Rating college basketball teams is mainly about identifying NCAA tournament teams. The tournament itself decides who is the ultimate champion, but who deserves to be there?
There are arguments every year regarding fringe teams. ESPN Bracketologist Joe Lunardi will tell you who is likely to make it. Jay Bilas and other ESPN college basketball analysts want to be able to tell you who should make it. To that end, they asked the ESPN Analytics Department to help them. What we created is this, the College Basketball Power Index, or BPI for short.
Other power rankings already exist, the most prominent being the RPI, Sagarin Ratings, Massey Ratings and Ken Pomeroy's ("Kenpom") ratings. All of these methods are based upon the outcomes of games, their location -- home/road/neutral -- and the quality of opponents. Each one basically puts these together in slightly different ways and arrives at slightly different results.
RPI, due to its simplicity, tends to be the biggest decision aid for the NCAA tournament committee, even though it doesn't account for the actual scores of games. Kenpom and our BPI system both account for the varying pace that teams can play, which is an important analytical component of evaluating basketball teams. But we believe the BPI is a little more refined than any other existing power ranking.
There are a number of small details that we have in our methodology to make it reflective of a résumé for a tournament team -- these are pretty technical and many people won't be interested, so we won't go into detail, but we think they improve how the tool works.
On top of this, we decided to incorporate a little bit more information than the other power ranking systems use. In particular, we added a way of accounting for missing players.
If a team or its opponent is missing one of its most important players (determined by minutes per game) for a contest, that game is less important for ranking the teams compared to games in which both teams are at full strength.
Every season has teams for whom missing players can be important. In 2011-12, for example, Syracuse center Fab Melo missed three games in the middle of the season and, in that stretch, the Orange lost their first game of the year. He returned for the rest of the regular season and Syracuse lost only once more – in the Big East Conference Tournament. Over all these games, the three games without Melo get weighted a little less – in proportion to how much he played regularly – towards their season BPI. This means that the loss to Notre Dame while he was out, though it lowers Syracuse's BPI, lowers it less than had they lost by the same amount with him in the lineup.
The Melo case is a useful example of how BPI also can help evaluate a team when a player presence for the NCAA Tournament itself is in question. Although the regular season BPI had Syracuse second with a BPI of 90 entering the Tournament, it turned out that Melo wouldn't be eligible and Syracuse wasn't necessarily the same team that BPI was evaluating. BPI is available on a game-by-game basis, so you can look at the three games that Melo missed and see what Syracuse's BPI was in those games– it was 41 against Notre Dame, 96 against Cincinnati, and 84 against West Virginia, for an average of 74. These are still opponent-adjusted and still adjusted for home/road/neutral, so the profile paints a picture that can be useful for identifying Tournament teams or adjusting seeds. If Melo hadn't missed any games during the season, one could still look at games where he played relatively few minutes to see if there was an effect.
Another way that BPI can rank teams differently than Sagarin or Kenpom is counting close games at home versus on the road. In BPI, a close win at home is better than a close loss on the road against the same opponent. This isn't necessarily true in other methods and, in methods that do that, they don't typically account for bigger wins. BPI gives marginally decreasing credit for bigger wins, with a 30-point win being only about 20 percent better than a 15-point win, not twice as good, which can happen in other methods.
By capturing blowouts, but not overweighting them, BPI credits the ability of good teams to easily beat poor teams without providing incentive to win by 30 when 20 is a safe margin. By capturing both blowouts and close games in this way, BPI summarizes a team's résumé for the NCAA tournament well.
By reflecting a résumé, BPI was not explicitly built to make predictions. But, in tests, its ability to predict appears to be as good or better than Sagarin or RPI at predicting results in the NCAA tournament and NIT.
Between the 2007 and 2011 NCAA tournaments, it picked 74.4 percent of the matchups correctly, whereas Sagarin picked 73.2 percent and RPI picked 71.9 percent. (Kenpom is more difficult to evaluate because its pre-tournament rankings are not available.) The average ranking of the NIT finalists was better in BPI than in Sagarin or RPI. Notice, of course, that many of these differences are small. The BPI is not a guaranteed way to pick a perfect bracket, but we do think it is the best power ranking available.
Ultimately, the College Basketball Power Index gives us a tool for rating teams that is more useful for ESPN than existing tools. It explains a team's wins and losses in a more detailed context and seemingly predicts future results as well as other tools, if not better. It also incorporates information about injuries and missed games that should be relevant for weighting the résumés of bubble teams on Selection Sunday. We feel confident that it will enhance coverage of college basketball for years to come.
Dean Oliver is ESPN's director of production analytics and previously helped construct Total QBR. He is the author of "Basketball on Paper" and has worked in the front offices of the Seattle SuperSonics and Denver Nuggets. His ESPN archives can be viewed here.