大学篮球比赛中主队获胜概率的贝叶斯估计

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of Quantitative Analysis in Sports Pub Date : 2022-04-25 DOI:10.1515/jqas-2021-0086

Jason Maddox, Ryan Sides, Jane L. Harvill

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引用次数: 2

摘要

摘要提出了两种新的贝叶斯方法来估计和预测NCAA男子大学篮球甲级联赛中主队获胜概率。第一种方法有一个先验，调整为铅差和时间流逝的函数。第二种方法是第一种方法的调整版本，其中调整是贝叶斯估计器与时间加权赛前获胜概率的线性组合。将所提出的方法与现有方法进行了比较，表明新方法在估计和预测方面与现有方法具有竞争力或优于现有方法。该实用程序通过2012/2013至2019/2020 NCAA一级男子篮球赛季的应用程序进行了说明。

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Bayesian estimation of in-game home team win probability for college basketball

Abstract Two new Bayesian methods for estimating and predicting in-game home team win probabilities in Division I NCAA men’s college basketball are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pregame win probability. The proposed methods are compared to existing methods, showing the new methods are competitive with or outperform existing methods for both estimation and prediction. The utility is illustrated via an application to the 2012/2013 through the 2019/2020 NCAA Division I Men’s Basketball seasons.

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来源期刊

Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

2.00

自引率

12.50%

发文量

期刊介绍： The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.