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A Test for Bias of Inherent Characteristics in Betting Markets

Author

Listed:
  • William H. Dare
  • Steven A. Dennis

Abstract

The authors develop a model to investigate potential biases of inherent characteristics in betting markets. The test requires only that there be both a sides (“spread†) market and a totals (“over/under†) market for the game. The authors utilize the model to test for the well-documented “home-underdog†bias in the National Football League (NFL). They show that the bias specifically favors the offense (or defense) of home underdogs (away-favorites), with no bias against the offense (defense) of away favorites (home-underdogs).

Suggested Citation

  • William H. Dare & Steven A. Dennis, 2011. "A Test for Bias of Inherent Characteristics in Betting Markets," Journal of Sports Economics, , vol. 12(6), pages 660-665, December.
    • Handle: RePEc:sae:jospec:v:12:y:2011:i:6:p:660-665
    DOI: 10.1177/1527002510395864
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    References listed on IDEAS

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    1. Linda M. Woodland & Bill M. Woodland, 2001. "Market Efficiency and Profitable Wagering in the National Hockey League: Can Bettors Score on Longshots?," Southern Economic Journal, John Wiley & Sons, vol. 67(4), pages 983-995, April.
    2. 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.
    3. Linda M. Woodland & Bill M. Woodland, 2003. "The Reverse Favourite–longshot Bias and Market Efficiency in Major League Baseball: An Update," Bulletin of Economic Research, Wiley Blackwell, vol. 55(2), pages 113-123, April.
    4. William Dare & A. Steven Holland, 2004. "Efficiency in the NFL betting market: modifying and consolidating research methods," Applied Economics, Taylor & Francis Journals, vol. 36(1), pages 9-15.
    5. Borghesi, Richard & Dare, William, 2009. "A test of the widespread-point-shaving theory," Finance Research Letters, Elsevier, vol. 6(3), pages 115-121, September.
    6. Justin Wolfers, 2006. "Point Shaving: Corruption in NCAA Basketball," American Economic Review, American Economic Association, vol. 96(2), pages 279-283, May.
    7. Golec, Joseph & Tamarkin, Maurry, 1991. "The degree of inefficiency in the football betting market : Statistical tests," Journal of Financial Economics, Elsevier, vol. 30(2), pages 311-323, December.
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    Cited by:

    1. Robert Arscott, 2023. "Market Efficiency and Censoring Bias in College Football Gambling," Journal of Sports Economics, , vol. 24(5), pages 664-689, June.
    2. Berkowitz, Jason P. & Depken, Craig A. & Gandar, John M., 2015. "Information and accuracy in pricing: Evidence from the NCAA men׳s basketball betting market," Journal of Financial Markets, Elsevier, vol. 25(C), pages 16-32.
    3. Kevin Krieger & Justin L. Davis & James Strode, 2021. "Patience is a virtue: exploiting behavior bias in gambling markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 735-750, October.
    4. Ege Can & Mark W. Nichols, 2022. "The Income Elasticity of Gross Sports Betting Revenues in Nevada: Short-Run and Long-Run Estimates," Journal of Sports Economics, , vol. 23(2), pages 175-199, February.
    5. Michael DiFilippo & Kevin Krieger & Justin Davis & Andy Fodor, 2014. "Early Season NFL Over/Under Bias," Journal of Sports Economics, , vol. 15(2), pages 201-211, April.
    6. Daniel C. Hickman, 2020. "Efficiency in the madness? examining the betting market for the ncaa men’s basketball tournament," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 611-626, July.
    7. Mark W. Nichols, 2014. "The Impact of Visiting Team Travel on Game Outcome and Biases in NFL Betting Markets," Journal of Sports Economics, , vol. 15(1), pages 78-96, February.

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