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Modelling the dynamic pattern of surface area in basketball and its effects on team performance

Author

Listed:
  • Metulini Rodolfo

    (University of Brescia, Big & Open Data Innovation (BODaI) Laboratory, C.da S. Chiara, 50, Brescia IT 25122, Italy)

  • Manisera Marica

    (University of Brescia, Big & Open Data Innovation (BODaI) Laboratory, C.da S. Chiara, 50, Brescia IT 25122, Italy)

  • Zuccolotto Paola

    (University of Brescia, Big & Open Data Innovation (BODaI) Laboratory, C.da S. Chiara, 50, Brescia IT 25122, Italy)

Abstract

Because of the advent of GPS techniques, a wide range of scientific literature on Sport Science is nowadays devoted to the analysis of players’ movement in relation to team performance in the context of big data analytics. A specific research question regards whether certain patterns of space among players affect team performance, from both an offensive and a defensive perspective. Using a time series of basketball players’ coordinates, we focus on the dynamics of the surface area of the five players on the court with a two-fold purpose: (i) to give tools allowing a detailed description and analysis of a game with respect to surface areas dynamics and (ii) to investigate its influence on the points made by both the team and the opponent. We propose a three-step procedure integrating different statistical modelling approaches. Specifically, we first employ a Markov Switching Model (MSM) to detect structural changes in the surface area. Then, we perform descriptive analyses in order to highlight associations between regimes and relevant game variables. Finally, we assess the relation between the regime probabilities and the scored points by means of Vector Auto Regressive (VAR) models. We carry out the proposed procedure using real data and, in the analyzed case studies, we find that structural changes are strongly associated to offensive and defensive game phases and that there is some association between the surface area dynamics and the points scored by the team and the opponent.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:jqsprt:v:14:y:2018:i:3:p:117-130:n:2
    DOI: 10.1515/jqas-2018-0041
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    Citations

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    Cited by:

    1. Rodolfo Metulini & Giorgio Gnecco, 2023. "Measuring players’ importance in basketball using the generalized Shapley value," Annals of Operations Research, Springer, vol. 325(1), pages 441-465, June.
    2. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, June.
    3. Marco Sandri & Paola Zuccolotto & Marica Manisera, 2020. "Markov switching modelling of shooting performance variability and teammate interactions in basketball," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1337-1356, November.
    4. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
    5. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2023. "Spatial performance analysis in basketball with CART, random forest and extremely randomized trees," Annals of Operations Research, Springer, vol. 325(1), pages 495-519, June.
    6. Tullio Facchinetti & Rodolfo Metulini & Paola Zuccolotto, 2023. "Filtering active moments in basketball games using data from players tracking systems," Annals of Operations Research, Springer, vol. 325(1), pages 521-538, June.
    7. Manlio Migliorati & Marica Manisera & Paola Zuccolotto, 2023. "Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 271-293, March.
    8. 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.
    9. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2021. "Spatial Performance Indicators and Graphs in Basketball," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 725-738, August.

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