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Silver Bulletin Women's College Basketball (SBCB) ratings ๐Ÿ€
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Silver Bulletin Women's College Basketball (SBCB) ratings ๐Ÿ€

Our Elo-derived ratings for all 362 women's Division I NCAA basketball teams.

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Nate Silver
Mar 13, 2025
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Silver Bulletin
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Silver Bulletin Women's College Basketball (SBCB) ratings ๐Ÿ€
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These are our season-ending womenโ€™s SBCB ratings for 2024-25! Theyโ€™ll be back next season! For the menโ€™s version of SBCB ratings, see here.

Welcome to the homepage for the womenโ€™s edition of our Silver Bulletin College Basketball (SBCB) ratings. It will be updated roughly once per day, either in the evening or the morning after the full slate of games is played.1 If youโ€™re viewing this in the Substack app or your email client, you may want to click over to the web version to see the latest data.

There are two versions of SBCB: a โ€œpureโ€ version that essentially only accounts for performance on the court and a โ€œBayesianโ€ version that also factors in human ratings โ€” specifically preseason ratings in the AP and Coaches polls. For instance, on the menโ€™s side, Kansas has a considerably higher rating in the Bayesian version because they were the preseason #1 โ€” although their performance hasnโ€™t lived up to their advance billing. Despite this, the Bayesian version generally yields more accurate predictions of upcoming games. Here is the current top 10 in each system:

Note that the top womenโ€™s teams tends to have higher ratings than the men, as there is more dominance in the womenโ€™s game and more lopsided outcomes.

The pure version of SBCB is free for all readers, while the Bayesian version requires a paid subscription. Paid subscribers will also get a spreadsheet showing how to translate Elo ratings to win probabilities and projected margins of victory (i.e. point spreads). And theyโ€™ll get full access to our menโ€™s and womenโ€™s NCAA tournament forecasts once those launch, which blend SBCB with other ratings.

SBCB is an Elo-based rating system, though with many enhancements and modifications. For a more complete methodological description, including some subtle differences for the womenโ€™s version of the ratings, see here. Iโ€™ll just go over the basics in a few bullet points.

  • The most important factor in Elo is simply winning, especially against strong opponents. A team can never lose Elo status in any game it wins โ€” but it gains far more credit for winning games as an underdog.

  • However, our formula also accounts for margin of victory and home court.

  • Home-court advantage consists of two factors. First, a rolling score specific to each school indicating its tendency to overperform or underperform its Elo rating in home games โ€” menโ€™s teams like Purdue and Duke that are reputed to have substantial home-court advantages often do. Home-court advantage is generally lower in the womenโ€™s game, though the most popular programs tend to have more of one. Second, a travel distance factor.2

  • SBCB ratings never reset to zero. Games from previous seasons have some influence. This adds predictive value since the college basketball season is relatively short, though it may make SBCB less appropriate for comparing tournament resumes based on the current seasonโ€™s play.

  • There are some special procedures at the start of each season. In the pure version of SBCB, each teamโ€™s rating is reverted toward the mean of its conference. In the Bayesian version, the mean reversion process considers human polls in addition to conference strength.

  • The womenโ€™s ratings account for more than 100,000 games3 going back to the 2002-03 season4. Some of the modifications weโ€™ve made to reflect the changing nature of menโ€™s basketball donโ€™t apply to the women. For instance, thereโ€™s higher ratings carryover from season to season because players canโ€™t enter the WNBA until theyโ€™re 22, meaning they retain more of their top talent.

Letโ€™s keep the rest of this simple. Here are the pure SBCB ratings for those of you who donโ€™t want any human-flavored inputs:

And here is the Bayesian version, which should be slightly better for predictions:

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