extra day of rest (e.g., if the opposing team is coming off a Monday Night game) is worth about 0.3 points.

Teams playing at late hours relative to their home time zone can suffer a significant penalty. This is most pronounced when an East Coast team plays a night game on the West Coast. A 1 p.m. ET start (i.e., 10 a.m. local time) produces a slight disadvantage for a West Coast team, but the effect is smaller. Thus, West Coast teams have a slight intrinsic advantage built into the schedule.

Empirically, travel distance also matters. This reflects a combination of shorter flights, as well as how teams in close proximity to their opponents can bring more of their fans with them. There are special procedures in place when a team plays a “home” game outside of its metro area due to, e.g., natural disasters, or when both teams are in the same metro area (Giants vs. Jets).

Home-field advantage has been in gradual decline over time. Having spent a considerable amount of time on this question, however, we believe the extent of this is somewhat overstated, as the NFL season is inherently a small sample size and fluctuations in HFA can be noisy. Our HFA adjustment is slightly larger than the consensus, typically ranging from 1 to 3.5 points.

Cold weather, wind, and altitude increase home-field advantage — especially cold weather. Teams that play on artificial surfaces also have a slightly larger home-field advantage. This trend is surprisingly robust empirically and likely reflects the fact that home teams are familiar with the quirks of their own turf; however, the effect has declined as artificial surfaces have improved.

The weather forecast also affects our projected totals (over/unders) for each game. Wind is especially detrimental to offense since it impacts both passing and kicking, two rather important factors in the NFL! The gradual increase in scoring in the NFL over the past few decades is partially attributable to the shift to warm-weather and/or domed stadiums.

Home-field advantage is about 0.75 points larger in playoff games, controlling for other factors. The fanless games played during the COVID pandemic — home teams had a negative overall point differential in 2020 — also provided evidence for an intuitive conclusion: having an enthusiastic crowd can actually matter.

In fact, ELWAY implements a fan avidity rating for each team on a 1-to-5 scale. The primary ingredient in this is the relative rate of Google searches in the local market for the term “NFL” over the past 20 years. However, there are also some semi-subjective adjustments, such as player sentiment about the toughest places to play. Generally speaking, the NFL is a bigger deal in the northern half of the country, reflecting its historical origins at the saddle between the Northeast and Midwest, and HFA is smaller once you get south of roughly Kansas City or Washington. Green Bay and Buffalo are the only teams with perfect-5 avidity rankings. Kansas City is a 4.5, while Baltimore, Cleveland, Denver, Minnesota, New Orleans, Philadelphia, Pittsburgh, and Seattle are the next highest at 4. The two Los Angeles teams are the only 1’s. Keep in mind, however, that a team that overperforms at home may also suffer a bigger penalty in road and neutral-site games.

Non-quarterback injuries are accounted for based on injury reports. Since we already calculated a WAR for each player in Step #2, we basically just sum up all the positive WARs that are expected to be on the sidelines for the upcoming game. Injuries are segregated by offense and defense — so, for instance, a beat-up secondary will primarily affect a team’s projected defensive rating while injuries on the offensive line will mostly impact its offensive rating. Beginning with the 2025 season, we also account for trades since the start of the season.

In general, individual non-QB injuries don’t have that large an effect. In extreme cases, such as Myles Garrett or peak J.J. Watt, it might move the projected point spread by up to 2 points. But more commonly, even All-Pro non-QB absences only have an impact closer to ½ point to 1 point. Nevertheless, teams with a ton of injuries all over their depth chart can be significantly affected. Betting on NFL games without accounting for non-QB injuries is a risky proposition.

For weeks beyond the forthcoming one, we do not specifically project return dates or new injuries for non-QB players. However, ELWAY applies several empirically-derived heuristics:

• Injuries tend to accumulate throughout the season.

• Contending teams return players to action more quickly, while losing teams are indifferent or sometimes actively tank; therefore, in general, teams benefit from playing weaker opponents later in the season.

• Injured players are much more likely to return to action in the playoffs.

• Injured players return more often after longer rest periods, such as bye weeks.

• Finally, older teams accumulate significantly more injuries over the course of the year than younger ones.

Head coaching changes negatively affect performance for the first ~10 games of a new coach’s tenure, with the effect concentrated in the first several games. In-season coaching changes produce a bigger impact than changes in the off-season as teams will have a steep learning curve for new schemes and playbooks.

In our simulations, we also project the probability of mid-season coaching changes that could negatively affect a team’s ratings in future games. In other words, we model a team’s likelihood of firing its coach after each game. Teams with losing records are much more likely to fire their coaches, especially after big losses. Teams rarely fire coaches in the first few games of the season or if they’ve recently been hired, conversely.7

Empirically, there is a small degree of persistence in matchups; for instance, if the Eagles beat the Giants 41-10 in Week 1, it will positively affect their expectation when they play the Giants again later in the season.8 The effect is relatively minor but ELWAY does account for it.

Teams that have “locked up” a specific playoff seed often rest their QB and other star players and can underperform naive projections in the final game of the season (currently Week 18), and ELWAY accounts for this, too. The first cases we can detect of this were in 1989 (Bill Walsh and Marv Levy pioneered the tactic), but it is becoming more common. About two-thirds of the time in recent seasons, a team rests its starting QB under these circumstances.

5. Simulations

ELWAY simulates the rest of the regular season and the playoffs thousands of times from the current starting point. The simulations are dynamic: for instance, what happens in Week 7 of Simulation #1622 also affects Week 8 and the rest of the season in that universe.

In addition to simulating the schedule and changes to a team’s ratings based on its simulated offensive and defensive game scores, we also simulate quarterback changes, quarterback injuries, coaching changes, and non-QB injuries on a probabilistic basis.

For the forthcoming week, the simulations account for weather based on weather reports. For future weeks, weather conditions are simulated based on long-term climate averages for the month of the game in the metro area where the game is played. The simulations account for the historical uncertainty in weather forecasts, which is larger in some cities than others. For teams with retractable roofs, we account for the likelihood of the roof being open, which generally occurs with temperatures between 50-80°F and otherwise favorable conditions.9

The model also simulates a discrete score for each game and these closely resemble real-world values, e.g., a score of 24-17 is more likely to occur both in ELWAY and real-life than 24-18 or 24-19. Among other things, this allows for more precision when calculating the probability of a team beating a given point spread. As bettors know, some point spreads (especially +3/-3) are particularly important because these margins occur more commonly.

The score distributions are based on simulating hundreds of thousands of games, which account for the empirical likelihood of the outcome of a given drive (e.g., touchdowns, field goals, safeties) based on recent NFL data. For instance, we account for the recent proliferation in field-goal scoring. The simulations also take into account how the game score affects 2-point conversions and overall strategy.10 Furthermore, they incorporate new “playoff” overtime rules that have now been adopted for the regular season, in which each team now gets at least one possession in most circumstances.11

In the late weeks of the season and the first two rounds of the playoffs, game times (and in some cases the dates of games) are not set in advance due to flex scheduling. These can potentially affect travel and rest adjustments. For the regular season, we just make some best guesses for which games will be slotted where based on teams’ home time zones; these will be updated as actual schedules are unveiled. For the playoffs, time slots are randomized until the actual playoff schedule is announced.

We also account for the NFL’s tiebreaking procedures to a high degree of precision, skipping only some of the most obscure tiebreakers that almost never come up in practice.

Disclaimers

NFL point spreads and over-under lines are notoriously tricky to beat. Although ELWAY may be somewhat sharp, it is difficult to beat the ~4.5 percent cut of each bet that the bookmaker takes. Thus, we do not recommend betting based on ELWAY blindly, if you do so at all. Always shop for the best lines, and combine ELWAY with your overall football knowledge. Discrepancies between point spreads and ELWAY projections may reflect late information since our last update or idiosyncratic factors that our system does not account for.

ELWAY is new in 2025, and there may be some bugs and miswritten code. If you see something that looks weird, please let us know.

Updates

We’ve added this section for changes made in season.

• 10/21/2025. The formula for predicting totals (over/unders) was underdispersed (the range of outcomes was too narrow) relative to Vegas lines; we’ve switched to a formula that is slightly less elegant but validates better empirically. In addition, we’ve added new factors in projecting totals, for example accounting for heat in addition to cold weather (they both reduce totals) and accounting for the tendency of scoring to increase over the course of the season.

• 10/21/2025. We’re now applying a variance-reduction technique to forecasted game scores. Essentially, instead of taking a purely random draw from a normal distribution, we now divide the distribution into 1/x increments, where x is the number of simulations. This means, for example, that if a team is favored by any margin in the deterministic version of our forecast, it will always win the majority of times (excluding ties) in the simulations.

• 11/5/2025. We now account for non-QB trades as well as non-QB injuries. For a team trading away a player, any positive WAR from that player is subtracted from the date of the trade forward, as though he’s had a season-ending injury. The team acquiring the player gets 70 percent of his WAR, which will improve its projection for the rest of the season. The reason we use 70 percent rather than 100 percent is because teams acquiring players mid-season in the NFL are typically contending teams with above-average roster depth, so the new player may be replacing others in the lineup who already have a positive WAR.

This is the Silver Bulletin solo byline debut for Joseph George, our Assistant Sports Analyst. As I hope you can see from this story, Joseph brings a unique blend of scouting/film study and advanced analytics to his NBA analysis. It’s also the informal launch of our NBA coverage for the season. We don’t have an official season preview planned, but you can check out our long-term rankings on which teams have the brightest futures. We’re super excited about the season. And we hope to begin work on RAPTOR 2.0 once our NFL modeling work is fully ready to go.

One more small detail: the photo of Wemby you see above is from Getty Images, a provider of editorial and stock photography. We’re now paying for their services as part of our continual effort to professionalize and upgrade our operations. Silver Bulletin is still in a “slow growth” phase, and that feels very comfortable for us; we’re not looking to conquer the world or pay for fancy office space somewhere. But we are reinvesting in the business, and we’re deeply appreciative for subscribers who help make that possible. —Nate Silver

Subscribe now

If you prefer to ignore the Steve Ballmer-Kawhi Leonard tree company fiasco1, the NBA has been quiet since the Kevin Durant trade. This has been good for us over here at Silver Bulletin — we’ve been knee-deep in work on our NFL models, QBERT and ELWAY — but bad for the NBA news ecosystem.

The most interesting player to follow through this gray period has been Victor Wembanyama, who, in a single summer, became a Shaolin monk, met Snoop Dogg and Daniel Radcliffe, and trained with Aaron Donald, Kevin Garnett, and Hakeem Olajuwon. And then in the Spurs’ preseason debut on Monday, Wemby nearly tallied a triple-double in 16 minutes, admittedly against the Guangzhou Loong Lions.

