论文标题
贝叶斯小组学习专业篮球运动员的射门
Bayesian Group Learning for Shot Selection of Professional Basketball Players
论文作者
论文摘要
在本文中，我们开发了一种小组学习方法，以分析NBA职业篮球运动员中射击选择的潜在异质性结构。我们提出了有限混合物（MFM）模型的混合物，以根据log Gaussian Cox工艺（LGCP）捕获不同玩家之间的射击选择的异质性。我们提出的方法可以同时估计组和组配置的数量。为我们提出的模型开发了有效的马尔可夫链蒙特卡洛（MCMC）算法。已经进行了模拟研究以证明其性能。最终，我们提出的学习方法在分析NBA 2017-2018常规赛中几位球员的射门图中进一步说明了。
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 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. Ultimately, our proposed learning approach is further illustrated in analyzing shot charts of several players in the NBA's 2017-2018 regular season.
