Bayesball: A Bayesian hierarchical model for evaluating fielding in major league baseball

Bayesball: a Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball

The use of statistical modeling in baseball has received substantial attention recently in both the media and academic community. We focus on a relatively under-explored topic: the use of statistical models for the analysis of fielding based on high-resolution data consisting of on-field location of batted balls. We combine spatial modeling with a hierarchical Bayesian structure in order to eva...

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...

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...

Competitive Balance in Major League Baseball

The nature of Major League Baseball (MLB) requires competition in order to keep the business alive. In any sports league, the presence of competitors allows for the livelihood of the game through producing an event with an uncertain outcome. The current state of the game lacks the competitive balance necessary for a thriving industry. Much of this competitive imbalance possesses the underlying ...

Causal Inference and Major League Baseball