I, for one, am quite jealous: Wemby is a whole year younger than me, and while I was doing a summer internship, he was getting barked at by KG and casting spells with Harry Potter. But I digress.

I bring up Victor because, despite his phenomenal play, he hasn’t yet been the personality that casual NBA fans tend to flock towards. He’s a bit Kareem-esque: calm, stoic, and while not exactly aloof, certainly not interested in appealing to the masses.

But for what it’s worth, prediction markets are warming up on Victor’s chances for an MVP this season. While I think 11.7 percent is an okay bet — Polymarket had it at 4.4 percent when I originally wrote this, which would have provided a much better value — that figure is skewed by the constraints of the MVP award as a whole. It’s more likely the Spurs miss the playoffs than make the Finals next season — but how much of that is a reflection on his talent rather than the weird limbo the Spurs are in?

Wemby walked into the league a defensive juggernaut

When discussing Wemby, the first place to start is obviously his defense. By my estimation, he’s the best player on that side of the floor in the NBA right now. It’s easy to point to his blocks — the gargantuan numbers he puts up in that category are a massive outlier:

Blocks, however, are a weird stat to evaluate in a vacuum. A large percentage of blocks don’t change possession: a study from the 2003-04 season found only 57 percent were recovered by the defense. The biggest issue with blocks, though, is that they can create poor incentives for players. For example, a player out of position can be rewarded with a box score boost even if at his own team’s detriment. (Hassan Whiteside was a frequent culprit of this, for example.)

Nate’s work for RAPTOR 1.0 didn’t put much emphasis on blocks, treating them as subordinate to overall rim protection, and we’d be surprised if the new version we’re working on is much different. Instead, it’s generally accepted nowadays that deterrence — preventing efficient shots from being taken in the first place — is a better indicator of a good defender. In a more spaced-out game, this concept becomes especially important. Todd Whitehead’s (@CrumpledJumper) mostly-in-jest stat from the 2023-24 season, HELLNAH2 tries to quantify this. When a player plays out of position, he might get a block, but is that really indicative of smart defense? Or is it a reckless gamble? A decent signal of this is seeing how much rim and corner three attempts go down when a player is on the floor:

The best rim protectors stifle both corner three and rim attempts — the two most efficient shots in the game — and as you can see from his presence in the happy green quadrant, Wemby is among the league leaders in both.

Almost everyone already knows Wemby is a stellar defender, though. There’s far more debate about his offensive production, and that’s where he’ll need to improve to compete for the MVP against the NBA’s inevitable forces.

Wemby is creative but unpolished

The most valid critiques of Wembanyama’s ability as a creator thus far have centered around his turnovers. He’s a bold passer — much better than any of us geeks had assessed during his draft cycle — but that creativity can sometimes go a little too far:

It’s important to note that in this chart, I’ve used bad pass turnovers specifically — and not just generalized turnovers, which can include having the ball stolen off a live dribble, shot clock violations, travels, and double dribbles. Regular assist-to-turnover ratio can be an okay indicator of passing efficiency but assist-to-bad-pass ratio is simply a much more direct measure of passing skill. It’s not uncommon for young players to be turnover-prone, but if Victor is going to produce at an MVP level, he’ll have to either fix his tendency to force those weaker passes or learn counters against defenses that are daring him to open up his playbook.

Still, we’d bet on improvement here; it’s hard to overstate how steep the learning curve is for a player in his first 100-200 NBA games (Wemby has 117 in his career). Merely being average is a favorable indicator for such a young player, and he’s been much better than that. Maybe people were asking the wrong questions about Wemby in his pre-draft process. The most common critiques were of his frame and how it would hold up in “high-pressure situations” (i.e., against post-ups and double teams). But, perhaps we were ignoring a few of the natural advantages to being so tall and lanky.

Attention magnets do well in the modern era

We know Victor can straight-up see over his opponents, and paired with his shooting and movement, that should open up a number of interesting cutting opportunities for his teammates. For instance, take this clip from his duel with Nikola Jokic and the Nuggets this past season:

There are a number of crazy things that happen in this possession. It’s not just that Wemby decides to fake an absolutely bonkers three-pointer near the logo. But, in order to understand this, we need to find a good framework to analyze the film.

The three-point revolution has given us a lot of great basketball. Some may disagree, but I think it’s produced a more sophisticated form of the game. Most importantly, I believe the greatest gift of the spacing era has been reframing offensive player value not just in terms of points or assists, but in terms of the attention players command.

For instance, take a look at this anachronistically grainy footage of a Warriors play in Game 2 of the 2019 Finals against the Raptors:

We can see Steph curl around Kevon Looney’s screen, and even though Fred VanVleet gets there, it’s not convincing enough for Kawhi, who lingers for an extra few seconds instead of recognizing Draymond Green’s cut. Against a worse pull-up shooter3, Kawhi might not need to play up. But knowing Curry can get that shot up with decent efficiency, or even take FVV off the dribble, the entire defense breaks down.

In this case, Curry gets credited with an assist and Draymond gets his two points — and that’s fine, because both players made the right play — but fundamentally, the box score isn’t an apt measure of just how high a floor this phenomenon provides for an offense. It also highlights that raw shooting percentages aren’t the only way to measure efficiency — the difficulty distribution of a player’s shot diet matters just as much. For example, Steph Curry’s ability to hit movement threes is far more valuable than P.J. Tucker being an elite catch-and-shoot threat. Even if both shoot 40 percent from three on the same volume, Curry generates advantages because of the attention he draws — this is what we call gravity — while the Tucker types capitalize on their work.

Now, let’s go back to our original play with Wemby. He draws particular attention from the Nuggets because he can shoot from deep, but only a few of their players can meaningfully bother his jump shot at its apex. The Nuggets scramble, even though only Michael Porter Jr. can really affect the shot, and Victor’s teammates recognize that.

Pure shooters are the easiest way to demonstrate this concept, but if you’re hypothesizing that it also applies to other player archetypes, you’d be correct. In the simplest case — a good isolation player beating his defender one-on-one to the point where he draws a double-team — we can also see the effect of gravity:

In this play, which happens right before the other one we analyzed, Wemby starts at the elbow and CP3 comes over to draw a defender switch. In this case, Jokic neither has the length, vertical or speed to bother Victor, and ultimately, Christian Braun has to try and force the ball out of his hands. Jokic doesn’t know who his assignment is anymore and Justin Champagnie relocates to the corner and knocks it down. In this case, Wemby was neither the assister or scorer, but still had the largest influence on the play.

If two players set equally solid screens, their impact can differ. A lob threat, for instance, forces the defense to stay attached, opening driving lanes for the ball-handler. A screener without that threat won’t bend the defense in the same way, and the result is fewer high-quality looks for teammates.

There have been a number of attempts to quantify this phenomenon, and all of them start with the idea that every player exhibits some level of pull, depending on where they are on the floor. We can try to measure this empirically through the placement of defenders in relation to the ball handler and the off-ball player, or by the consistency with which a player is doubled on the ball.

However, the public isn’t given access to full coordinate tracking data, and building a computer vision model to get these details precisely is a super arduous task much better suited for a full scale research team. That’s not to take the wind out of your sails, especially if this framework excites you. The lack of premium tracking data hasn’t stopped analytics geeks from figuring out some interesting solutions to most problems, and this is no exception.

An advanced stat detour can help us figure out Victor’s value

I want to highlight one approach in particular — not just because I admire its elegance — but because it’s aligned with an interesting trend in analytics circles. Krishna Narsu and the BBall Index team have tried an RAPM approach towards shot quality influence.

For context, RAPM (Regularized Adjusted Plus-Minus) is a regression-based model that estimates a player’s impact on team performance by controlling for the other players on the floor. A technique known as ridge regression is used so that low-minute samples aren’t treated with too much significance. Ultimately, RAPM is a way to extract hidden value by ignoring the box score and focusing on an offense’s overall productivity.

RAPM is not at all a recent development in the basketball world — its predecessor, APM, has been around for at least 20 years now — but it’s still somewhat obfuscated from casual fans of the sport. Some of the baseline criticisms of RAPM lie in it “not giving enough context”. This would be a decent critique if we took its results at face value and never bothered with adjustments, but as these approaches have evolved, opponent styles and strengths, teammate roles, and spacing have been factored in. The RAPM approach can be adjusted to understand a number of interesting things about a player, like how much they affect the four factors.

BBall Index, for instance, has adjusted the original RAPM model to isolate off-ball value with the following design:

The key distinction is the target variable being Second Spectrum’s Shot Quality metric and tagging one offensive player with a “shooter” designation. By doing this, they can better identify off-ball value.

So, while RAPM-ification (coined by myself) hasn’t yet hit the mainstream for the average fan, I envision it as the backbone of our understanding of value. It seems poised to become an even bigger deal in the future, especially with the success of RAPM-based models like EPM and RAPTOR.

Just by glancing at the results, it’s clear that while Victor doesn’t have a Jokic–level impact on his teammates, no other “big” does either, and he already belongs firmly in the “solid impact” group. This season should bring even more improvement, especially with the addition of the speedy De’Aaron Fox and herky-jerky Dylan Harper, along with some progression from decent cutters Stephon Castle and Jeremy Sochan.

Wemby has been an … experimental NBA player

Still though, given that Victor warps the geometry of the court with his mere presence — he walked into the NBA as the greatest lob threat ever — it’s hard not to view his teammate shot quality influence relative to all NBA players (not just bigs) as smaller than one would expect. I can posit a few explanations for this. RAPM models are inherently a bit unstable for younger players because of small sample sizes. In addition, few players have been asked to shoulder such an enormous burden early in their career, and as we’ve demonstrated with his passing, Victor does not shy away from experimentation — which can be both good and bad.

Wemby has certainly demonstrated what made him so unique as a prospect. His shot diet has been absolutely mind boggling. In the past two seasons, he’s put up so many more pull-up threes than other big men that it feels like the only thing that remains traditionally “big” about him is his frame:

As someone who’s admittedly been accused of being more interested in experimental NBA players than rote productivity, it’s still hard to look at this and think that Victor is doing anything wrong. He’s probably not. Some may disagree, but if you’re playing next to very few productive teammates, it’s probably fine for your shot diet to veer from serious to audacious.4

And looking at the plot, you might be wondering why such a high pull-up rate could be considered a problem in the first place. After all, Wemby makes them at a decent clip. However, there are valid questions about whether Victor is sacrificing better opportunities for his team:

Even with his presence as a lob threat and shooter, it has yet to translate into consistently good shots for the Spurs. With him in lineups, the Spurs’ rim attempts actually fell off a bit (which is baffling given his physical advantages), and corner threes only improved slightly. In fact, long midrange attempts went up slightly, which is reason to be a little concerned. It may suggest that, despite the added attention, Victor isn’t yet capitalizing on it.

