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Minimizing Game Score Violations in College Football Rankings

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  • B. Jay Coleman

    (Department of Management, Marketing, and Logistics, Coggin College of Business, University of North Florida, 4567 St. Johns Bluff Road, South, Jacksonville, Florida 32224-2645)

Abstract

One metric used to evaluate the myriad ranking systems in college football is retrodictive accuracy. Maximizing retrodictive accuracy is equivalent to minimizing game score violations: the number of times a past game’s winner is ranked behind its loser. None of the roughly 100 current ranking systems achieves this objective. Using a model for minimizing violations that exploits problem characteristics found in college football, I found that all previous ranking systems generated violations that were at least 38 percent higher than the minimum. A minimum-violations criterion commonly would have affected the consensus top five and changed participants in the designated national championship game in 2000 and 2001—but not in the way most would have expected. A final regular season ranking using the model was perhaps the best prebowl ranking published online in 2004, as it maximized retrodictive accuracy and was nearly the best at predicting the 28 bowl winners.

Suggested Citation

  • B. Jay Coleman, 2005. "Minimizing Game Score Violations in College Football Rankings," Interfaces, INFORMS, vol. 35(6), pages 483-496, December.
    • Handle: RePEc:inm:orinte:v:35:y:2005:i:6:p:483-496
    DOI: 10.1287/inte.1050.0172
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    References listed on IDEAS

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    1. Joseph Martinich, 2002. "College Football Rankings: Do the Computers Know Best?," Interfaces, INFORMS, vol. 32(5), pages 85-94, October.
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    Cited by:

    1. Sebastián Cea & Guillermo Durán & Mario Guajardo & Denis Sauré & Joaquín Siebert & Gonzalo Zamorano, 2020. "An analytics approach to the FIFA ranking procedure and the World Cup final draw," Annals of Operations Research, Springer, vol. 286(1), pages 119-146, March.
    2. B. Jay Coleman & Andres Gallo & Paul M. Mason & Jeffrey W. Steagall, 2010. "Voter Bias in the Associated Press College Football Poll," Journal of Sports Economics, , vol. 11(4), pages 397-417, August.
    3. Shai Bernstein & Eyal Winter, 2012. "Contracting with Heterogeneous Externalities," American Economic Journal: Microeconomics, American Economic Association, vol. 4(2), pages 50-76, May.
    4. G Kendall, 2008. "Scheduling English football fixtures over holiday periods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 743-755, June.
    5. Buchman Susan & Kadane Joseph B., 2008. "Reweighting the Bowl Championship Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-13, July.
    6. Csató, László, 2013. "Rangsorolás páros összehasonlításokkal. Kiegészítések a felvételizői preferencia-sorrendek módszertanához [Paired comparisons ranking. A supplement to the methodology of application-based preferenc," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 1333-1353.
    7. Burer Samuel, 2012. "Robust Rankings for College Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-22, June.
    8. B. Jay Coleman, 2014. "Minimum violations and predictive meta‐rankings for college football," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 17-33, February.
    9. Miles William W & Fowks Gary T & Coulter Lisa O, 2010. "AccuV College Football Ranking Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-17, July.

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