Defense Date
6-20-2012
Graduation Date
2012
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
Eric Ruggieri
Committee Member
John Kern
Committee Member
Stacey Levine
Keywords
Bayesian, Logistic regression, MCMC, NCAA
Abstract
Many rating systems exist that order the Division I teams in Men's College Basketball that compete in the NCAA Tournament, such as seeding teams on an S-curve, and the Pomeroy and Sagarin ratings, simplifying the process of choosing winners to a comparison of two numbers. Rather than creating a rating system, we analyze each matchup by using the difference between the teams' individual regular season statistics as the independent variables. We use an MCMC approach and logistic regression along with several model selection techniques to arrive at models for predicting the winner of each game. When given the 63 actual games in the 2012 tournament, eight of our models performed as well as Pomeroy's rating system and four did as well as Sagarin's rating system when given the 63 actual games. Not allowing the models to fix their mistakes resulted in only one model outperforming both Pomeroy and Sagarin's systems.
Format
Language
English
Recommended Citation
Nelson, B. (2012). Modeling the NCAA Tournament Through Bayesian Logistic Regression (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/970