All of this offers an explanation for his underwhelming offensive impact metrics, like teammate shot quality RAPM and O-RAPM (Offensive RAPM). Still though, relative to his age, he’s way, way ahead of schedule, which makes this more of a caution flag than a flashing red one.

So what can we make of Victor? We’re obviously looking at one of the greatest young players the NBA has ever seen — and I think he’s poised for a gigantic leap this year. While the Spurs (who ranked second in our Future of the Franchise Rankings) aren’t yet title contenders, this is the first season they’re fielding a competitive supporting cast alongside him. And if there are a few obvious-ish flaws in his approach, flaws that are typical of developing players, that also means there are also obvious pathways for improvement. If I had to guess, health willing, he’ll be a strong contender for the MVP late into the season. Whether or not he takes home the trophy in a league with Jokic and SGA — probably not — he’s poised to graduate from “best young player” to the All-NBA 1st Team.

If viewing this in the Substack app or on email, we strongly suggest that you check out the web version of this article instead since it has a whole bunch of fancy charts.

For roughly the past six weeks, I’ve been immersed in developing an NFL projection system for Silver Bulletin subscribers, which we’re calling ELWAY1. We’ve analyzed every professional game since literally the start of the American Professional Football Association in 1920. I’ve written a couple thousand lines of code. I’m really excited about it: the FiveThirtyEight NFL models were good, but ELWAY will go a lot further in various dimensions. It’s about 85 percent ready.

But can I be honest? The NFL season starts on Thursday, and there’s obviously just not enough time left to get it published by then no matter how many more 4 AM coding sessions I put in. Plus, for any “rookie” model, it’s always nice to take some time to see how it behaves when presented with fresh, out-of-sample data. So, we’re going to roll out ELWAY in stages:

• The quarterback component of ELWAY — QBERT2 — is pretty cool on its own; really a whole model unto itself. And QBERT is done, so that’s what you’re seeing first. This article introduces QBERT with a lot of historical perspective on quarterbacks.

• As a bonus for paying subscribers, this article also includes our initial 2025 quarterback projections — QBERT predicts future ratings in addition to assessing past performance — plus a spreadsheet of historical QBERT ratings.

• We’ll also create a landing page for QBERT after a week or two, which will be updated weekly.

• The rest of ELWAY — including projected W-L records and playoff odds for each team, plus game-by-game projections for scoring margins, win probabilities and total points scored3 — is coming soon: we’re hoping by two to four weeks into the season. ELWAY projections incorporate QBERT and other factors including roster turnover and detailed data on each game that goes far beyond the final score — much of which are more predictive of future performance than plain ol’ points scored and allowed. Most features of ELWAY will also be for paying subs.

We’re in this for the long term, and we want everything that we do at Silver Bulletin be among the best versions you can find anywhere — even if that sometimes means they take a little longer to get ready. Now, here’s QBERT.

Why the world needs a better QB rating system

Not to get all anthropological on you, but as the most prominent position in the country's favorite sport, NFL quarterbacks occupy a role like almost nothing else in the American psyche. There are 32 starting quarterbacks in the NFL, as compared to 50 governors and 100 senators — but the average American could probably identify more signal-callers than log-rollers. It’s the quarterbacks who are the household names, who carry a city's hopes and dreams on their shoulders in a culture that has fewer and fewer focal points left.

And there's a good reason for that. There's a line of thinking that a QB is just one player on an 11-man offense and 53-man roster, that he gets unearned credit or undeserved blame for whatever his team does. In researching ELWAY, however, we've discovered that the contrarian take is wrong. Quarterbacks, as Vegas oddsmakers long ago figured out, are really damned important. We've been able to “discover” this in various ways, but mostly by observing what happens over the past 75 NFL seasons — our QBERT ratings date back to 1950 — when a QB is injured or joins a new team. We estimate that quarterbacks are responsible for something like one-third of all player value generated in today’s NFL, far more than the share of the money they receive under the salary cap (about 12 percent, subtracting out minimum salaries).

While, for the most part, QBERT is aligned with conventional wisdom — not necessarily a bad thing — we believe it gives credit for elements of QB performance that other systems overlook. The NFL’s traditional passer rating assigns no credit at all for QB rushing plays, even though QBs from Steve Young to Lamar Jackson have completely disrupted opposing defenses that way: ignoring this isn’t much better than neglecting the impact of 3-point shooting in today’s NBA. Passer rating also fails to incorporate basic statistics like sacks, fumbles, and first downs generated (even though first downs are in some ways the backbone of the sport). And it makes no attempt to allocate credit between a QB and his receivers and offensive line — and here’s a more subtle one: QBs are also more efficient when they have better running backs — while QBERT does.

ESPN’s QBR is considerably better — in fact, QBERT ratings are bootstrapped to QBR in various ways. But it nevertheless has some limitations. It probably gets too cute about trying to give credit for performance in high-stakes, high-leverage situations — which, as important as they are, can inherently be random and introduce noise into their ratings. QBERT is more consistent from start to start, and actually predicts QBR as well as QBR itself.4

Passing efficiency has improved dramatically, inflating the ratings for modern QBs

And QBR is only available dating back to 2006, whereas we’ve calculated QBERT ratings for every game since 1950. Once you do that, you’ll realize that you need a system where the greats of the past can compete on a level playing field with today’s stars. Nearly everything that has happened in the NFL — from bigger offensive lines to better sports medicine to officiating rules that increasingly protect the quarterback — has favored perpetual growth in passing efficiency. Other than a few greats like Young, Joe Montana, and Dan Marino, MVP-type seasons from most quarterbacks before 2000 would grade out as merely good today.

However, our adjusted QBERT ratings account for this by calculating a rolling baseline of QB performance that every player’s raw performance is continuously benchmarked against. We also adjust for the quality of opposing defenses faced, home field advantage, and even the weather: cold-weather QBs like Brett Favre faced a significant disadvantage as compared to those who spent their career in domes or in balmy climes.

If you noticed that unadjusted QBERT ratings closely track with traditional passer rating, that’s intentional. One thing we like about NFL passer rating — at least as it existed in the 1980s and 1990s before further inflation in passing statistics made triple-digit ratings common — is that it follows a fairly intuitive scale. A QB rating in the 90s was great, something in the 80s was good, whereas anything in the mid-70s or below meant the team was left wondering if this guy taking every offensive snap was really the franchise QB they hoped for when they drafted him. Adjusted QBERT ratings are designed to follow the same benchmarks:

We were also able to derive replacement level in a rather literal way: it’s defined by what happens when an undrafted rookie makes his first NFL start. Although some of those no-name undrafted starters might later turn out to be Tony Romo or Warren Moon, most of the time they’re pretty bad. Ironically, however, the first start for highly touted rookies — even #1 overall picks — doesn’t necessarily go any better. Quarterbacks face a steep learning curve, and high picks like Ryan Leaf are often given opportunities before they’re ready for prime time, or even after they’ve proven they aren’t.

Because it’s such a difficult position, the league can’t help but to take a sink-or-swim approach with its quarterbacks. Zach Wilson (like Leaf, the #2 overall pick) turned out to be a goat, while Tom Brady (#199) turned into the GOAT. (Yes, Brady is tops in our system.) It’s rare for quarterbacks to hold their starting jobs with ratings below the mid-70s, though high draft picks will get more leash than others.

How QBERT ratings work

Having given you a few teasers about where quarterbacks rank, let me briefly explain more about how QBERT ratings are calculated. If you don’t care about this and want to see the top seasons and careers — well, just scroll down. Or if you really care, there are a lot of footnotes, and we’re working on a methodology page to come soon.

While I like ESPN QBR, it’s something of a black box. However, through regression analysis, we were able to essentially crack the code and explain about 75 percent of the variation in game-by-game QBR based on statistics that are more widely available, in most cases stretching back for decades.5 (We’re also not convinced there’s necessarily much signal in the remaining 25 percent.) These are the basis for QBERT, and here are the factors it considers6:

Primary factors

• Passing and rushing TDs

• Completions

• Interceptions

• Fumbles7

• Passing yards before the catch (a.k.a. Air Yards) — our analysis suggests that credit for yards after the catch should mostly go to receivers8

• Net QB rushing yards (rushing yards gained less sack yardage lost). This is a big factor that’s neglected by other metrics that focus on passing efficiency alone. It accounts for much of the difference between QBERT and the conventional wisdom. Both rushing yardage and sacks are really important, especially when taken together.9

• Passing and rushing first downs10

Secondary factors

• QB rushing attempts (fewer is better, holding other factors constant11)

• Pass pressure: a quarterback gets more QBERT credit for facing a lot of it12

• Running back yards per carry: a QB gets less credit if his running game is effective13

• Game script, as measured by the net score at end of the second and third quarters14

• And clutch wins, defined as any win in overtime, or when the QB’s team trailed entering the fourth quarter. This has a fairly minor impact; QBERT isn’t too “clutch-pilled”.15

This gives us a raw QBERT rating, which is then tweaked for various circumstances to create adjusted QBERT:

• The strength of the opposing defense; each defense basically gets a rolling Elo rating based on the QBERT it has allowed in recent games

• Home field advantage

• Weather: QB performance declines substantially in cold temperatures16 or especially with wind

• And most importantly, a rolling factor that tracks the change in leaguewide QBERT scores over time, so the average always hugs the 80 line closely.17

Just how valuable are the best quarterbacks?

Last thing before we finally address the question implied by the headline. Since QBERT is an efficiency measure — how good the QB is per snap? — a quarterback’s overall value in a game, season or career is determined both by his adjusted QBERT rating relative to the replacement level of 68, and by his number of QB plays, defined as passing attempts + sacks + QB rushing attempts excluding kneels. Since 1950, when QBERT begins, there have been 15,787 total NFL and AFL games, counting the playoffs. A team full of replacement-level players would be very bad: we estimate about 1-15 over a 16-game schedule.18 After subtracting these out, we allocate 30 percent of the remaining wins to quarterbacks in the form of wins above replacement (WAR). As the rushing game has become less prominent, in fact, this percentage has gradually been increasing to around 33 percent today.

How did we get to that 30 percent? It’s sort of a consensus figure. We ran some complicated statistical analyses on what happens when QBERT ratings are added to the team-based measures from ELWAY. These suggest that, if anything, 30 percent is too low and the value might be closer to 40 percent instead. We looked at Vegas point spreads, because QB changes have a similar effect in ELWAY as they do on Vegas lines, and tried to back out some estimates from there. And we checked whether this passed a “smell test”. We estimate that the best QB seasons in NFL history are worth something like 6 to 8 WAR in the regular season19 — so if you replaced Jackson or Mahomes or Josh Allen with a scrub, their teams wouldn’t be terrible but would probably be an underdog to finish over .500.

I’d be more comfortable if you asked me for a range: I’d say that QBs are probably responsible for somewhere in the range of 25 to 40 percent of all marginal value generated by NFL players. If you think I’m wrong, then Vegas disagrees with you, too, and you can make some very profitable bets whenever a star QB gets hurt.

One objection you might raise is that this credits QBs with a huge share of whatever happens on offense. If offense is 45 percent of football, defense is 45 percent and special teams are 10 percent, there’s almost no room left to give credit to receivers, offensive linemen and running backs.

Except, offense is worth more than that. Our analysis for ELWAY suggests there’s considerably more predictability in points scored per game than points allowed. Moreover, offense bleeds into defense — and vice versa — because holding the ball denies the opposing team the opportunity to score and sticks them with worse field position. In ELWAY, we attribute around 62 percent of overall marginal value to offensive players, 36 percent to defensive players and just 2 percent to special teams.20

The top 100 quarterbacks by QBERT

Since I just wrote at length about WAR, let’s start there. To head off this question, the reason why Mahomes isn’t rated higher is simple: he’s in the middle of his career and he’s on track to be among the best ever once he’s finished. Still, modern QBs do have some advantages in WAR: there are more regular season games now, more playoff games, and quarterbacks account for a higher share of offense than they once did.

In career value, Brady is #1 with a bullet, and it’s not particularly close; he has 114.3 lifetime WAR versus 94.8 for Peyton Manning and 86.5 for Drew Brees. And sure, that’s partly due to his longevity — on an efficiency basis, others like Marino and even Dan Fouts are similar, not to mention active QBs like Mahomes and Jackson. But when you win one Super Bowl in 2002 and another in 2021 (and five more in between), you’re the GOAT.

Share

But otherwise, you can see big effects from the era adjustment. You can also detect how much running or at least scrambling ability matters; guys like Steve McNair and Randall Cunningham rank higher here than they probably do in the popular imagination.21 The weather adjustment also makes a bigger difference than you’d think.22 As evaluated by QBERT, Fran Tarkenton, who played in Minnesota before the Metrodome was built, rates higher than Philip Rivers, while the reverse is true in Pro-Football-Reference’s approximate value.

As for Mahomes, if you figure he’s halfway through his career and will double his WAR, he’ll eventually rank #3 all time between Manning and Brees — but Brady will be hard for anyone to catch.23

Here’s the list sorted by QBERT instead, meaning efficiency per play rather than overall value. I’ve set a minimum of 2,000 quarterback plays for this list, although that’s actually not so easy to achieve. NFL quarterbacks have long careers, and only about 150 QBs since 1950 have started at least ten games in at least five distinct NFL or AFL seasons, so you’ll get some distinctly forgettable quarterbacks in the back half of the top 100.

There’s a lot we might say here. Again, the era adjustments make a really big difference. Quarterbacks like Otto Graham, Johnny Unitas and Norm Van Brocklin were actually below average in raw QBERT, meaning basically that a quarterback with those statistics today might struggle to hold down their job. But they were much better than other quarterbacks of their day. Roger Staubach is a more subtle example: he played in a very defense-forward era in the 1970s and he was a good scrambler, so he benefits from a couple of the factors that QBERT considers more prominently than other methods.24

Still, the two biggest outliers are the former 49ers teammates, Young and Montana, who posted stat lines that would fit in perfectly well in the modern game, but decades ahead of time. In unadjusted QBERT, the highest all-time ratings are for Mahomes, Allen and Jackson in that order — but Young ranks fourth in unadjusted QBERT despite predating them by almost three decades.

Next, the top QB seasons by QBERT WAR. These include WAR accumulated in the playoffs; we’ve always considered it a weird blind spot in the NFL and other sports that winning titles is seen as the sine qua non of success, and yet statistics accumulated during post-season games — Brady threw for 13,400 yards in the playoffs! — are all but ignored in historical assessments.

There’s strong correspondence here between what league MVP voters thought at the time and our QBERT assessments. While QBERT doesn’t agree with every MVP pick — it would have chosen Jackson over Allen last year and Brees over Rodgers in 2011 — it also picks up on some forgotten-about seasons like Ken Anderson’s 1981, Matt Ryan’s 2016 and even Brian Sipe’s 1980 that were rewarded with hardware, even if they aren’t fully appreciated today. (Anderson also ranks #17 on the career QBERT WAR list, but isn’t in Canton.)

Once again, though, it’s Brady who tops the list for his 2007 season — yes, that’s the year that the Patriots went 16-0. And, yes, it’s one of the times that Brady lost the Super Bowl: he posted a mediocre 79.8 QBERT against the Giants, in fact. Jackson’s 2024 season ranks 10th all time, although with a 41-to-4 TD-to-INT ratio plus 915 rushing yards, you don’t need an advanced stat like QBERT to see the merits of it even if MVP voters didn’t.

That WAR list is slightly biased toward recent QBs, though, given that there are more regular season and playoff games nowadays. So here are the best seasons ranked by adjusted QBERT, with a minimum of 250 QB plays.

Montana’s 1989, Young’s 1994 and Manning’s 2004 are essentially tied for first as the most efficient seasons of all time, and nobody is going to complain about those. But I don’t like this list as much, with some QBs sneaking onto it with smallish sample sizes: you’ve probably never heard of Babe Parilli before, who had a pretty good year but played just 10 games for the Boston Patriots in 1962. Still, there’s once again strong correspondence with MVP voting.

Tracking and projecting quarterbacks over time

Everything we’ve discussed so far is purely retrospective. But QBERT is also designed to plug into ELWAY, the projection system that we’ll unveil in a few weeks. While I don’t want to spoil everything here — we’re hoping that some of you will sign up for a paid subscription! — let me tease some of the cool things that having evaluated more than 30,000 quarterback starts since 1950 will allow us to do.

Let’s take a great but polarizing quarterback as an example: Rodgers. Here is his forecasted QBERT rating — his prior — for each of his starts since 2008, when he finally supplanted Favre on the Packers. We’re still working out the kinks in how we might visualize this data, but this should get the basic idea across:

Rodgers begins with an expected QBERT of just 69.1 in his first start. But that’s nothing against him because pretty much every quarterback starts out with a mediocre rating. These initial ratings are based on a player’s college statistics — yes, college QBERTs are moderately predictive of NFL QBERTs, provided you adjust them for strength of schedule. And Rodgers’s stats at Cal were good but not great. But great numbers would only help a little bit. Barring some weird cases where a QB took a significant number of snaps as a backup before starting a game25, the players with the best college QBERT ratings (the system really likes Jayden Daniels’ NCAA numbers, for instance) still only project to a rating of around 70 in their first start in the league.

However, QBERT also expects very rapid improvement over roughly a QBs first 20 starts — enough that rookie QBs are typically much better by the end of the year than at the start — provided that the player continues to hold down the starting job.

That’s a vital qualifier, because it’s extremely costly to throw away a season with a sub-average quarterback. So the mere fact that a QB continues to start provides a clue to his trajectory; there is a considerable amount of survivorship bias that you have to navigate around when projecting quarterbacks.

The main exception to this are high draft picks like Leaf and Wilson, for whom two or three seasons might be sacrificed to rationalize the draft pick. For this reason, in fact, draft position isn’t used in QBERT. Empirically, undrafted rookies like Romo start out with almost the same QBERT as first-round picks like Rodgers because in order for them to be granted a start, they have to have shown some promise in practice.26 If every draft pick were guaranteed a fixed number of starts, there would be a stronger correlation with draft slot, but they aren’t.27

After a few seasons, the prior based on a college stats rolls out of the system completely, and a player has to exhibit the improvement that QBERT expects out of a young quarterback. Rodgers did that, posting an adjusted QBERT of 88.2 in his first full season and then continuing to improve until logging a gaudy 108.6 rating in his first MVP season in 2011. While actual QBERT can be noisy, expected QBERT tends to be smoother as the system bakes in an aging curve that also accounts for how many games a quarterback has started recently.

By mid-career, QBERT is more agnostic about a player’s expected trajectory, and you can see Rodgers’ numbers jumping around with peaks corresponding nicely with his other MVP seasons (in 2014, 2020 and 2021). Often, gaining more experience is enough to offset expected aging into a quarterback’s mid-thirties — it’s such a difficult position that you can never really have enough reps — but this isn’t always the case. Rodgers’s late-career renaissance — he won his last MVP at age 38 — was a little bit of a surprise to QBERT. QBs generally age well, but that’s later than you would ordinarily expect.

There will be more about this process on the methodology page. It’s a bit involved. QBs are regressed somewhat toward a long-term mean at the start of a fresh season: sometimes the opportunity to look at film and design a new scheme or tinker with the personnel helps. So while Baker Mayfield arguably had a better season than Mahomes last year (as discussed in SBSQ #23), Mahomes will start out with a considerably higher QBERT projection in Week 1 for 2025. The system is also punitive to players who miss starts, especially if they’re older. You can see that Rodgers was expected to decline from above-average to merely average after missing nearly all of the 2023 season — and that’s pretty much what the Jets got out of him last season. So QBERT isn’t expecting any miracles out of Rodgers’s new team, the Steelers.

Initial QBERT projections for 2025

Here’s how the system ranks every quarterback currently on an NFL roster if they were to start in Week 1.28 I’ve also listed their QBERT ratings over the past three seasons for reference.

Read more

Nate Silver: This is Part II of II of our NBA Future of the Franchise rankings. And it’s the fun part — the top half of the league1 — though we’re still expecting some pushback from area codes such as 617 and 213.

There’s almost 6,000 words of analysis ahead, so I’ll just recap the rules briefly. The sole criterion for these rankings is a team’s expected number of championships over the next 10 NBA seasons (2025-26 through 2034-35). We hope these rankings are well-informed and analytically-minded, but ultimately they’re subjective opinions. The voters are me, Silver Bulletin Assistant Sports Analyst Joseph George, and Jeremias Engelmann, the developer of Real Plus-Minus and a former analyst for the Mavericks and Suns, who writes at the newsletter 5x5.

One more quick note if you’re reading this in email. The fancy headers I used for each team in Part I didn’t render well in most email clients. There wasn’t any text or data missing, but there was a lot of awkward white space. This time, I’ve posted them as images, which should help. If there are still problems, it’s often helpful to click over to the web version.

