Modelling the dynamic pattern of surface area in basketball and its effects on team performance
-
Rodolfo Metulini
,
Marica Manisera
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.
Acknowledgement
The authors are grateful to the anonymous reviewers for their valuable comments, which greatly improved the paper, and also thank MYagonism (www.myagonism.com) for having supplied the data. A special thanks goes to Raffaele Imbrogno (“Foro Italico” University, Roma IV), Paolo Raineri (MYagonism) for fruitful discussions, and to Tullio Facchinetti (University of Pavia) for the help with data manipulation. We also thank Giuseppe Arbia (Catholic University of the Sacred Heart) and Marcello Chiodi (University of Palermo) for useful comments at the SIS 2017 conference. Research carried out in collaboration with the Big & Open Data Innovation Laboratory (BODaI-Lab), University of Brescia (project nr. 03-2016, title “Big Data Analytics in Sports”, bodai.unibs.it/bdsports), granted by Fondazione Cariplo and Regione Lombardia.
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©2018 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A network diffusion ranking family that includes the methods of Markov, Massey, and Colley
- Quantifying the probability of a shot in women’s collegiate soccer through absorbing Markov chains
- Modelling the dynamic pattern of surface area in basketball and its effects on team performance
- A Bayesian regression approach to handicapping tennis players based on a rating system
- Bayesian hierarchical models for predicting individual performance in soccer
Articles in the same Issue
- Frontmatter
- A network diffusion ranking family that includes the methods of Markov, Massey, and Colley
- Quantifying the probability of a shot in women’s collegiate soccer through absorbing Markov chains
- Modelling the dynamic pattern of surface area in basketball and its effects on team performance
- A Bayesian regression approach to handicapping tennis players based on a rating system
- Bayesian hierarchical models for predicting individual performance in soccer
