报告题目：What could statistics offer for sports analytics?
报告人：胡冠宇（美国密苏里大学）
报告时间：2023年6月16日14:00-15:00
报告地点：文波楼401
摘要：Sports analytics are applications of data science to decision-making in all aspects of sports. As important as player/team performance evaluations are, there are also a wide range of sports analytics problems beyond this category. In this talk, two topics will be included. The first topic of today’s talk is heterogeneity learning of NBA players. 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. In the second part of today’s talk, I will discuss a novel causal inference approach to study the causal effect of home field advantage in English Premier League. We develop a hierarchical causal model and show that both league level and team level causal effects are identifiable and can be conveniently estimated.
报告人简介：
胡冠宇，美国密苏里大学助理教授。2017年毕业于佛罗里达州立大学统计系，获得统计学博士学位。2017-2022年在康涅狄格大学从事博士后研究。主要研究方向是非参数贝叶斯统计方法，空间统计学，因果推断以及其在体育分析，临床推断，教育统计和环境科学上的应用。胡博士现在担任 Biometrics, Statistics and its Interface，Environmental and Ecological Statistics，和 New England Journal of Data Science in Statistics 等期刊的副主编。
