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A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament: Updated Results from 2007

Published/Copyright: April 28, 2008
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Journal of Quantitative Analysis in Sports
From the journal Journal of Quantitative Analysis in Sports Volume 4 Issue 2

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 from 2006 and 2007. Given that the proposed method can incorporate several different predictors of success in the NCAA tournament when calculating a rating, and is shown to have better predictive power than a model-based approach, it should be considered as an alternative to other rating methods currently used to assign seeds and regions to the teams selected to play in the tournament. The predictive power of the model-based simulation approach is also discussed, given the success of this approach in 2007. The paper concludes with limitations and directions for future work in this area.

Keywords: quantitative reasoning; ratings; sports statistics; NCAA basketball
Published Online: 2008-4-28

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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https://doi.org/10.2202/1559-0410.1099
Keywords for this article
quantitative reasoning; ratings; sports statistics; NCAA basketball

Articles in the same Issue

  1. Conference Paper
  2. New England Symposium on Statistics in Sports
  3. Estimating Situational Effects on OPS
  4. Why On-Base Percentage is a Better Indicator of Future Performance than Batting Average: An Algebraic Proof
  5. Improving Major League Baseball Park Factor Estimates
  6. In Search of the "Last-Ups" Advantage in Baseball: A Game-Theoretic Approach
  7. The Role of Rest in the NBA Home-Court Advantage
  8. Racial Bias in the NBA: Implications in Betting Markets
  9. A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament: Updated Results from 2007
  10. The Passing Premium Puzzle Revisited
  11. Isolating the Effect of Individual Linemen on the Passing Game in the National Football League
  12. Probability and Statistical Models for Racing
  13. Improving Golf Instruction with the iClub Motion Capture Technology
  14. Composite Poisson Models for Goal Scoring
  15. Skill Evaluation in Women's Volleyball
  16. Probability Formulas and Statistical Analysis in Tennis
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