报告题目：What could statistics offer for sports analytics?

报 告 人： 胡冠宇 教授 （University of Texas）

报告时间：2023年12月20 日 9:30—

报告地点： 格物楼数学研究中心528报告厅

报告摘要：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：Prof. Hu’s research mainly focuses on Bayesian nonparametric methods, spatial and spatio-temporal statistics, point process, and causal inference. Prof. Hu has also worked on the analysis of clinical trials, spatial transcriptomic, regional economics, environmental science, educational measurements, and sports data. Now, Prof. Hu is the associate editor of Biometrics, Environmental and Ecological Statistics, Statistics and its interface, and he also serves as the Chair of ASA statistics in sports section and the Program Chair of ISBA East Asia Chapter. Dr. Hu is elected member of ISI.