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Modelling player performance in basketball through mixed models

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  • Martí Casals
  • A. Jose Martinez

Abstract

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.

Suggested Citation

  • Martí Casals & A. Jose Martinez, 2013. "Modelling player performance in basketball through mixed models," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(1), pages 64-82, April.
    • Handle: RePEc:taf:rpanxx:v:13:y:2013:i:1:p:64-82
    DOI: 10.1080/24748668.2013.11868632
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    Cited by:

    1. Bakkenbüll, Linn-Brit, 2017. "Physical constitution matters for athletic performance and salary of NBA players," Discussion Papers of the Institute for Organisational Economics 1/2017, University of Münster, Institute for Organisational Economics.
    2. Tomasz Zając & Kazimierz Mikołajec & Paweł Chmura & Marek Konefał & Michał Krzysztofik & Piotr Makar, 2023. "Long-Term Trends in Shooting Performance in the NBA: An Analysis of Two- and Three-Point Shooting across 40 Consecutive Seasons," IJERPH, MDPI, vol. 20(3), pages 1-12, January.
    3. Patric Dolmeta & Raffaele Argiento & Silvia Montagna, 2023. "Bayesian GARCH modeling of functional sports data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 401-423, June.
    4. Zsombor Zilinyi & Ágoston Nagy & Szilvia Borbély & Tamás Sterbenz, 2022. "Bounded Rationality and Heuristics: Do We Only Need to Score in Order to Win Individual Awards in Basketball?," IJERPH, MDPI, vol. 19(4), pages 1-9, February.
    5. Pierpalo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach," Annals of Operations Research, Springer, vol. 325(1), pages 419-440, June.

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