Joseph George: The 76ers are in one of the NBA’s more unique situations — they have an abundance of arguably misfit assets in the form of young players (Jared McCain, VJ Edgecombe, Tyrese Maxey), a chronically injured league MVP (Joel Embiid), and a poor contract I can’t exactly blame them for (Paul George). There’s an avenue for them to be consistent Eastern Conference contenders through their core and an Embiid resurgence. Or maybe a Maxey trade can materialize into a wing/forward that would pair better with Edgecombe and McCain (Franz Wagner comes to mind).

The biggest question is about their former league MVP. It’s unlikely Embiid is going to return to that 2024 pre-injury form, when he looked like the best player in the world. But can he simply be consistently positive? I want to say yes, even if he has to sit half the games in the regular season to get there. It’s still possible he can create out of the low block, which combined with McCain’s shotmaking, could be a potent mix as the team tries to climb out of the East next season.

I have Philly lower than say, Orlando or Atlanta, because if George doesn’t rebound next season, and Embiid has (yet) another spate of injuries, there’s just not much flexibility the team has that doesn’t involve dumping most of their assets to shed these contracts. Daryl Morey has pulled rabbits out of his hat before, but it’d be a particularly tricky situation that could hamper the team for years even if Edgecombe and McCain live up to the expectations.

Nate: I thought the Sixers were just about the most difficult team to rank, so I was happy to see the three of us land in the same vicinity. Without the lottery luck and Edgecombe, the situation might have tilted toward the distressed. Now the glass-half-full case is easier to see.

So much comes down to Joel. He’s become so distrusted by NBA nerds that he’s now just #84 on the Ringer’s Top 100 list, even though he was the MVP in 2022-23 and scoring with unprecedented efficiency in 2023-24. I said in February that I thought this was an “overcorrection”.2 But last year was a huge setback. Not just his minimal 574 minutes played, but also because Embiid was notably shy around the rim, dunking just five times.

A few years ago, Embiid went from an already good offensive player to a superlative one by adding an effective midrange game. But can he be as effective from the triple threat position or draw as many fouls if his efficiency around the rim is merely good?

Joseph: The Celtics have had an interesting summer, gutting the starting five that took them to the promised land just a year ago. The moves, which look ambiguous on the surface, are strategically positive — as one would expect from Brad Stevens — in that they get the Celtics below the second apron. For now, it looks like the Celtics are entering their name into what should be an epic tankfest next season and then likely looking to make a Jaylen Brown trade.

Nate: I’m bearish on the Celtics this season. Their 43.5 projected win total already feels high for a team that’s down three-and-a-half key rotation pieces — Jayson Tatum, Jrue Holiday, Kristaps Porzingis and Al Horford (he’s the half) — and up only Anfernee Simons, whom advanced metrics grade out as a league-average player. And that’s before considering the possibility that they make further offloading moves; they own their own pick, so they have an incentive to tank. I’m not as confident as Joseph that a Brown trade is in the works. But if they could get significant positive assets for a guy who's due to make $65 million a year by the end of his contract but has only made one All-NBA team, that would help their ranking.

Joseph: Their fortunes were upended fairly quickly — all of this feels a little preemptive, even if I’m mostly used to the NBA’s rapid pace at this point. If not for Tatum’s injury they would probably not be doing much retooling right now, nor would they be ranked this low. Unlike the teams that surround them, however, they have a legitimate shot through next year’s draft. If they play their cards right and win on the margins consistently, this team is set up for success long term.

Nate: It did unravel quickly. There is … I’m not going to use the term “denial” because maybe you’re more at peace with the situation if you’re just one year removed from your favorite franchise’s 18th championship. But if you’re a regular listener of the Bill Simmons podcast, for instance, he’s been surprisingly low-key about the turn of misfortune for a team that, in the span of a single series against the Knicks, went from potential back-to-back champions to the messy middle.

Yes, they have outs if the cards break right, and they’re run by a front office that always plays the game well. But there’s no longer nearly so clear a thru-line to their next title. I've always thought of the Stevens-era Celtics as more of an ensemble cast, sort of the 2004 Pistons on steroids, rather than buying into Tatum as a super-duper star. But I'm not sure whether that's bullish or bearish for their future. Various members of the orchestra like Jrue were going to be subject to attrition one way or another through age or the apron; he and Porzingis were ghosts of themselves in the playoffs anyway.

You could even talk yourself into saying that getting a head start on Version 2 or 3 or whatever Stevens build number we’re on — inevitably a more Tatum-centric version, so they’d better hope he eventually gets back to at least 95 percent of full strength — is a blessing in disguise. But they had a lot of championship equity last year and were supposed to have a lot this year, and now that’s been zeroed out.

Nate: Well, I’m a big outlier here relative to Jeremias and Joseph. And the difference is mostly about the long term. As I wrote about in Part I, I’ll put a premium on “brand intangibles” when considering the second half of the 10-year window that FotF covers. In the Player Empowerment Era, having superstars willing to move heaven and earth to play for your franchise is a significant advantage, and no franchise has a better record of attracting superstar talent than the Lakers.

Speaking of which, they already have a young superstar who was the best player on an NBA Finals team just a summer ago. While there’s a bearish case to make on Luka Doncic, especially once his supermax kicks in, the base case is that the Lakers start out with a high floor for the next half-dozen seasons. Getting a superstar in the prime of his career is the hardest thing to do in the league, and by hook or by crook, they’ve done it again. They also have a clean-ish cap sheet to build around Luka.

Oh, and they have LeBron James, who has sort of passive-aggressively made it be known that he’s running out of patience with the situation. I think my ranking is pretty robust to most LeBron scenarios. If they could get a real return for him, that would be good on net for their FotF ranking.

But if he stays … this almost never happens since they’re a notorious favorite of recreational gamblers, but might the Vegas line of 47.5 wins for the Lakers actually be too low?

Read more

Nate Silver: June 22nd was one of the craziest days in NBA history. Kevin Durant was traded to the Rockets. The Oklahoma City Thunder won their first championship since moving to The Big Friendly.1 And Tyrese Haliburton tore his Achilles tendon in the Pacers’ Game 7 defeat to OKC, not undermining what they’d accomplished but leaving their future uncertain.

Perhaps it was inevitable that the free agent period that followed would be an anticlimax.

Still, the league looks vastly different than when I last ranked the 30 teams after the trade deadline in February. Jayson Tatum is probably out for all of next season, and Jrue Holiday and Kristaps Porzingis are no longer members of the Celtics at all. Damian Lillard will miss the year too — and he’s teamless anyway, having been waived and stretched by the Bucks. But the Knicks reached their first conference finals in 30 years. And the Mavericks were redeemed from the most infamous trade in NBA history by landing the #1 pick and Cooper Flagg.

The goal of these, the Future of the Franchise rankings, is deceptively simple: to rank the teams in order of their projected number of championships over the next ten NBA seasons, from 2025-26 through 2034-35. Because it’s a hard task — how to balance the short term against the long run, franchise “intangibles” against the talent on the roster, and which teams have realistic timelines versus those that are likely to run into a Thunder-shaped brick wall — this time I’ve brought some friends to help.

Joseph George is the new Silver Bulletin Assistant Sports Analyst, and he specializes in the NBA as a consultant for the G League’s Mexico City Capitanes and the author of the newsletter The Zone Master.2 And Jeremias Engelmann, previously at ESPN, is the developer of Real Plus-Minus and a former analyst for the Dallas Mavericks and Phoenix Suns. His current site is xRAPM.com and he writes on Substack at the outstanding newsletter 5x5.

There’s a lot of analysis below, so I’ll cut the pregame show short in a few moments. Teams are ordered based on a simple average of our ratings, with ties broken by the median. We’ll publish the bottom half of the league (teams #16 through #30) this week and the top half next week. (Hopefully there’s no Giannis or LeBron trade over the weekend that forces us to reconfigure.) And pay careful attention to the author of each comment: we have our share of disagreements, and in these cases I assigned more than one of us to a team to articulate the glass-half-full and half-empty view.

One final point before we get started is that the singular emphasis on championship equity helps to makes for a more tractable set of rankings: if Silver Bulletin is still around in 2035, we’ll be able to evaluate them based on who hangs banners over the next ten seasons. But it’s also arguably a bit myopic, rewarding high-variance, all-in approaches over more patient team-building. You could claim that the line has become blurrier: last year’s Pacers, in particular, are a paradigmatic example of a franchise that seemed happy to settle for being pretty good, and yet they almost won a title anyway. But at least for me — I won’t speak for Jeremias and Joseph — this made a big difference in some cases.

Jeremias Engelmann: If the past two decades are any indication, Sacramento has tough odds to overcome: The Kings have made the playoffs just once since the 2005-06 season, when they lost in the first round in 2023.

The issue is that their 17 losing seasons in 19 years aren't just a product of bad luck. Instead, they stem primarily from a suboptimal decision-making process, leading to such blunders as drafting Marvin Bagley III (!!!) over Luka Doncic. Their biggest problem now, though, is the refusal to start a rebuild, repeatedly opting to chase the elusive No. 8 seed instead — one of the most "effective" ways of never becoming a true contender.

The result is a roster void of superstar talent. Instead, the Kings have several players of the type I’ve dubbed "quagmires" — players who move any team closer to an undesirable .500 win percentage by coupling high usage with very ordinary impact.

Jeremias: In the past three seasons, the Suns — for whom I worked briefly in 2013-14 — infamously went all-in and busted. Now they’ve traded Durant at a bargain price and are expected to buy out Bradley Beal at an exorbitant one. Meanwhile, Vegas has their over/under at 30.5 wins for next season, and they don't control their first-round pick until 2032.

They did get former No. 2 overall pick Jalen Green in the KD trade, but metrics such as xRAPM rate him as a bottom-rung defender. And their remaining star, Devin Booker, is a topnotch scorer but another defensive sieve — who happens to play the same position as Green.

Of course, the Suns’ big-picture problem is not the lackluster roster per se, but the extremely short-range thinking and poor process that put them in this position. That was summed up by Suns owner Mat Ishbia in a March interview with ESPN in which he expressed his disdain for the patient approach of other franchises: “I want to try to see the game today. I want us to win today, and we're going to try.”

Nate: Just so you get used to the rhythm here, this is where Joseph and I might step in with a rebuttal. But as you can see, we have very little overall disagreement on the bottom three teams in the league. (Spoiler alert for Part II — there wasn’t much disagreement on the top three teams, either; the middle is tougher.) I hadn’t seen that Ishbia quote, but as big defender of state schools in general and Michigan State University in particular — I grew up in East Lansing and my dad taught there — I’m a little embarrassed to see a Sparty alum be so proud of failing the marshmallow test.

