CHOICE MODELS FOR PREDICTING DIVISIONAL WINNERS IN MAJOR-LEAGUE BASEBALL

被引：14

作者：

BARRY, D ^{[1
]}

HARTIGAN, JA ^{[1
]}

机构：

[1] YALE UNIV,NEW HAVEN,CT 06520

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 1993年 / 88卷 / 423期

关键词：

BASEBALL; CHANGE POINT MODELS; MARKOV CHAIN MONTE-CARLO;

D O I：

10.2307/2290761

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Major league baseball in the United States is divided into two leagues and four divisions. Each team plays 16 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.

引用

页码：766 / 774

页数：9

共 13 条

[1]

[Anonymous], 1979, MONTE CARLO METHODS

[2] AN EVALUATION OF MAJOR LEAGUE BASEBALL OFFENSIVE PERFORMANCE MODELS [J].