Abstract
The NCAA Division I Men's Basketball Committee annually selects its national championship tournament's at-large invitees, and assigns seeds to all participants. As part of its deliberations, the Committee is provided a so-called "nitty-gritty report" for each team, containing numerous team performance statistics. Many elements of this report receive a great deal of attention by the media and fans as the tournament nears, including a team's Ratings Percentage Index (or RPI), overall record, conference record, non-conference record, strength of schedule, record in its last 10 games, etc. However, few previous studies have evaluated the degree to which these factors are related to whether a team actually wins games once the tournament begins. Using nitty-gritty information for the participants in the 638 tournament games during the 10 seasons from 1999 through 2008, we use stepwise binary logit regression to build a model that includes only eight of the 32 nitty-gritty factors we examined. We find that in some cases factors that receive a great deal of attention are not related to game results, at least in the presence of the more highly related set of factors included in the model.
Original language | American English |
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Article number | 8 |
Pages (from-to) | 1-27 |
Number of pages | 27 |
Journal | Journal of Quantitative Analysis in Sports |
Volume | 5 |
Issue number | 3 |
State | Published - 2009 |
Keywords
- binary logit
- committee decision
- performance metrics
- stepwise
Disciplines
- Business
- Business Administration, Management, and Operations
- Management Sciences and Quantitative Methods