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A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach

Author

Listed:
  • Pierpalo D’Urso

    (Sapienza University of Rome)

  • Livia Giovanni

    (Data Lab Luiss University)

  • Vincenzina Vitale

    (Sapienza University of Rome)

Abstract

Statistics in sports plays a key role in predicting winning strategies and providing objective performance indicators. Despite the growing interest in recent years in using statistical methodologies in this field, less emphasis has been given to the multivariate approach. This work aims at using the Bayesian networks to model the joint distribution of a set of indicators of players’ performances in basketball in order to discover the set of their probabilistic relationships as well as the main determinants affecting the player’s winning percentage. From a methodological point of view, the interest is to define a suitable model for non-Gaussian data, relaxing the strong assumption on normal distribution in favour of Gaussian copula. Through the estimated Bayesian network, we discovered many interesting dependence relationships, providing a scientific validation of some known results mainly based on experience. At last, some scenarios of interest have been simulated to understand the main determinants that contribute to rising in the number of won games by a player.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-04871-5
    DOI: 10.1007/s10479-022-04871-5
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    References listed on IDEAS

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    1. Hanea, A.M. & Kurowicka, D. & Cooke, R.M. & Ababei, D.A., 2010. "Mining and visualising ordinal data with non-parametric continuous BBNs," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 668-687, March.
    2. Kubatko Justin & Oliver Dean & Pelton Kevin & Rosenbaum Dan T, 2007. "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-24, July.
    3. Page Garritt L & Fellingham Gilbert W & Reese C. Shane, 2007. "Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-18, October.
    4. Brian Skinner & Stephen J Guy, 2015. "A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.
    5. Daniel Cervone & Alex D’Amour & Luke Bornn & Kirk Goldsberry, 2016. "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 585-599, April.
    6. Metulini Rodolfo & Manisera Marica & Zuccolotto Paola, 2018. "Modelling the dynamic pattern of surface area in basketball and its effects on team performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(3), pages 117-130, September.
    7. Chih-Hai Yang & Hsuan-Yu Lin & Chiang-Ping Chen, 2014. "Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA," Annals of Operations Research, Springer, vol. 217(1), pages 565-589, June.
    8. Loeffelholz Bernard & Bednar Earl & Bauer Kenneth W, 2009. "Predicting NBA Games Using Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-17, January.
    9. Steven Wu & Luke Bornn, 2018. "Modeling Offensive Player Movement in Professional Basketball," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 72-79, January.
    10. Fearnhead Paul & Taylor Benjamin Matthew, 2011. "On Estimating the Ability of NBA Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-18, July.
    11. 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.
    12. Baghal Tarek, 2012. "Are the "Four Factors" Indicators of One Factor? An Application of Structural Equation Modeling Methodology to NBA Data in Prediction of Winning Percentage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-17, March.
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