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Does the Hot Hand Drive the Market? Evidence from College Football Betting Markets

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Abstract

This paper investigates whether market makers rationally price out certain strategies at the expense of leaving other strategies profitable. We examine betting market outcomes in college football from over 14,000 games during 1985–2008. We find that favorites are statistically overpriced while home teams are statistically underpriced. Furthermore, we provide suggestive evidence for why this inefficiency persists: betting houses deliberately inflate the betting lines to account for previous strong performance against the spread, or the “hot hand.” Although eliminating the “hot hand” is rational since betting on “hot” teams is popular, tempering the “hot hand” results in profitable simple betting strategies.

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Notes

  1. For example, it is estimated that as much as $12 billion is bet on the NCAA Final Four Basketball tournament each Spring [Matuszewski 2009].

  2. For example, every single NFL city has all four major TV affiliates and a daily newspaper with their own dedicated sports reporters, and many cities have multiple newspapers.

  3. Kilby et al. [2002] and Roxborough and Rhoden [1991] point out in guides for sports book management that a bookmaker’s primary objective is, in fact, to minimize risk.

  4. Betting houses may cap the size of the bet; for example, Kilby et al. [2002] suggests that gambling houses should cap the size of the bet at $2,000 for college football, although betting houses may choose to pursue higher limits if they feel that bettors are particularly uninformed. However, this would not prevent bettors from having others bet for them. Thus, we model bet size to be arbitrarily large, although in principle the bet size is capped. We return to this idea later, when we discuss potential limits to arbitrage in this market.

  5. We note that while there may be events that would influence betting market results after the final betting line, these events are neither known to bettors nor betting houses at the time of the bet, and thus these events do not impact market efficiency. Put another way, we only examine betting house pricing in terms of what betting houses are able to control for ex-ante, not what they cannot control, such as within-game injuries, for ex-post.

  6. These teams are available from the authors upon request.

  7. These opponents are substantially weaker and are almost always away underdogs. These data were excluded from our data set because of the fact that betting houses do not provide lines for these teams unless they are playing major opponents, indicating the markets for betting on these teams individually are thin. As betting houses do not pay attention to these teams except in the light of major opponents, we do not include results with these data in our analysis. However, our results are robust to the inclusion of these teams.

  8. We show the results of the traditional test for our data in the appendix

  9. If a team neither loses to nor beats the betting line, it is dropped from the analysis, as all bets related to that game are returned.

  10. Dare and Holland [2004] suggest that these effects should be exactly opposite to each other, and that this should therefore be a model restriction. Although our results are consistent with their intuition, we do not impose equality of coefficients as a model restriction. First, there may be unobserved heterogeneity associated with away underdogs but not with home favorites, particularly because point spreads are not set in all college football games. 1-AA teams that are away underdogs may perform differently in games compared with their 1-A counterparts since they appear in games with point spread relatively infrequently. In these cases, imposing equality between the two effects may eliminate this interesting variation.

  11. We tried other definitions of WINSTREAK with similar results.

  12. We document this claim from our search of the narrative record, with some key examples presented here:“Following a team’s winning streak is one of the best way of making money in sports gambling, as everybody loves to ride the hot team!” [Raymond 2009] http://www.atsdatabase.com/blog/blog1.php/2009/09/15/rice-owls-vs-oklahoma-state-cowboys-coll“You need to find out which teams are blazing hot and seemingly can’t lose to anybody and the teams that are cold as ice which look like they couldn’t beat themselves. It’s basic common knowledge and handicapping strategy to try and ride a winner until she bucks you and to stay away from dead beats.” [Boyd 2009] http://www.locksmithsportspicks.com/college-football-trends/

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Acknowledgements

We thank the editor and an anonymous referee for helpful remarks. Andrew Weinbach graciously shared data sources. Dwight Barthelmy, Nathan Fritz-Joseph, Kyle Kain, Michael Kovach, Justin Sawyer, and Christopher Zukauckas provided excellent research assistance. We thank Ben Anderson, Rodney Andrews, Vicki Bogan, Lisa D. Cook, John Gray, P.J. Healy, Pok-Sang Lam, Peter McGee, and Brandon Restrepo for helpful comments and conversations.

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Authors and Affiliations

  1. Department of Economics, University of West Georgia, 1601 N. Maple Street, Carrollton, 30117, GA, USA

    Michael Sinkey

  2. Ohio State University and NBER, 1945 N. High Street, Columbus, 43210, OH, USA

    Trevon Logan

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Sinkey, M., Logan, T. Does the Hot Hand Drive the Market? Evidence from College Football Betting Markets. Eastern Econ J 40, 583–603 (2014). https://doi.org/10.1057/eej.2013.33

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