Date of Award

8-2012

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics

First Advisor

Russell S. Stocker, Ph.D.

Second Advisor

Christoph E. Maier, Ph.D.

Third Advisor

Yu-Ju Kuo, Ph.D.

Abstract

This study examines statistics in Major League Baseball and their effects on team wins. Frequentist and Bayesian approaches are compared and past research is evaluated. Using Bayesian analysis and logistic regression, models are created for runs scored, runs allowed, wins and order of divisional finish. The models are subsequently used to analyze the performance of the Pittsburgh Pirates in 2011 and to predict their performance in 2012. The results reveal that the Pirates outperformed their expectations in 2011 based on their underlying statistics and that they are expected to have their 20th consecutive losing season in 2012 and finish fifth in the National League Central Division.

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