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Optimal Strategies for Sports Betting Pools

Author

Listed:
  • Bryan Clair

    (Department of Mathematics and Computer Science, Saint Louis University, St. Louis, Missouri 63103)

  • David Letscher

    (Department of Mathematics and Computer Science, Saint Louis University, St. Louis, Missouri 63103)

Abstract

Every fall, millions of Americans enter betting pools to pick winners of the weekly NFL football games. In the spring, NCAA tournament basketball pools are even more popular. In both cases, teams that are popularly perceived as “favorites” gain a disproportionate share of entries. In large pools there can be a significant advantage to picking upsets that differentiate your picks from the crowd. In this paper, we present a model of betting pools that incorporates pool participant behavior. We use the model to derive strategies that maximize the expected return on a bet in both football pools and tournament-style pools. These strategies significantly outperform strategies based on maximizing score or number of correct picks---often by orders of magnitude.

Suggested Citation

  • Bryan Clair & David Letscher, 2007. "Optimal Strategies for Sports Betting Pools," Operations Research, INFORMS, vol. 55(6), pages 1163-1177, December.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:6:p:1163-1177
    DOI: 10.1287/opre.1070.0448
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    References listed on IDEAS

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    1. Metrick, Andrew, 1996. "March madness? Strategic behavior in NCAA basketball tournament betting pools," Journal of Economic Behavior & Organization, Elsevier, vol. 30(2), pages 159-172, August.
    2. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
    3. Caudill, Steven B., 2003. "Predicting discrete outcomes with the maximum score estimator: the case of the NCAA men's basketball tournament," International Journal of Forecasting, Elsevier, vol. 19(2), pages 313-317.
    4. Steven Caudill & Norman Godwin, 2002. "Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 991-1001.
    5. Edward H. