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We propose a multidimensional tensor clustering approach for studying howprofessional basketball players' shooting patterns vary over court locationsand game time. Unlike most existing methods that only study continuous-valuedtensors or have to assume the same cluster structure along different tensordirections, we propose a Bayesian nonparametric model that deals withcount-valued tensors and projects the heterogeneity among players onto tensordimensions while allowing cluster structures to be different over directions.Our method is fully probabilistic; hence allows simultaneous inference on boththe number of clusters and the cluster configurations. We present an efficientposterior sampling method and establish the large-sample convergence propertiesfor the posterior distribution. Simulation studies have demonstrated anexcellent empirical performance of the proposed method. Finally, an applicationto shot chart data collected from 191 NBA players during the 2017-2018 regularseason is conducted and reveals several interesting insights for basketballanalytics.

Multidimensional heterogeneity learning for count value tensor data with applications to field goal attempt analysis of NBA players