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Zero‐inflated Poisson model with clustered regression coefficients: Application to heterogeneity learning of field goal attempts of professional basketball players
Author(s) -
Hu Guanyu,
Yang HouCheng,
Xue Yishu,
Dey Dipak K.
Publication year - 2023
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11684
Subject(s) - basketball , poisson regression , field (mathematics) , poisson distribution , zero inflated model , regression analysis , econometrics , zero (linguistics) , computer science , regression , statistics , mathematics , psychology , geography , population , linguistics , philosophy , demography , archaeology , sociology , pure mathematics
Although basketball is a dynamic process sport, played between two sides of five players each, learning some static information is essential for professional players, coaches, and team managers. 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 captures a large portion of the court with zero field goal attempts, and the mixture of finite mixtures model captures 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 data from 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.