论文标题
通过贝叶斯矩阵聚类方法对职业篮球射门得分进行分析
Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach
论文作者
论文摘要
我们提出了一种贝叶斯非参数矩阵聚类方法，以分析从国家篮球协会（NBA）的职业篮球运动员收集的射门选择数据中的潜在异质性结构。提出的方法采用有限混合物框架的混合物，并通过矩阵正态分布表示的混合物充分利用空间信息。我们提出了一种有效的马尔可夫链蒙特卡洛算法，用于后抽样，该算法允许同时推断簇数和群集配置。我们还为后验分布建立了较大的样品收敛性。该方法的出色经验性能通过仿真研究以及2017年NBA常规赛中选定玩家的射击图表数据的应用来证明。
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.
