Zero Inflated Poisson Model with Clustered Regression Coefficients: an Application to Heterogeneity Learning of Field Goal Attempts of Professional Basketball Players

Abstract

Although basketball is a dynamic process sport, with 5 plus 5 players competing on both offense and defense simultaneously, learning some static information is predominant for professional players, coaches and team mangers. In order to have a deep understanding of field goal attempts among different players, we propose a zero inflated Poisson model with clustered regression coefficients to learn the shooting habits of different players over the court and the heterogeneity among them. Specifically, the zero inflated model recovers the large proportion of the court with zero field goal attempts, and the mixture of finite mixtures model learn the heterogeneity among different players based on clustered regression coefficients and inflated probabilities. Both theoretical and empirical justification through simulation studies validate our proposed method. We apply our proposed model to the National Basketball Association (NBA), for learning players' shooting habits and heterogeneity among different players over the 2017--2018 regular season. This illustrates our model as a way of providing insights from different aspects.

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• Natural Scales in Geographical Patterns
• Bayesian Group Learning for Shot Selection of Professional Basketball Players
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• Spatial Clustering Regression of Count Value Data via Bayesian Mixture of Finite Mixtures
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Cited by in corpus (1)

• Bayesian clustering of multiple zero-inflated outcomes