Jeremias: Speaking of questionable decision-making, the Pelicans' trade that gifted the Hawks a “superfirst” — the better of New Orleans’ and Milwaukee’s unprotected 2026 picks — was, for most analysts, the most laughable development of draft night.

That's not the type of feedback you're hoping for when you’ve just hired a new front office. But herein lies the crux: New president of basketball operations Joe Dumars and GM Troy Weaver appear to be in over their heads, based on their track record. Weaver's aimless tenure in Detroit, for example, was immediately followed by the Pistons making a historically great leap, from worst in the NBA to solid playoff team.

It will probably take at least two or three years before Pelicans ownership sees the light and replaces them, but the damage will be done.

Nate: I had the Pelicans ranked a respectable 19th in February, and yet have them even lower than Jeremias this time around. I think the lesson is that teams that have a lot of “scrap metal value” — where if you melted down the spare parts, you might find something useful — won’t be in a position to maximize on that without competent management.

Joseph George: The Wizards’ young core as a whole, frankly, isn’t good — Alexandre Sarr could be a fine, versatile defender, but struggled as a finisher, rebounder, and despite his tendency to spray threes, isn’t a particularly efficient shooter either. I would’ve liked to see the Wizards draft someone with a little more defensive versatility this year, but they opted for more shot creation in the form of Tre Johnson. Their asset situation is fine, but I need to see them do more on the development front than just draft and hold prospects.

Nate: Despite being connected to politics, I’ve never had any desire to live in DC. It has its charms — I’m a sucker for French city planning and Ethiopian food — but I find it too much of an industry town and too formal. However, here’s the real problem: it’s possibly the worst sports town in America, with a series of problematically branded franchises that failed to bring home a championship from when the Redskins last won the Super Bowl in 1992 until the Caps finally took home the Stanley Cup more than a quarter-century later in 2018. So I’ll count myself as surprised that I’m writing in defense of Washington, but my #23 overall ranking for the Wizards was higher than the rest of the group.

But I wound up organizing my list into tiers, and the Wizards were at the bottom of my second-lowest tier, teams that I consider to be fully committed to the rebuilding process. (Conversely, my bottom six franchises are all teams I consider “distressed” or underwater in some fashion, with negative-value assets, inept management, or both.) I’m not sure there’s a player on the roster I’d count on to be the best player on the next good Wizards team. But they have a lot of lottery picks, plus a competent management team with a lineage in the budding OKC dynasty. I wouldn’t recommend Wizards Basketball: It Could Be Worse! as a marketing slogan, but at least they’re not still paying Bradley Beal — in fact, they got a pretty good haul for him.

Read more

A couple of quick announcements before today’s main event.

First, we have a new hire to announce! Joseph George is now the Assistant Sports Analyst at Silver Bulletin. Joseph writes the NBA-focused newsletter The Zone Master, and has been an analyst for the Mexico City Capitanes of the NBA G League. He officially begins on July 1. Joseph beat out many other extremely talented candidates, and I’m grateful to him and to everyone who applied.1

Second, I’m going to pick a morning next week for a ⚡ lightning round edition⚡ of Silver Bulletin Subscriber Questions. Paid subscribers can submit questions in the comments of SBSQ #21. As usual, any questions in the typical purview of the newsletter are fair game: politics, sports, poker, the newsletter business, AI, New York City, you name it. To avoid my usual tendency to go super long in responses, I’ll put myself on the clock, getting through as many questions as possible in ~2-3 hours.

Subscribe now

Three seasons ago, the Oklahoma City Thunder, rebuilding from a Kevin Durant and Russell Westbrook-led era that peaked with a quick exit in the NBA Finals in 2012, went 24-58. None of their top eight players was more than 23 years old. They drew the right lottery balls for the 2nd pick in the NBA Draft, snagging Chet Holmgren, along with Jalen Williams later in the first round.

Two years ago, the Thunder were 40-42 — but actually a little pluckier than that, finishing with a positive point differential even as Holmgren was hurt in the preseason and took a gap year. Shai Gilgeous-Alexander was suddenly a star, averaging 31.4 points per game. Still, nobody was prepared to anoint the Thunder a contender: they entered the 2023-24 season with +10000 odds (100:1 against) of winning the NBA Finals. Instead, with Holmgren making his debut and Williams blossoming, they finished with the #1 seed in the Western Conference at 57-25 before losing to Dallas in the Western Conference Semifinals.

And then this year, the Thunder were one of the best teams in NBA history, with a 68-14 record, the best point differential ever, an MVP Award for SGA, and then on Monday night, the first title in Thunder history.2

People are going to debate that clause — one of the best teams in NBA history. But this was an incredibly quick rise, too quick for some fans to appreciate. And it probably wasn’t any sort of fluke. The NBA has a long season — maybe too long considering the increasing number of injuries to players in their primes — and it’s a low-variance sport. (The better team usually wins when each team gets ~100 possessions per game.) So the numbers mostly speak for themselves over the course of the 105 regular-season-plus-playoff games that the Thunder played.

In fact, this year’s Thunder were one of just 11 members of what I’ll call the 65-and-10 club: teams to win at least 65 regular season games with a double-digit scoring differential. Nine of those 11 teams went on to win the title, the exceptions being the 2015-16 Spurs — who ironically lost to the Thunder in the Western Conference Semifinals — and in the same season, the infamous 73-9 Warriors, who blew a 3-1 lead to LeBron’s Cavs and were so chafed about it that they went out and acquired Durant.

True, the Thunder endured seven losses in the post-season, some of which were hard to stomach — a choke job in Game 1 against Denver in the Western Conference Semis, and then a blowout loss to the Pacers in a closeout Game 6 in which they trailed by 30 points after the third quarter. But Michael Jordan’s 1992 Bulls also lost seven games en route to their second title. The Thunder won whenever it mattered, losing back-to-back games just twice in the regular season and zero times in the playoffs.

It’s also true that OKC had its share of injury luck: the defending champions and preseason title favorites, the Boston Celtics, lost to the Knicks in the Eastern Conference Semifinals after Jayson Tatum tore his Achilles3, and then Tyrese Haliburton also gruesomely tore his Achilles in the first quarter of Game 7 on Monday night. None of this was the Thunder’s fault, and for that matter, they weren’t fully healthy themselves, with Holmgren limited to 32 regular season games.

Still, it’s enough to create a perception that the Thunder won the championship for 70 cents on the dollar, perhaps somewhat like the Lakers’ title in the NBA bubble in 2020. On his podcast after the game on Monday night, Bill Simmons said within the first minute that “I think this might be first NBA playoffs that comes and goes where the team I’m going to remember isn’t the team that won the title.”

Not that Simmons is a Thunder hater exactly.4 Still, Haliburton’s injury, plus the trade of Durant to the Houston Rockets on Monday afternoon5, abutted into their victory lap and might provide the excuse to treat OKC’s championship as just one item on a list of bullet points in the never-ending NBA news cycle. In some ways, in fact, the NBA flywheel feels more and more like the political news cycle where it’s hard to tell what matters and what doesn’t. But titles matter, or at least they ought to. Perhaps the Rockets went from something like a 5 percent chance to a 12 percent chance of winning next year’s championship, for instance, after acquiring KD. But the Thunder just won this year’s title with 100 percent certainty.

And the Thunder do have their haters, not that they should care. They are not only one of the better NBA champions, they’re also the second-youngest. And they have arguably the best stash of draft assets in the league6. In an era where league rules are incredibly punitive to sustaining even homegrown success, they are nevertheless set up as well as any franchise ever to reel off a string of championships. But I have a theory for why the Thunder aren’t quite getting the love that they deserve.

The NBA is a “prove it” league

I have no particular connection to Oklahoma City; in fact, Oklahoma is one of just four states that I’ve never been to.7 But I like making winning sports bets, and aware of track record of teams that perform this strongly in the regular season, I placed various futures wagers on OKC over the course of the year, first at about the half-way point of the regular season, then again at the start of the playoffs, then again after the Celtics fell behind 3-1 to the Knicks.

I’m also aware that playoff inexperience can be a detriment — in fact, this was incorporated into the projections that we ran at FiveThirtyEight. So I expected a few bumps in the road in the playoffs of exactly the sort that OKC endured. I just thought the signs of elite performance were enough to outweigh those concerns. You might win 64 games almost by accident as a merely very good team, as this year’s Cavs did. But you don’t win 68 and have a +12.8 point differential unless you’re all-time elite. There are just too many ways to lose regular-season games — on the tail end of back-to-backs, or when you shoot 7-41 one night from three. To be that many standard deviations above the mean, you really have something going.

I didn’t bet the Thunder at the start of the season, though maybe I should have. They entered the season at +675 to win it all, meaning a $100 wager would have returned a $675 profit — about the same price as the Knicks! But one of the themes I’ve discussed in the previous two installments of what’s now become an annual Silver Bulletin tradition, a celebration of the new NBA champion — see my piece on the Nuggets here from 2023 and the Celtics here from 2024 — is that NBA fans probably go too far in defaulting to the familiar.

If you look at past preseason odds — Basketball Reference has them dating back to 1985 — all but 2 of the 17 previous champions to enter the regular season with +250 title odds or lower had recently won a championship. Even for franchises that later proved to be dynasties, however, the betting public was initially skeptical. The 2015 Warriors went into their championship season at +2800. Jordan’s Bulls were +700 in 1991, slightly worse than this year’s Thunder.

Of course, betting on newcomers has been a more winning strategy lately now that the NBA has had six distinct champions in six seasons (although the 2022 Warriors count as a carryover from 2018 for “recent repeater” status). But NBA history isn’t that long, and the structure of the league is changing, making sustained success harder to achieve. If you’re building a model, it’s wise to give some extra championship equity to veteran teams that may pace themselves in the regular season. But fans and bettors may be overindexing to the presence of a small handful of dynasties in the fossil record, like the Warriors and the Bulls, who may have been subtly lucky in their own ways8 and who played in a less competitive league under conditions that no longer exist. If you started from more of a clean slate — the Thunder were already very good last year, they were very young, and they added two key rotation pieces in Isaiah Hartenstein and Alex Caruso — they should probably at least have been co-favorites with the Celtics. Certainly not relegated to second-tier status with the likes of the Knicks.

To be fair, though, the Thunder are somewhat literally off the charts. Weighted by regular-season minutes played, the team was just 24.8 years old this season. (Historically, NBA players peak at about age 27, but the timelines may be extending due to modern sports medicine.) Only the 1976-77 Portland Trail Blazers were a younger champion (24.5 years old), but they went just 49-33 in a literal dark era for the league9. Nobody else is particularly close. No team ever has been this good, this young, this fast.

