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A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings

Author

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  • West Brady T

    (University of Michigan at Ann Arbor)

  • Lamsal Madhur

    (University of Michigan at Flint)

Abstract

This 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.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:4:y:2008:i:3:n:3
    DOI: 10.2202/1559-0410.1115
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    References listed on IDEAS

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    1. Itay Fainmesser & Chaim Fershtman & Neil Gandal, 2009. "A Consistent Weighted Ranking Scheme With an Application to NCAA College Football Rankings," Journal of Sports Economics, , vol. 10(6), pages 582-600, December.
    2. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, vol. 19(2), pages 257-270.
    3. Stern H S, 2006. "In Favor of A Quantitative Boycott of the Bowl Championship Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-6, January.
    4. Mease D., 2003. "A Penalized Maximum Likelihood Approach for the Ranking of College Football Teams Independent of Victory Margins," The American Statistician, American Statistical Association, vol. 57, pages 241-248, November.
    5. Stern, Hal S., 2004. "Statistics and the College Football Championship," The American Statistician, American Statistical Association, vol. 58, pages 179-185, August.
    6. 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.
    7. Annis David H. & Craig Bruce A., 2005. "Hybrid Paired Comparison Analysis, with Applications to the Ranking of College Football Teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 1(1), pages 1-33, October.
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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.
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    4. 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.
    5. Wigness Maggie B & Williams Chadd C & Rowell Michael J, 2010. "A New Iterative Method for Ranking College Football Teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-15, April.

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