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

Author

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  • West Brady T

    (University of Michigan, Ann Arbor)

Abstract

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
    DOI: 10.2202/1559-0410.1039
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    Citations

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    Cited by:

    1. Karl Andrew T., 2012. "The Sensitivity of College Football Rankings to Several Modeling Choices," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-44, October.
    2. Daniel C. Hickman & Andrew G. Meyer, 2017. "Does Athletic Success Influence Persistence At Higher Education Institutions? New Evidence Using Panel Data," Contemporary Economic Policy, Western Economic Association International, vol. 35(4), pages 658-676, October.
    3. 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.
    4. 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.
    5. repec:ebl:ecbull:v:4:y:2007:i:34:p:1-7 is not listed on IDEAS
    6. 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.
    7. Daniel F. Stone & Jeremy Arkes, 2018. "March Madness? Underreaction To Hot And Cold Hands In Ncaa Basketball," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1724-1747, July.
    8. Ludden Ian G. & Khatibi Arash & King Douglas M. & Jacobson Sheldon H., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
    9. 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.
    10. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
    11. Cassey Lee, 2007. "A Cheap Ticket to the Dance: Systematic Bias in College Basketball's Ratings Percentage Index," Economics Bulletin, AccessEcon, vol. 4(34), pages 1-7.
    12. Gupta Ajay Andrew, 2015. "A new approach to bracket prediction in the NCAA Men’s Basketball Tournament based on a dual-proportion likelihood," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 53-67, March.

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