  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.
