论文标题
带有聚类回归系数的零载泊泊尼模型：专业篮球运动员的射门得分尝试的异质性学习的应用
Zero Inflated Poisson Model with Clustered Regression Coefficients: an Application to Heterogeneity Learning of Field Goal Attempts of Professional Basketball Players
论文作者
论文摘要
尽管篮球是一项充满活力的进程运动，但有5位加5名球员同时在进攻和防守方面竞争，但学习一些静态信息对于专业球员，教练和团队漫不可及。为了深入了解不同玩家之间的射门得分尝试，我们提出了一个带有集群回归系数的零载泊托式模型，以学习法院不同参与者的射击习惯以及其中的异质性。具体而言，零夸大的模型以零射门射门尝试恢复了法院的很大比例，并且有限混合模型的混合物根据聚类的回归系数和易膨胀的概率来了解不同玩家之间的异质性。通过模拟研究的理论和经验理由都验证了我们提出的方法。我们将建议的模型应用于国家篮球协会（NBA），以学习球员的射击习惯和2017--2018常规赛的不同球员的异质性。这说明了我们的模型是从不同方面提供见解的一种方式。
Although basketball is a dynamic process sport, with 5 plus 5 players competing on both offense and defense simultaneously, learning some static information is predominant for professional players, coaches and team mangers. In order to have a deep understanding of field goal attempts among different players, we propose a zero inflated Poisson model with clustered regression coefficients to learn the shooting habits of different players over the court and the heterogeneity among them. Specifically, the zero inflated model recovers the large proportion of the court with zero field goal attempts, and the mixture of finite mixtures model learn the heterogeneity among different players based on clustered regression coefficients and inflated probabilities. Both theoretical and empirical justification through simulation studies validate our proposed method. We apply our proposed model to the National Basketball Association (NBA), for learning players' shooting habits and heterogeneity among different players over the 2017--2018 regular season. This illustrates our model as a way of providing insights from different aspects.
