Modeling the NCAA Tournament Through Bayesian Logistic Regression

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2012

Author dc:creator

• Nelson, Bryan

Contributors dc:contributor

• Eric Ruggieri
• John Kern
• Stacey Levine

• Bayesian
• Logistic regression
• MCMC
• NCAA

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/970

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1986