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An Improved LRMC Method for NCAA Basketball Prediction

Author

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  • Brown Mark

    (City College, City University of New York)

  • Sokol Joel

    (Georgia Institute of Technology)

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.

Suggested Citation

  • Brown Mark & Sokol Joel, 2010. "An Improved LRMC Method for NCAA Basketball Prediction," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-23, July.
    • Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:3:n:4
    DOI: 10.2202/1559-0410.1202
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    Cited by:

    1. Phillips Andrew J. K., 2014. "Uncovering Formula One driver performances from 1950 to 2013 by adjusting for team and competition effects," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-18, June.
    2. Leman Scotland C. & House Leanna & Szarka John & Nelson Hayley, 2014. "Life on the bubble: Who’s in and who’s out of March Madness?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-14, September.

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