Modelling player performance in basketball through mixed models

The aims of this study were to identify variables which may potentially influence player performance, and to implement a statistical model to study their relative contribution in order to explain two outcomes: points and win score. We used all the possible variables affecting player performance creating a comprehensive database from two sources of statistical information about the NBA 2007 regular season: www.basketball-reference.com and www.nbastuffer.com. The data employed for the analysis were composed of 2187 cases (27 players * 81 games), having followed a filtering process. We dealt with a balanced study design with repeated measurements given that each player was observed the same number of games, and therefore the player was considered as a random effect. We carried out mixed models to quantify the variability in points and win score among players. Minutes played, the usage percentage and the difference of quality between teams were the main factors for variations in points made and win score. The interaction between player position and age was important in win score. We encourage managers and coaches of sports teams to choose appropriate methods according to their aims. Future research should take into consideration the use of models with random effects on players’ characteristics.

[1]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[2]  Derek D. Reed Quantitative Analysis of Sports , 2011 .

[3]  Ana Ivelisse Avilés,et al.  Linear Mixed Models for Longitudinal Data , 2001, Technometrics.

[4]  Jerry A. Hausman,et al.  Panel Data and Unobservable Individual Effects , 1981 .

[5]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[6]  Peter O’Donoghue,et al.  Development and application of computer-based prediction methods , 2005 .

[7]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[8]  David J. Berri,et al.  The Wages of Wins: Taking Measure of the Many Myths in Modern Sport , 2006 .

[9]  Piette James,et al.  Scoring and Shooting Abilities of NBA Players , 2010 .

[10]  J. Bradbury Peak athletic performance and ageing: Evidence from baseball , 2009, Journal of sports sciences.

[11]  W. Hopkins,et al.  Methods for tracking athletes' competitive performance in skeleton , 2009, Journal of sports sciences.

[12]  John Hollinger,et al.  Pro Basketball Forecast , 2004 .

[13]  K. Mandroukas,et al.  Seasonal variation of aerobic performance in soccer players according to positional role. , 2006, The Journal of sports medicine and physical fitness.

[14]  David J. Berri,et al.  Working in the Land of the Metricians , 2010 .

[15]  Sophia Rabe-Hesketh,et al.  Multilevel and longitudinal modeling using Stata. 2nd edition , 2008 .

[16]  T. Maclennan Moneyball: The Art of Winning an Unfair Game , 2005 .

[17]  Martin B. Schmidt,et al.  Does one simply need to score to score , 2007 .

[18]  Alexander M. Schoemann,et al.  Multilevel and longitudinal modeling , 2014 .

[19]  David J. Berri,et al.  Stumbling On Wins: Two Economists Expose the Pitfalls on the Road to Victory in Professional Sports , 2009 .

[20]  W. Mallios Forecasting in Financial and Sports Gambling Markets: Adaptive Drift Modeling , 2010 .

[21]  Michael J. Crawley,et al.  The R book , 2022 .

[22]  Jeremy Arkes,et al.  Journal of Quantitative Analysis in Sports Finally , Evidence for a Momentum Effect in the NBA , 2011 .

[23]  David J. Berri Measuring Performance in the National Basketball Association , 2012 .

[24]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[25]  R Hugh Morton,et al.  Season-to-Season Variations of Physiological Fitness Within a Squad of Professional Male Soccer Players. , 2008, Journal of sports science & medicine.

[26]  Philippe Hellard,et al.  Modeling the training-performance relationship using a mixed model in elite swimmers. , 2003, Medicine and science in sports and exercise.

[27]  David J. Berri,et al.  Who is 'most valuable'? Measuring the player's production of wins in the National Basketball Association , 1999 .

[28]  A Note on Consistent Players’ Valuation , 2002 .

[29]  J. Schafer,et al.  Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values , 2002 .

[30]  Jeffrey M. Wooldridge,et al.  Introductory Econometrics: A Modern Approach , 1999 .

[31]  Justin Kubatko,et al.  A Starting Point for Analyzing Basketball Statistics , 2007 .

[32]  Jaime Sampaio,et al.  Effects of season period, team quality, and playing time on basketball players' game-related statistics , 2010 .

[33]  Dean Oliver,et al.  Basketball on Paper: Rules and Tools for Performance Analysis , 2003 .

[34]  R. Koning Home advantage in professional tennis , 2011, Journal of sports sciences.

[35]  J. A. Martínez,et al.  EL USO DE INDICADORES DE DESEMPEÑO NORMALIZADOS PARA LA VALORACIÓN DE JUGADORES: EL CASO DE LAS ESTADÍSTICAS POR MINUTO EN BALONCESTO , 2010 .

[36]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .