NDLI: An Improved LRMC Method for NCAA Basketball Prediction

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An Improved LRMC Method for NCAA Basketball Prediction

Content Provider	Scilit
Author	Brown, Mark Sokol, Joel
Copyright Year	2010
Abstract	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.
Related Links	http://www.degruyter.com/downloadpdf/j/jqas.2010.6.3/jqas.2010.6.3.1202/jqas.2010.6.3.1202.xml
ISSN	21946388
e-ISSN	15590410
DOI	10.2202/1559-0410.1202
Journal	Journal of Quantitative Analysis in Sports
Issue Number	3
Volume Number	6
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2010-01-19
Access Restriction	Open
Subject Keyword	Journal of Quantitative Analysis in Sports Transportation Logistic Regression Model Statistical Significance Markov Chain Model Ordinary Least Square Markov Chain Seasonality Logistic Regression Journal: Journal of Quantitative Analysis in Sports, Vol- 6, Issue- 1
Content Type	Text
Resource Type	Article
Subject	3300/3301 1800/1801

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