Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach

We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in shot selection data collected from professional basketball players National Basketball Association (NBA). The proposed method adopts mixture of finite mixtures framework and fully uses spatial information via normal distribution representation. an efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both number clusters cluster configurations. also establish large-sample convergence properties distribution. compelling empirical performance is demonstrated simulation studies application chart selected NBAs 2017–2018 regular season. Supplementary materials this article are available online.