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College Football Rankings and Market Efficiency

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  • Ray Fair
  • John Oster

Abstract

The results in this paper show that various college football ranking systems have useful independent information for predicting the outcomes of games. Optimal weights for the systems are estimated, and the use of these weights produces a predictive system that is more accurate than any of the individual systems. The results also provide a fairly precise estimate of the size of the home field advantage. These results may be of interest to the Bowl Championship Series in choosing which teams to play in the national championship game. The results also show, however, that none of the systems, including the optimal combination, contains any useful information that is not in the final Las Vegas point spread. It is argued in the paper that this is a fairly strong test of the efficiency of the college football betting market.

Suggested Citation

  • Ray Fair & John Oster, 2002. "College Football Rankings and Market Efficiency," Yale School of Management Working Papers amz2377, Yale School of Management, revised 01 Aug 2007.
  • Handle: RePEc:ysm:somwrk:amz2377
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    File URL: http://icfpub.som.yale.edu/publications/2377
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    References listed on IDEAS

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    1. Woodland, Linda M & Woodland, Bill M, 1994. " Market Efficiency and the Favorite-Longshot Bias: The Baseball Betting Market," Journal of Finance, American Finance Association, vol. 49(1), pages 269-279, March.
    2. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    3. Camerer, Colin F, 1989. "Does the Basketball Market Believe in the 'Hot Hand'?," American Economic Review, American Economic Association, vol. 79(5), pages 1257-1261, December.
    4. Gray, Philip K & Gray, Stephen F, 1997. " Testing Market Efficiency: Evidence from the NFL Sports Betting Market," Journal of Finance, American Finance Association, vol. 52(4), pages 1725-1737, September.
    5. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    6. John M. Gandar & William H. Dare & Craig R. Brown & Richard A. Zuber, 1998. "Informed Traders and Price Variations in the Betting Market for Professional Basketball Games," Journal of Finance, American Finance Association, vol. 53(1), pages 385-401, February.
    7. Sauer, Raymond D, et al, 1988. "Hold Your Bets: Another Look at the Efficiency of the Gambling Market for National Football League Games: Comment," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 206-213, February.
    8. Dare, William H. & MacDonald, S. Scott, 1996. "A generalized model for testing the home and favorite team advantage in point spread markets," Journal of Financial Economics, Elsevier, vol. 40(2), pages 295-318, February.
    9. Lyn D. Pankoff, 1968. "Market Efficiency and Football Betting," The Journal of Business, University of Chicago Press, vol. 41, pages 203-203.
    10. Avery, Christopher & Chevalier, Judith, 1999. "Identifying Investor Sentiment from Price Paths: The Case of Football Betting," The Journal of Business, University of Chicago Press, vol. 72(4), pages 493-521, October.
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    Cited by:

    1. Trevon D. Logan, 2007. "Whoa, Nellie! Empirical Tests of College Football's Conventional Wisdom," NBER Working Papers 13596, National Bureau of Economic Research, Inc.
    2. Trandel Gregory A & Maxcy Joel G, 2011. "Adjusting Winning-Percentage Standard Deviations and a Measure of Competitive Balance for Home Advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-17, January.
    3. Radek Janhuba, 2016. "Do Victories and Losses Matter? Effects of Football on Life Satisfaction," CERGE-EI Working Papers wp579, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

    More about this item

    Keywords

    Football Rankings; Predictive Information;

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