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A Consistent Weighted Ranking Scheme with an Application to NCAA College Football Rankings

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  • Fershtman, Chaim
  • Gandal, Neil
  • Fainmesser, Itay

Abstract

The NCAA college football ratings, in which the "so-called" national champion is determined, has been plagued by controversies the last few years. The difficulty arises because there is a need to make a complete ranking of teams even though each team has a different schedule of games with a different set of opponents. A similar problem arises whenever one wants to establish a ranking of patents or academic journals, etc. in which the raw data are (incomplete) bilateral citations or interactions among objects. This paper develops and estimates a simple consistent weighted ranking (CWR) scheme which, in the sports world, depends on four parameters (winning vs. losing and the relative importance of home vs. away games). In most ranking problems, there are not explicit criteria to evaluate the success of proposed rankings. NCAA college football has a special structure that enables the evaluation of each ranking scheme. Each season is essentially divided into two parts: the regular season and the post season bowl games. If a ranking scheme is accurate it should correctly predict a relatively large number of the bowl game outcomes. We use this structure to estimate the four parameters of our ranking function using "historical" data from the 1999-2003 seasons.

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  • Fershtman, Chaim & Gandal, Neil & Fainmesser, Itay, 2005. "A Consistent Weighted Ranking Scheme with an Application to NCAA College Football Rankings," CEPR Discussion Papers 5239, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:5239
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    Cited by:

    1. Jason A. Winfree, 2021. "If You Don'T Like The Outcome, Change The Contest," Economic Inquiry, Western Economic Association International, vol. 59(1), pages 329-343, January.
    2. Liam J. A. Lenten, 2008. "Unbalanced Schedules And The Estimation Of Competitive Balance In The Scottish Premier League," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(4), pages 488-508, September.
    3. Jason A. Winfree, 2020. "Rivalries, Bowl Eligibility, and Scheduling Effects in College Football," Journal of Sports Economics, , vol. 21(5), pages 477-492, June.
    4. 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.
    5. 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.
    6. 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.
    7. Liam J. A. Lenten, 2015. "Measurement of Competitive Balance in Conference and Divisional Tournament Design," Journal of Sports Economics, , vol. 16(1), pages 3-25, January.
    8. E. Woodrow Eckard, 2013. "Is the Bowl Championship Series a Cartel? Some Evidence," Journal of Sports Economics, , vol. 14(1), pages 3-22, February.
    9. 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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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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