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

West Brady T
Additional contact information
West Brady T: University of Michigan, Ann Arbor

Journal of Quantitative Analysis in Sports, 2006, vol. 2, issue 3, 16

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.

Keywords: quantitative reasoning; ratings; sports statistics; NCAA basketball (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://doi.org/10.2202/1559-0410.1039 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:2:y:2006:i:3:n:3

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.2202/1559-0410.1039

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().