Are differences in ranks good predictors for Grand Slam tennis matches

This paper tests whether the differences in rankings between individual players are good predictors for Grand Slam tennis outcomes. We estimate separate probit models for men and women using Grand Slam tennis match data from 2005 to 2008. The explanatory variables are divided into three groups: a player's past performance, a player's physical characteristics, and match characteristics. We estimate three alternative probit models. In the first model, all of the explanatory variables are included, whereas in the other two specifications, either the player's physical characteristics or the player's past performances are not considered. The accuracies of the different models are evaluated both in-sample and out-of-sample by computing Brier scores and comparing the predicted probabilities with the actual outcomes from the Grand Slam tennis matches from 2005 to 2008 and from the 2009 Australian Open. In addition, using bootstrapping techniques, we also evaluate the out-of-sample Brier scores for the 2005-2008 data.

[1]  John Goddard,et al.  Forecasting football results and the efficiency of fixed‐odds betting , 2004 .

[2]  Vasant A. Sukhatme,et al.  Testing Rosen's Sequential Elimination Tournament Model , 2008 .

[3]  John Goddard,et al.  Regression models for forecasting goals and match results in association football , 2005 .

[4]  S. Coles,et al.  Modelling Association Football Scores and Inefficiencies in the Football Betting Market , 1997 .

[5]  W. Greene,et al.  Discrete Choice Modeling , 2007 .

[6]  Håvard Rue,et al.  Prediction and retrospective analysis of soccer matches in a league , 2000 .

[7]  Robert Simmons,et al.  Forecasting sport: the behaviour and performance of football tipsters , 2000 .

[8]  Ladder tournaments and underdogs: lessons from professional bowling , 2002 .

[9]  Jan R. Magnus,et al.  Forecasting the winner of a tennis match , 2003, Eur. J. Oper. Res..

[10]  Steven B. Caudill,et al.  Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament , 2002 .

[11]  David Law,et al.  The Favourite-Longshot Bias and Market Efficiency in UK Football Betting , 2000 .

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

[13]  Ruth N. Bolton,et al.  Searching for positive returns at the track: a multinomial logic model for handicapping horse races , 1986 .

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

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

[16]  Robert Simmons,et al.  Odds setters as forecasters: the case of English football , 2005 .

[17]  S. Clarke,et al.  Using official ratings to simulate major tennis tournaments , 2000 .

[18]  J. Magnus,et al.  Are Points in Tennis Independent and Identically Distributed? Evidence From a Dynamic Binary Panel Data Model , 2001 .

[19]  David Dyte,et al.  A ratings based Poisson model for World Cup soccer simulation , 2000, J. Oper. Res. Soc..

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

[21]  Patric Andersson,et al.  Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts☆ , 2005 .

[22]  Ian G. McHale,et al.  Anyone for Tennis (Betting)? , 2007 .

[23]  Lee Sigelman,et al.  The forecasting accuracy and determinants of football rankings , 2001 .