A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings
AbstractThis paper begins with a thorough review of previous quantitative literature dedicated to the development of ratings for college and professional football teams, and also considers various methods that have been proposed for predicting the outcomes of future football games. Building on this literature, the paper then presents a straightforward application of linear modeling in the development of a predictive model for the outcomes of college football bowl games, and identifies important team-level predictors of actual bowl outcomes in 2007-2008 using real Football Bowl Subdivision (FBS) data from the recently completed 2004-2006 college football seasons. Given that Bowl Championship Series (BCS) ratings are still being used to determine the teams most eligible to play for a national championship and a playoff system for determining a national champion is not yet a reality, the predictive model is then applied in a novel method for the calculation of ratings for selected teams, based on a round-robin playoff scenario. The paper also considers additional possible applications of the proposed methods, and concludes with current limitations and directions for future work in this area.
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Bibliographic InfoArticle provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.
Volume (Year): 4 (2008)
Issue (Month): 3 (July)
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Web page: http://www.degruyter.com
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- Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
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