SEEDING IN THE NCAA MEN'S BASKETBALL TOURNAMENT: WHEN IS A HIGHER SEED BETTER?

A number of methods have been proposed for predicting game winners in the National Collegiate Athletic Association’s (NCAA) annual men’s college basketball championship tournament. Since 1985, more than 70% of the teams in the fourth, fifth, and sixth rounds of the tournament have been high-seeded teams (i.e., teams assigned seeds of one, two, or three); a method that can accurately compare two such teams is often necessary to predict games in these rounds. This paper statistically analyzes tournaments from 1985 to 2009. A key finding is that there is an insignificant difference between the historical win percentages of high-seeded teams in each of the fourth, fifth, and sixth tournament rounds, which implies that choosing the higher seed to win games between these seeds does not provide accurate predictions in these rounds, and alternate predictors or methods should be sought. Implications on gambling point spreads are discussed.

[1]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[2]  Nitin R. Patel,et al.  A Network Algorithm for Performing Fisher's Exact Test in r × c Contingency Tables , 1983 .

[3]  W. Mendenhall,et al.  Statistics for engineering and the sciences , 1984 .

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

[5]  S. P. Wright,et al.  Adjusted P-values for simultaneous inference , 1992 .

[6]  Descriptors Change Strategies in the National Collegiate Athletic Association , 1993 .

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

[8]  Andrew Metrick,et al.  March madness? Strategic behavior in NCAA basketball tournament betting pools , 1996 .

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

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

[11]  Bill M. Woodland,et al.  Testing Contrarian Strategies in the National Football League , 2000 .

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

[13]  Edward H. Kaplan,et al.  March Madness and the Office Pool , 2001, Manag. Sci..

[14]  L. K. Miller,et al.  The Gambling Industry and Sports Gambling: A Stake in the Game? , 2001 .

[15]  Gerd Gigerenzer,et al.  Models of ecological rationality: the recognition heuristic. , 2002, Psychological review.

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

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

[18]  Rodney J. Paul,et al.  Bettor Misperceptions in the NBA , 2005 .

[19]  Tilman Klumpp,et al.  Early Round Upsets and Championship Blowouts , 2004 .

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

[21]  Christian Frings,et al.  Who will win Wimbledon? The recognition heuristic in predicting sports events , 2006 .

[22]  Thorsten Pachur,et al.  Forecasting from ignorance: the use and usefulness of recognition in lay predictions of sports events. , 2007, Acta psychologica.