Choice Models for Predicting Divisional Winners in Major League Baseball
作者:
Daniel Barry,
J.A. Hartigan,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1993)
卷期:
Volume 88,
issue 423
页码: 766-774
ISSN:0162-1459
年代: 1993
DOI:10.1080/01621459.1993.10476337
出版商: Taylor & Francis Group
关键词: Baseball;Change point models;Markov chain Monte Carlo
数据来源: Taylor
摘要:
Major league baseball in the United States is divided into two leagues and four divisions. Each team plays 162 games against teams in the same league. The winner in each division is the team winning the most games of the teams in that division. We wish to predict the division winners based on games played up to any specified time. We use a generalized choice model for the probability of a team winning a particular game that allows for different strengths for each team, different home advantages, and strengths varying randomly with time. Future strengths and the outcomes of future games are simulated using Markov chain sampling. The probability of a particular team winning the division is then estimated by counting the proportion of simulated seasons in which it wins the most games. The method is applied to the 1991 National League season.
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