But if you scroll through that chart, you can see some patterns emerging. For the past 11 seasons, dating back to the Warriors’ first title of the Steph Era in 2015, the average NBA champion has been 27.6 years old. By contrast, in the 30 seasons before that, from 1985 through 2014, the average champion was 28.9. It’s not a huge shift — a difference of just 15 months — but it matters at the margin that NBA teams (and NBA bettors) operate at.

The increasing intensity of the regular season, and the increasing strictness of the league’s various salary caps and aprons, render it all but a necessity to have at least some prime contributors who are either on their rookie-scale contracts (like Williams and Holmgren) or at least on their initial rookie extensions (like SGA). Youth and depth are in — the success of the Pacers will also contribute to that trend — and the era of the veteran “superteam” is probably out, as evidenced by the cheap price that the Rockets paid for KD after the king’s ransom forsaken by the Suns for him.

The Thunder skipped their rite of passage

It might have helped the Thunder perception-wise if they’d faced either Steph’s Warriors (assuming he was healthy) or LeBron’s Lakers in the Western Conference Finals. Not that these teams were actually any better than the Timberwolves, whom the Thunder dispatched in five games. But like in almost no other sport, NBA teams are expected to endure a rite of passage, perhaps initially being sacrifices to elder dynasties before earning respect for themselves. Jordan’s Bulls lost in the opening round three times, then the semifinals to the Pistons in 1988, then the Eastern Conference Finals twice (also to Detroit) before eventually reaching the promised land. The Celtics had several near-misses before their title last season. Even LeBron’s Miami Heat needed two chances, losing in an upset to the Dallas Mavericks in 2011 before winning the title the next year.

The major exception to this is the Warriors: they went 23-43 in the NBA’s lockout-shortened 2011-12 season (which equates to 29 wins per 82 regular season games) but then were dominant by 2014-15, their first season under Steve Kerr, winning 67 regular season games and the Finals. Their trajectory isn’t quite so linear as for the Thunder, but it’s close.

And here, it is worth acknowledging that the Warriors were more of a viral sensation than OKC was. There are a lot of intangible factors behind this. Steph is one of the most beloved players ever, while SGA and the rest of Thunder are kind of square. (Even now, Steph is still #1 in jersey sales whereas Gilgeous-Alexander is 11th). They play in a small market for a franchise that’s fully rebranded from its bitter end as the Seattle SuperSonics. They’re sometimes accused of tanking, but that isn’t really fair: the Thunder only had two truly bad seasons, and the only real lottery luck they received was with the Holmgren pick; everyone else way overperformed their draft slot (or not being drafted at all, in the case of Lu Dort). In all of this, there may also be some element of jealousy verging on fear: the potential dominance of the Thunder could wreck every other franchise’s championship hopes.

But also, the world has changed, even from a decade ago. Sports, and particularly the NBA with its dynastic franchises, are a place we go to look for familiarity when there’s so little of it in other parts of our lives. Tom Brady won three Super Bowl rings under George W. Bush, two under Barack Obama, one in Trump’s first term, and his final one in Tampa under Joe Biden. There’s nothing like that now. The closest replacements for him in the American sports landscape are Steph and LeBron — #1 and #2 in jersey sales — but they’re more like the bands still playing to sold-out arenas even if they’re clearly past their primes, with fans mostly looking for renditions of their old hits rather than whatever new material they’ve come up with. Meanwhile, the box office, another source of escape, is completely dominated by movie mega-franchises. Even the rare exceptions, like Barbie in 2023 or A Complete Unknown last year, are often awash in nostalgia.

If Hollywood had scripted it, this year’s Finals would have had a more sympathetic champion: probably the Pacers (a redux of Hoosiers?) or even the Knicks. That would have set the Thunder up for a redemption arc next season. In time, NBA fans will come to fully appreciate everything that OKC accomplished; familiarity breeds contempt, but also respect. Instead, they’ll enter next season still with a chip on their shoulders — but a ‘chip already in the bag.

While I’ve become a huge New York Knicks fan, I never got over my skepticism about the team’s chance of reaching the NBA Finals — let alone actually winning their first title since 1973. I thought the Pistons would be a tough out, and they were. I did like Knicks’ odds against the Celtics — and even placed some small wagers along those lines — but that’s the whole point of odds. (If you’re getting 6.6-to-1, you only have to be right 14 percent of the time to turn a profit.) I wasn’t quite sure what to make of their matchup against the Pacers. But between losing the tactical battle — the Pacers controlled the pace — and a devastating collapse in Game 1, the series immediately became uphill. And if the Knicks had survived Indiana, they were probably a sitting duck for the Oklahoma City Thunder.

Overall, the Knicks’ performance has been in line with reasonable expectations, with an emphasis on the word “reasonable”. Talent-wise, they’re probably something like the 4th or 5th best team in the NBA. (I’ll attempt to quantify this in a moment.) You’d expect such a team to be one of the four conference finalists roughly once every other season. That’s exactly what the Knicks did, losing to Indiana in the conference semifinals in Game 7 last year amidst a series of injuries, but then advancing to the Eastern Conference Finals (but again losing to Indy) this year.

I suspect when Knicks fans look back on this era in 10 years, they’ll remember it warmly. But unless they cash in over the next couple of seasons, a decade from now is about when the Knicks might be competitive again. They’ve plundered their draft capital to build a good team. Perhaps a very good team, even. But certainly not a great one.

The Knicks made an unusual move. But that doesn’t make it wrong.

On Tuesday, the Knicks fired their head coach, Tom Thibodeau, the only one among the 13 Knicks coaches since Jeff Van Gundy abruptly resigned in 2002 to post a winning record in the playoffs. Only 3 of the other 12 even coached a single playoff game. While you can cherry-pick some optimistic exceptions — Bill Simmons cited the Warriors replacing Mark Jackson with Steve Kerr in 2014 — it’s pretty unusual to dismiss a coach amid a rising tide. Particularly after he beats the fucking Celtics.

And yet, the move probably makes sense. If James Dolan had texted me and asked for my opinion, I’d have demurred. I think it’s a close enough call that my lack of inside knowledge about Thibs’ relationships with the players and the Knicks’ offseason plans would render my opinion more noise than signal. But if there had been some sort of incentive for giving my most honest answer — courtside seats next to Spike? — I’d have been a reluctant member of Team Fire Thibs.

Outside of HR violations, there are basically three reasons to fire a coach. One is for tactical misexecution, the nominal purpose of the coach’s job. The second is for the failure to adequately develop players or realize their full talent. And the third is just to mix things up: to introduce variance.

Often, this last reason is just a lame excuse: you can’t fire the owner, the current CBA makes it hard to fire the players, and so a general manager sometimes uses his first-mover advantage to fire a coach so that he doesn’t get fired himself. (I know most of you aren’t hockey fans, but see, for instance, this year’s New York Rangers.) But at other times, it can be a reasonable tiebreaker, particularly in the NBA, where it’s really hard to win a title unless you have OKC-level talent. And I’d argue this is one of those times for the Knicks. However, we’re getting ahead of ourselves.

Reason #1 to fire a coach: tactics

I’m not really an X’s-and-O’s type of NBA fan, but I know the Knicks well enough to know that outside of some minor stuff — minor stuff like coaches’ challenge strategy and fouling when up 3 points that happened to be highly pertinent in Game 1 against Indiana — the bill of tactical complaints about Thibs is basically two items long.

The first complaint is that Thibs overtaxes his starters. In the past regular season, Josh Hart ranked first in the league at 37.6 minutes per game, Mikal Bridges was 3rd (37.0), OG Anunoby was 5th (36.6), Jalen Brunson was 14th (35.4), and Karl-Anthony Towns was 20th (35.0). Bridges and Hart are famously durable players, but clearly, Thibs is bucking a leaguewide trend toward playing your starters less often.

It’s not entirely straightforward to determine whether this is a mistake. In principle, it’s an empirical question. It certainly presents some player development challenges: if young Knicks like Tyler Kolek or Pacôme Dadiet are any good, we’d have no way to know because they played so rarely. And one probably ought to have a wisdom-of-crowds prior that deviating from the consensus strategy is wrong more often than not. And in this particular case, it doesn’t help Thibs that this exact 5-man lineup that was played so heavily — Brunson-Bridges-Anunoby-Hart-Towns — was not especially effective, to the point where he abandoned it against Indy.1

And yet, the Knicks are thin, and there’s a certain caveman logic to “play good players more often.” I’d bet against this, but if the stathead conventional wisdom shifts over the next decade to thinking players are too coddled — elite starting pitchers are removed too early, NFL teams should ride All-Pro running backs instead of sticking them in platoons, and superstar NBA players should average high-30s MPGs instead of low-to-mid 30s — it won’t entirely surprise me. So this is a strike against Thibs, but not a decisive one.

The other main line of complaint is that the Knicks’ offense is stale and lacks a higher gear. And here I’m less sympathetic to Thibodeau. The Knicks ranked 5th in the league in offensive efficiency this year, which is perfectly fine if you also have an elite defense — but the Knicks don’t, ranking tied for 13th.

As I discussed in my previous Knicks piece, the team can be astonishingly effective with their backs to the wall late in the shot clock, with Brunson often pulling a rabbit out of his hat. But these possessions are only effective on a relative basis: the best late-possession offenses (last year, the Knicks and the Suns) are still generating fairly putrid offensive efficiency as compared to getting a shot off early, or at least giving yourself the optionality to use all 24 seconds, as Indiana does. The Knicks aren’t a particularly young team, but they aren’t an old one either. They’d likely be more effective with more ball movement, seeking out mismatches, and then using the rabbit-out-of-your-hat-Brunson-7-second-offense as their ace in the hole if nothing better develops.

What sort of shots should they be looking for? Well, Towns, one of the best-shooting big men of all time, attempted just 6.7 3-pointers per 100 possessions this year, his lowest total since 2019, despite shooting 42 percent from long range. The Knicks almost certainly aren’t tapping into this enough. And Bridges often felt like an afterthought, a luxury item that the Knicks paid dearly for. Brunson averaged a 26-and-7 line (points and assists) last year. That’s good, but the Knicks would probably be better if he weren’t pounding the ball so much and posting a 23-and-9 line instead.

So overall, I judge Thibodeau to be guilty of poor tactical execution.

