NCAA Tournament Games: The Real Nitty-Gritty

The NCAA Division I Men's Basketball Committee annually selects its national championship tournament's at-large invitees, and assigns seeds to all participants. As part of its deliberations, the Committee is provided a so-called "nitty-gritty report" for each team, containing numerous team performance statistics. Many elements of this report receive a great deal of attention by the media and fans as the tournament nears, including a team's Ratings Percentage Index (or RPI), overall record, conference record, non-conference record, strength of schedule, record in its last 10 games, etc. However, few previous studies have evaluated the degree to which these factors are related to whether a team actually wins games once the tournament begins. Using nitty-gritty information for the participants in the 638 tournament games during the 10 seasons from 1999 through 2008, we use stepwise binary logit regression to build a model that includes only eight of the 32 nitty-gritty factors we examined. We find that in some cases factors that receive a great deal of attention are not related to game results, at least in the presence of the more highly related set of factors included in the model.

[1]  Adaptive Choice of Tuning Constant for Robust Regression Estimators , 1996 .

[2]  Steven B. Caudill,et al.  Predicting discrete outcomes with the maximum score estimator: the case of the NCAA men's basketball tournament , 2003 .

[3]  Shane Sanders,et al.  A Cheap Ticket to the Dance: Systematic Bias in College Basketball's Ratings Percentage Index , 2007 .

[4]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[5]  N. C. Schwertman,et al.  More Probability Models for the NCAA Regional Basketball Tournaments , 1991 .

[6]  M. Wooders,et al.  On the theory of equalizing differences Increasing abundances of types of workers may increase their earnings , 2001 .

[7]  Hal S Stern,et al.  Statistics and the College Football Championship , 2004 .

[8]  T WestBrady,et al.  A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament , 2006 .

[9]  Joel Sokol,et al.  A logistic regression/Markov chain model for NCAA basketball , 2006 .

[10]  Barbara Mock,et al.  A Statistician Reads the Sports Pages , 1998 .

[11]  Neil C. Schwertman,et al.  Can the NCAA basketball tournament seeding be used to predict margin of victory , 1999 .

[12]  D. Harville,et al.  The Selection or Seeding of College Basketball or Football Teams for Postseason Competition , 2003 .

[13]  Bradley P. Carlin Improved NCAA Basketball Tournament Modeling via Point Spread and Team Strength Information , 1996 .

[14]  B. West A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament: Updated Results from 2007 , 2008 .

[15]  B. Jay Coleman,et al.  Identifying the NCAA Tournament "Dance Card" , 2001, Interfaces.

[16]  Herman Stekler,et al.  Are sports seedings good predictors?: an evaluation , 1999 .

[17]  H. Stekler,et al.  P redicting the outcomes of National Football League games , 2003 .