In this paper, we develop a group learning approach to analyze the underlying heterogeneity structure of shot selection among professional basketball players in the NBA. We propose a mixture of finite mixtures (MFM) model to capture the heterogeneity of shot selection among different players based on the Log Gaussian Cox process (LGCP). Our proposed method can simultaneously estimate the number of groups and group configurations. An efficient Markov Chain Monte Carlo (MCMC) algorithm is developed for our proposed model. Simulation studies have been conducted to demonstrate its performance. Finally, our proposed learning approach is further illustrated in analyzing shot charts of selected players in the NBA's 2017–2018 regular season.

中文翻译：

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贝叶斯小组学习为职业篮球运动员的投篮选择

在本文中，我们开发了一种小组学习方法来分析NBA职业篮球运动员投篮选择的潜在异质性结构。我们提出了一种有限混合混合（MFM）模型，以基于对数高斯考克斯（LGCP）捕获不同玩家之间镜头选择的异质性。我们提出的方法可以同时估计组数和组配置。针对我们提出的模型，开发了一种有效的马尔可夫链蒙特卡洛（MCMC）算法。已经进行了仿真研究以证明其性能。最后，我们建议的学习方法在分析NBA 2017-2018常规赛季选定球员的射门图表时得到进一步说明。