Reason #2 to fire a coach: player development

As an on-and-off NBA bettor — my attempt to take NBA betting seriously is described in On the Edge2 — I’ve learned to trust the “eye test” to some degree. Are you a believer in a team’s high-end talent, or are they the next iteration of the 2015 Atlanta Hawks? Or failing that, executing some sort of extraordinary system that reliably maximizes merely good talent? The eye test is annoyingly subjective, but as I’ve become a more seasoned bettor, I’ve come to think it’s usually a mistake to round down a subjective assumption down to zero.

So say you escape from Plato’s Cave, but somehow possessed of prescient and up-to-date knowledge of the National Basketball Association, and are told that the current Knicks’ roster consists of Brunson, Towns, Bridges, Anunoby, Hart, Mitchell Robinson and Deuce McBride, and then what’s at best some unproven flotsam-and-jetsam beyond that. What would you expect such a team’s regular season and playoff record to be?

Well, Vegas put the Knicks’ over-under at 53.5 wins to start the season. And then they won 51 games. So somewhere in that range feels about right.

But here’s a more rigorous approach that I used in my Future of the Franchise rankings. Mostly, the NBA is a star-driven league; to win a title, you usually need a top 5 player, or failing that two to three players in the top 10 to 20. And then you want to surround them with minor stars and elite role players that fit well with the core. There are perhaps 100 players in the league at any given time that really add to a franchise’s championship equity. Conveniently, The Ringer routinely updates a Top 100 player list, which I have my disagreements with3 but reflects a nice balance between a more stats-driven approach and a well-informed version of the conventional wisdom.

So my system, which I’ll call Talent Points, applies a steeply sliding scale. For example4:

• The #1 player in the league (currently Nikola Jokic) is worth 100 points

• The #10 player (Brunson before the playoffs began) is worth 56

• And the 100th-best player (Bradley Beal) is worth 8.

Here’s how every team’s Talent Points as of the end of the regular season5 compared against their regular season win total:

Read more

One quality you wouldn’t ordinarily associate with New Yorkers is patience. Commuters scamper to catch the subway when they hear the rumble of a train approaching, even though the next one is usually only a few minutes away. Service at restaurants in New York isn’t necessarily polite, but it’s punctual: it’s not a coincidence that the city’s favorite food, pizza, is typically served up in a New York minute. And although we might spend a lot of time stuck in traffic — albeit less now with congestion pricing — the rampant honking suggests we aren’t happy about it.

Except when it comes to basketball. There, the city just waits and waits and waits, with no light at the other end of the Holland Tunnel. Until now.

On Friday, the Knicks advanced to the Eastern Conference Finals by demolishing the Boston Celtics 119-81. The win was uncharacteristically easy: the Knicks had trailed either at halftime or at the start of the fourth quarter in six of their seven previous playoff wins.

Obviously, it helped that the Celtics were not at full strength: Jayson Tatum ruptured his Achilles tendon in Game 4, and Kristaps Porzingis, the former Knick whom the MSG crowd lustily booed at every opportunity, is a shell of himself. And the path forward isn’t necessarily easy. Although the Knicks are now the top seed remaining in the Eastern Conference, they’re only barely favored against the Indiana Pacers, who defeated them in the playoffs last year. Conditional probabilities at major sportsbooks suggest they’d be underdogs in the Finals against any of the Timberwolves, the Nuggets or (especially) the Thunder.

But none of that matters: it’s already a Bing Bong moment, with Timothée Chalamet exchanging high-fives with random strangers like we’re the 1996 Chicago Bulls. It’s the franchise’s first trip to the conference Finals appearance in 25 years, a span during which their best player by Basketball Reference win shares at various times included Kurt Thomas, Jamal Crawford, David Lee and Enes Kanter — and top 10 draft picks were wasted on Michael Sweetney, Jordan Hill, Kevin Knox II and Frank Ntilikina. After all the false starts, all the terrible trades and even worse free agent signings, we deserved this. We earned it.

Subscribe now

What are the rules for adopting sports franchises as adult?

You’ll notice that I stuck a few we’s in there. Although I could make a dubious claim of Knicks family lineage — my mom was born in New York and grew up in Westchester County and I think the first NBA game I ever attended was a Knicks game1 — we were of course a Detroit Pistons household. (With exactly one team in each of the big four sports, Michiganders didn’t have too many tough choices — well, other than between MSU and U of M.) The Knicks, at least, mostly stayed out of the Bad Boy Pistons’ way, knocking them out in the first round in 1992 but only after we felt pretty satisfied with our two championships. Instead, our main rivals were the Bulls, Celtics and Lakers, so rooting for any of those teams as an adult would be a cardinal sin.

Adopting the Knicks isn’t quite so unkosher. Still, I wouldn’t have expected to ever become a Knicks fan. Then I moved to New York in 2009. I’m not sure why exactly, but I felt bad for the team after LeBron James spurned them the next summer, soon unexpectedly finding myself lurking in the comments sections of obscure Knicks blogs. Then came Linsanity in 2012, back when people were still using their BlackBerrys to check out the score, and I saw Knicks love coming out of the pores of the city in a way that few other causes unite New Yorkers. The next year, I moved from Brooklyn to what is technically part of Chelsea but was really more of an ambiguous non-neighborhood primarily defined by its relationship with Madison Square Garden and Penn Station.2 By that point, I was hooked.

I don’t know what the honor code is for adopting sports teams later in life. But I’m pretty sure that you get special dispensation if the team in question is almost literally across the block from you; the MSG marquee dominated my view from my apartment window. There weren’t a lot of points of civic pride in this weird patch of the city, but the Knicks were one of them. Also, I’m pretty sure that you’re not allowed to frontrun, jumping on the bandwagon of an already successful franchise. That would rule out me ever becoming a huge Yankees guy, for instance. But this is not an issue in the case of the MSG teams, the Knicks and the Rangers, who have combined for just one title in my lifetime.

Why New York loves the Knicks

By the books, or at least by the revenue data, the Yankees are still the biggest ticket in town. But it doesn’t feel that way on the ground in Manhattan. Instead, the Yankees are more of a regional juggernaut, the team for people who grew up in Northern New Jersey or on Long Island. The Knicks are the city’s favorite team. And since the city is full of people who migrate here from all around the country and indeed all around the world, they’re a focal point for old and new New Yorkers alike.

New Yorkers have contradictory narratives about the city. One is that of hegemonic dominance. We expect the best and we usually get it: the best food, the best performing arts. We’re both the undisputed financial capital of the world and a contender for the fashion capital, too. The Yankees play into this image: they’re the Bronx Bombers, the Evil Empire. With a brand like that, it would simply be unacceptable not to put a winning product on the field, whatever the price — and the Yankees haven’t had a losing season since 1992.

The Knicks, conversely, have mostly lost, often embrrassingly. But New Yorkers also like to see themselves as scrappy, resilient underdogs, from the Battle of Brooklyn to recovering from “Ford to City: Drop Dead”, the September 11 attacks, and one of the deadliest COVID outbreaks anywhere in the world.

There’s something to this: the city hustles, and it's always been home to hundreds of thousands of immigrants who come here with virtually nothing. Still, this is mostly a self-flattering way to see ourselves. When I asked ChatGPT for examples of how New York City is really a scrappy underdog town, it agreed this was a common trope but rudely called it a “myth”. The best exceptions it could find of New York actually being a down-on-its-luck underdog town were from three of our famously cursed sports franchises: the Jets, Mets — and, of course, the Knicks.

New York Basketball is a thing, and Jalen Brunson is its finest example

It’s not that the Knicks have been underdogs by choice, exactly. They had the highest payroll in the NBA for four consecutive seasons from 2004 through 2007 — i.e. the Isiah Thomas Era — during which they averaged just a 32-50 record.

The Knicks, however, have at least had a common thematic approach that has spanned both their many years in the wilderness and their few moments of success. Unlike the Bronx Bombers, who seek to overwhelm you with firepower, the Knicks like to slow the game down, winning with grit, tenacity, rebounding, and defense. In the 61 seasons since 1964-65, the Knicks have played at a slower pace than the league average all but 10 times, the most significant exceptions coming during Mike D’Antoni’s coaching tenure. Wearing the other team down — as the Knicks have repeatedly done in the playoffs this year — has always been a big part of the brand.

This year’s iteration of the franchise can take this to ridiculous lengths. In theory, the Knicks might function okay as an uptempo, ball-movement and spacing-dependent offense, the style that has dominated the league since D’Antoni’s 7-seconds-or-less teams in Phoenix. They often field lineups with four or five players who are at least competent 3-point shooters, and they have quick-twitch athletes like Mikal Bridges and OG Anunoby on the roster.

But that’s just not the way Jalen Brunson rolls. Instead, Brunson lurks around the perimeter, often dribbling with his back to the basket, sometimes bypassing open looks from one of the more middling shooters3 as he presses for tactical advantages that are rarely obvious to anyone but Brunson himself. With 10 seconds left on the shot clock — then 9, 8, 7 — the possession often looks like a bust.

Then Brunson not so much drives as burrows toward the basket, and often pulls a rabbit out of his hat: a fadeaway jumper, a running finger roll layup, a kickout to one of the shooters. During the regular season, the Knicks averaged 0.9 points per shot attempt on field goals taken with four seconds or less on the shot clock, the third-best rating in the league. While that’s not great by the standards of a modern NBA possession — the league averaged 1.09 points scored on shot attempts overall this year — it’s often better than settling for some bailout, especially since these longer possessions often produce a lot of fouls and offensive rebounds, which aren’t accounted for the data I’m citing here.

I deliberately left a lot of white space in that scatterplot because now I’m going to compare it to the playoffs. This year especially, the NBA has basically become a whole different sport in the postseason: more physical, a slower tempo, with max defensive effort on every play.4 As you can see, the number of late-shot-clock possessions has considerably risen in the playoffs. But efficiency is notably down on account of facing better, more engaged defenses.5 The Knicks have thrived under these conditions, however, averaging 1.02 points per field goal attempt so far in the playoffs with four or fewer seconds on the clock. No other team has even reached 0.9:

Is this sustainable? Probably not to this level of consistent execution. Still, the Knicks put up those numbers against the Celtics and Pistons, who had two of the best rim defenses in the league this season.

Brunson may not be as lights-out as he was in Game 4 against the Celtics or Game 6 against Detroit, and it will depend on how the game is officiated. (I love you, Jalen, but you’re guilty as charged on the flopping stuff — and it’s usually a sign you’re on your B-game). Still, this could potentially frustrate the Pacers, who true to their moniker prefer to play a faster pace and are one of the most efficient transition teams in the league.

If we’re getting a lot of 117-111 scores, I’d probably take Indiana in the series. But if we’re back to the 90s — say, Knicks 93, Pacers 89 — that will indicate the Knicks are winning the tactical battle, and continuing to win over the city.