Rating System § Measuring success in basketball § Possessions § Scoring efﬁciency § Turning statistics into a rating system § Expected winning percentages § Using the ratings § Predicting games § Predicting point spreads § Alternative rating methodologies § Elo § Correlated-Gaussian
§ Seems trivial, but points and margin of victory are way more informative than wins and losses. § Points in a game are inﬂuenced by the quality of the two teams and how fast the game is played. § Points per possession, or 100 possession (Oliver, 2004). § Tempo-free statistics (Pomeroy, 2012). October 9, 2015 Statistical Analysis in Sports 4
and average of 1.10 points per possession. § Kansas plays slow, whereas Duke likes to play fast. § Both teams play Missouri, which averages 0.95 points per possession. October 9, 2015 Statistical Analysis in Sports 5 Team Team Efﬁciency Missouri’s Efﬁciency Possessions Expected Score Kansas 1.10 0.95 60 66-57 Duke 1.10 0.95 74 81-70
be calculated by: § Off. PPP = Points Scored / Total Possessions § Def. PPP = Points Allowed / Total Possessions § Commonly multiplied by 100 to give us points per 100 possessions. October 9, 2015 Statistical Analysis in Sports 6
(350) § One defensive parameter per team (350) § One intercept § Two home court parameters § Selecting the reference teams § One reference on offense and defense § Selected iteratively § Originally the last team alphabetically § Model rerun with reference team set to the team with the average offensive/defensive efﬁciency. October 9, 2015 Statistical Analysis in Sports 9
and defensive efﬁciency. § Offensive Efﬁciency = β 0 + β Team_Off § Calculate the mean offensive and defensive efﬁciency. § Determine which team is closest to the mean of each efﬁciency. § These are the new reference teams. § Estimate the model again with the updated reference teams. § Continue until the same teams are selected as the reference teams in consecutive runs. October 9, 2015 Statistical Analysis in Sports 10
win in proportion to their “quality”, n = 2. § n varies by sport, and reﬂects the role that chance plays in the outcome of games. § MLB: n = 1.83 § NHL: n = 2.15 § NFL: n = 2.37 § NBA: n = 16.5 October 9, 2015 Statistical Analysis in Sports 13
§ An adaptation of the Pythagorean rating where each team has their own exponent. § Based on the idea that points are more important in low-scoring games. § The more points that are scored, the higher ni will be (less chance). October 9, 2015 Statistical Analysis in Sports 14
margin of victory into the logit model. § Provides better predictions for extreme seasons and tends to be more stable over time. October 9, 2015 Statistical Analysis in Sports 15
function in R. § Calculate the pre-game adjusted efﬁciencies for each game from the 2002-03 season to the 2014-15 season. § Use optim to ﬁnd the exponents that minimize the binomial deviance, or log loss. October 9, 2015 Statistical Analysis in Sports 16
for each team. § To get a composite rating, I take the mean of the three ratings, weighted by Log Loss. October 9, 2015 Statistical Analysis in Sports 17 Method Exponent Log Loss Pythagorean 9.972 0.532 Pythagenpat 1.208 0.531 Linear/Logistic Combo -0.010 0.531
beating their opponent by using the Log5 formula (James, 1981). § This model generalizes to include the Bradley-Terry-Luce model commonly used in psychology, and the Rasch model in psychometrics (Long, 2013). § Kansas (0.9274) vs. Ohio State (0.9302): October 9, 2015 Statistical Analysis in Sports 20
compare the projected point spread to the actual margin of victory: § Calculate expected point spread for each game by averaging the three methods, weighted by RMSE. § We can also calculate win probabilities from the projected point spreads using logistic regression. October 9, 2015 Statistical Analysis in Sports 22 Method RMSE Weight GLS 10.774 0.331 Average Efﬁciencies 10.664 0.334 Net Efﬁciencies 10.651 0.335
and Paine (2012). § Model assumes the margin of victory of a given game is ~N(PS, 10.612). § The mean and standard deviation of the distribution over the course of a game are given by: § StDev = 10.612 / sqrt(40 / minutes remaining) § Mean = (PS * (minutes remaining / 40)) + (Margin * (40 / minutes played)) § The win probability is given by the proportion of the distribution covering margins of victory that would result in the team winning. October 9, 2015 Statistical Analysis in Sports 25
used in chess and international soccer. § How it works: § Given a starting state of two teams, how is each team expected to perform? § How did the teams actually perform? § Update the ratings with this new information. October 9, 2015 Statistical Analysis in Sports 29
out at a rating of 1300. § For a given game, Team A’s win probability is given by: § Team A’s rating is then updated using: October 9, 2015 Statistical Analysis in Sports 30
are more impressive and usually more informative, so: § Where the MOV Factor is given by: § Complicated but corrects for autocorrelation problems (favorites tend to win by more than they lose; Silver & Fischer-Baum, 2015). October 9, 2015 Statistical Analysis in Sports 31
scores (location can also be added in) § Track historical trends § Cons: § Ratings heavily dependent on performance in previous seasons (can be less accurate early in season) § Ratings are not retroactively adjusted to account for team’s being better/worst than expected. October 9, 2015 Statistical Analysis in Sports 32
by Oliver (2004) to estimate a team’s expected winning percentage given their performance. § Not adjusted for strength of schedule, but can be. § The raw correlated-Gaussian rating can be used to estimate a team’s “luck”: § Luck = Win% - CorGaus% October 9, 2015 Statistical Analysis in Sports 33
James, B. (1983). Baseball Abstracts. New York: Ballantine Books. Kubatko, J. (2013). Pythagoras of the hardwood [Web log post]. Retrieved from http://statitudes.com/blog/ 2013/09/09/pythagoras-of-the-hardwood/ Long, C. (2013). Baseball, chess, psychology, and psychometrics: Everyone uses the same damn rating system [Web log post]. Retrieved from http://angrystatistician.blogspot.com/2013/03/baseball-chess- psychology-and.html Oliver, D. (2004). Basketball on paper: Rules and tools for performance analysis. Dulles, Virginia: Potomac Books, Inc. Paine, N. (2012). Are NFL playoff outcomes getting more random? [Web log post]. Retrieved from http:// www.footballperspective.com/are-nﬂ-playoff-outcomes-getting-more-random/ Pomeroy, K. (2012, June 8). Ratings glossary [Web log post]. Retrieved from http://kenpom.com/blog/index.php/ weblog/entry/ratings_glossary Silver, N. & Fischer-Baum, R. (2015). How we calculate NBA Elo ratings [Web log post]. Retrieved from http:// ﬁvethirtyeight.com/features/how-we-calculate-nba-elo-ratings/ Smyth, D. & Heipp, B. (2009). Runs Per Win From Pythagenpat [Web log post]. Retrieved from http:// walksaber.blogspot.com/2009/01/runs-per-win-from-pythagenpat.html Winston, W. (2012). Mathletics: How gamblers, managers, and sports enthusiasts use mathematics in baseball, basketball, and football. Princeton, NJ: Princeton University Press. October 9, 2015 Statistical Analysis in Sports 34