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A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament


  • West Brady T

    (University of Michigan, Ann Arbor)


This paper first presents a brief review of potential rating tools and methods for predicting success in the NCAA basketball tournament, including those methods (such as the Ratings Percentage Index, or RPI) that receive a great deal of weight in selecting and seeding teams for the tournament. The paper then proposes a simple and flexible rating method based on ordinal logistic regression and expectation (the OLRE method) that is designed to predict success for those teams selected to participate in the NCAA tournament. A simulation based on the parametric Bradley-Terry model for paired comparisons is used to demonstrate the ability of the computationally simple OLRE method to predict success in the tournament, using actual NCAA tournament data. Given that the proposed method can incorporate several different predictors of success in the NCAA tournament when calculating a rating, and has better predictive power than a model-based approach, it should be strongly considered as an alternative to other rating methods currently used to assign seeds and regions to the teams selected to play in the tournament.

Suggested Citation

  • West Brady T, 2006. "A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-16, July.
  • Handle: RePEc:bpj:jqsprt:v:2:y:2006:i:3:n:3

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    1. repec:bla:coecpo:v:35:y:2017:i:4:p:658-676 is not listed on IDEAS
    2. repec:ebl:ecbull:v:4:y:2007:i:34:p:1-7 is not listed on IDEAS
    3. B. Jay Coleman & J. Michael DuMond & Allen K. Lynch, 2010. "Evidence of bias in NCAA tournament selection and seeding," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 431-452.
    4. Coleman Jay & Lynch Allen K, 2009. "NCAA Tournament Games: The Real Nitty-Gritty," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-27, July.
    5. West Brady T & Lamsal Madhur, 2008. "A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-21, July.
    6. Hoegh Andrew & Carzolio Marcos & Crandell Ian & Hu Xinran & Roberts Lucas & Song Yuhyun & Leman Scotland C., 2015. "Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 29-37, March.

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