How is margin of victory (mov) implemented in your model? Is (3 + s)^.85 applied as a scalar to your k value or as an addition to post-game elo change?
In other words,
If the standard elo change is: +- k * (1 - expected_win)
is the mov applied like:
1. +- k * (1 - expected_win) * (3+s)^.85 (would be a large number)
2. +- k * (1 - expected_win) + (3+s)^.85
Both seem larger than I'd expect. In the scenario where a 2000 elo team beats a 1500 elo team by 20, the expected point spread, the winning team would normally gain ~2 elo points (k factor of 38). With the MOV adjustment (as addition, option 2 above), this would be boosted to ~16. (as mult, option 1 above, this would be ~30).
Intuitively, this feels high for a team meeting expectations. Am I missing something? Or is this meant to add volatility?
"Margin of victory matters. Although there is some degree of diminishing returns — in basketball particularly, teams often send in the scrubs in lopsided games, and free-throw strategy can turn narrow wins into games that were closer than they appear from the final score — larger margins of victory get a higher multiplier. Specifically, the margin of victory factor is calculated as (3 + s) ^ .85, where s is the scoring differential"
Based on notes on some old 538 models, I think there is another term in the +- update which normalizes so that the expected post-game rating is the same as the pre-game rating. This is akin to dividing the `k * (1 - expected_win) * (3+s)^.85` by the expected value of `(3+s)^.85` conditioned on s being positive. If the expected post-game rating is not the same as the pre-game rating then there is something the model "knows" that is nor reflected in the current ratings.
So there is a "rating" of sorts, not just a ranking
Don't use the conversion factors from Sokol's early papers because they are for classical LRMC. But the two conversion *methods* in those papers might be useful.
Can the outcome of a game between A and B change C’s rating? For example, if C beat B last week and then B upsets A so B’s rating rises, does C’s go up too?
How is margin of victory (mov) implemented in your model? Is (3 + s)^.85 applied as a scalar to your k value or as an addition to post-game elo change?
In other words,
If the standard elo change is: +- k * (1 - expected_win)
is the mov applied like:
1. +- k * (1 - expected_win) * (3+s)^.85 (would be a large number)
2. +- k * (1 - expected_win) + (3+s)^.85
Both seem larger than I'd expect. In the scenario where a 2000 elo team beats a 1500 elo team by 20, the expected point spread, the winning team would normally gain ~2 elo points (k factor of 38). With the MOV adjustment (as addition, option 2 above), this would be boosted to ~16. (as mult, option 1 above, this would be ~30).
Intuitively, this feels high for a team meeting expectations. Am I missing something? Or is this meant to add volatility?
"Margin of victory matters. Although there is some degree of diminishing returns — in basketball particularly, teams often send in the scrubs in lopsided games, and free-throw strategy can turn narrow wins into games that were closer than they appear from the final score — larger margins of victory get a higher multiplier. Specifically, the margin of victory factor is calculated as (3 + s) ^ .85, where s is the scoring differential"
Based on notes on some old 538 models, I think there is another term in the +- update which normalizes so that the expected post-game rating is the same as the pre-game rating. This is akin to dividing the `k * (1 - expected_win) * (3+s)^.85` by the expected value of `(3+s)^.85` conditioned on s being positive. If the expected post-game rating is not the same as the pre-game rating then there is something the model "knows" that is nor reflected in the current ratings.
The LRMC (Baysian) pi factors are posted here:
https://www2.isye.gatech.edu/~jsokol/lrmc/lrmcratings.html
So there is a "rating" of sorts, not just a ranking
Don't use the conversion factors from Sokol's early papers because they are for classical LRMC. But the two conversion *methods* in those papers might be useful.
PS: a linear conversion of the pi's to spreads is not very good in my opinion.
Can the outcome of a game between A and B change C’s rating? For example, if C beat B last week and then B upsets A so B’s rating rises, does C’s go up too?
Does your Elo update rule satisfy the martingale property? I.e. that the expected ratings after a game are the same as the ratings before the game.