  We propose a Bayesian nonparametric matrix clustering approach to analyze the
latent heterogeneity structure in the shot selection data collected from
professional basketball players in the National Basketball Association (NBA).
The proposed method adopts a mixture of finite mixtures framework and fully
utilizes the spatial information via a mixture of matrix normal distribution
representation. We propose an efficient Markov chain Monte Carlo algorithm for
posterior sampling that allows simultaneous inference on both the number of
clusters and the cluster configurations. We also establish large sample
convergence properties for the posterior distribution. The excellent empirical
performance of the proposed method is demonstrated via simulation studies and
an application to shot chart data from selected players in the 2017 18 NBA
regular season.
