Predicting Major League Baseball Championship Winners through Data Mining

Predicting Major League Baseball Championship Winners through Data Mining

The world of sports is highly unpredictable. Fans of any sport are interested in predicting the outcomes of sporting events. Whether it is prediction based off of experience, a gut feeling, instinct, simulation based off of video games, or simple statistical measures, many fans develop their own approach to predicting the results of games. In many situations, these methods are not reliable and ...

Predicting the Major League Baseball Season

This paper attempts to predict the outcome of games from the 2012 Major League Baseball season. Sporting events are very important to many people, and professional leagues are worth billions of dollars. Baseball, in particular, is not only one of the most popular sports in the United States, but the vast amount of recorded data and statistics made publicly available also lends itself well to ma...

A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball

The probability of winning a game in major league baseball depends on various factors relating to team strength including the past performance of the two teams, the batting ability of the two teams and the starting pitchers. These three factors change over time. We combine these factors by adopting contribution parameters, and include a home field advantage variable in forming a two-stage Bayes...

Causal Inference and Major League Baseball

Customer-Based Discrimination in Major League Baseball?

We investigate the existence of customer-based discrimination in Major League Baseball using a direct measure of customer preference, allstar balloting. Unlike other measures used in the literature, such as trading-card prices, our data do not su er from hard to measure monetary in uences on preference. Using allstar votes from 1992 through 1996, and controlling for various player and team-spec...