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Title: Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach Authors:  Guanyu Hu - University of Missouri Columbia (United States) [presenting]
Abstract: A model-based clustering approach for matrix response data is developed to analyze the underlying heterogeneity structure of shot selection among professional basketball players in the NBA. Particularly, we propose a mixture of finite mixtures (MFM) model for heterogeneity learning. Our proposed method estimates the number of clusters and cluster configurations simultaneously. The theoretical properties of our proposed method are established. An efficient Markov Chain Monte Carlo (MCMC) algorithm is designed for our proposed model. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze shot charts of selected players in the NBAs 2017-2018 regular season.