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An Improved LRMC Method for NCAA Basketball Prediction
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Mark Brown
Published/Copyright:
July 19, 2010
The LRMC method for predicting NCAA Tournament results from regular-season game outcomes is a two-part process consisting of a logistic regression model to estimate head-to-head differences in team strength, followed by a Markov chain model to combine those differences into an overall ranking. We consider replacing each of the two parts of LRMC with alternative models, empirical Bayes and ordinary least squares, that attempt to accomplish the same goal. Computational results show that replacing the logistic regression with either of two empirical Bayes models yields a statistically-significant improvement when the probabilities are jointly conditioned.
Keywords: LRMC; logistic regression; Markov chain; empirical Bayes; college basketball; NCAA basketball
Published Online: 2010-7-19
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
LRMC;
logistic regression;
Markov chain;
empirical Bayes;
college basketball;
NCAA basketball
Articles in the same Issue
- Article
- Home Advantage in Three National Netball Competitions: Australia (1997-2007), New Zealand (1998-2007) and England (2005/06-2008/09)
- Relative Importance of Performance Factors in Winning NBA Games in Regular Season versus Playoffs
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- An Improved LRMC Method for NCAA Basketball Prediction
- Bayesian Modeling of Footrace Finishing Times
- Rating/Ranking Systems, Post-Season Bowl Games, and "The Spread"
- A New Approach in the Evaluation of Team Chess Championships Rankings
- A Point-Mass Mixture Random Effects Model for Pitching Metrics
- Tail Modeling, Track and Field Records, and Bolt's Effect
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- Validation of Match Notation (A Coding System) in Tennis
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- Scoring Variables and Judge Bias in United States Dressage Competitions
- The "Bradman Class": An Exploration of Some Issues in the Evaluation of Batsmen for Test Matches, 1877-2006
