报告题目：IN-GAME WIN PROBABILITIES FOR THE NATIONAL RUGBY LEAGUE
报 告 人：曹际国
报告人简介：曹际国博士,加拿大温哥华西蒙弗雷泽大学(Simon Fraser University)统计与精算系教授，加拿大数据科学国家特聘教授(Canada Research Chair in Data Science)，现担任Biometrics, Statistics in Medicine等国际主要统计期刊副主编。曹际国2006年获得加拿大麦吉尔大学(McGill University)博士，2007年美国耶鲁大学博士后出站，长期从事人工智能，机器学习，函数型数据分析(functional data analysis)和估计微分方程的研究。曹际国近些年来在国际学术期刊中发表超过100篇文章。曹际国2021年获得加拿大统计协会(Statistical Society of Canada)和国家数学研究中心(Centre de recherches mathématiques)联合评比的最高奖之一：加拿大国家杰出青年统计学家奖(CRM-SSC award)。
摘要：We develop new methods for providing instantaneous in-game win probabilities for the National Rugby League. Besides the score differential, betting odds, and real-time features extracted from the match event data are also used as inputs to inform the in-game win probabilities. Rugby matches evolve continuously in time, and the circumstances change over the duration of the match. Therefore, the match data are considered as functional data, and the in-game win probability is a function of the time of the match. We express the in-game win probability using a conditional probability formulation, the components of which are evaluated from the perspective of functional data analysis. Specifically, we model the score differential process and functional feature extracted from the match event data as sums of mean functions and noises. The mean functions are approximated by B-spline basis expansions with functional parameters. Since each match is conditional on a unique kickoff win probability of the home team obtained from the betting odds (i.e., the functional data are not independent and identically distributed), we propose a weighted least squares method to estimate the functional parameters by borrowing the information from matches with similar kickoff win probabilities. The variance and covariance elements are obtained by the maximum likelihood estimation method. The proposed method is applicable to other sports when suitable match event data are available.