We propose a multidimensional tensor clustering approach for studying how professional basketball players' shooting patterns vary over court locations and game time. Unlike most existing methods that only study continuous-valued tensors or have to assume the same cluster structure along different tensor directions, we propose a Bayesian nonparametric model that deals with count-valued tensors and projects the heterogeneity among players onto tensor dimensions while allowing cluster structures to be different over directions. Our method is fully probabilistic; hence allows simultaneous inference on both the number of clusters and the cluster configurations. We present an efficient posterior sampling method and establish the large-sample convergence properties for the posterior distribution. Simulation studies have demonstrated an excellent empirical performance of the proposed method. Finally, an application to shot chart data collected from 191 NBA players during the 2017-2018 regular season is conducted and reveals several interesting insights for basketball analytics.

翻译：我们提出一个多维的分类方法,用于研究专业篮球运动员的射击模式如何因法庭地点和游戏时间的不同而不同。与大多数仅研究连续价值高压球员或不得不在不同高压方向上承担相同集群结构的现有方法不同,我们提议了一个巴伊西亚非参数模型,处理计价高压球员之间的异质,并将球员的异质投射到高压层面,同时允许群集结构在方向上不同。我们的方法完全具有概率性,因此可以同时推断群集的数量和群集配置。我们提出了一个高效的后方取样方法,并为后方分布建立了大型抽样聚合特性。模拟研究表明,拟议方法的经验表现极佳。最后,对2017-2018年定期季节从191个NBA球队员收集的数据进行了图表拍摄应用,并揭示了篮球分析学的一些有趣的洞察力。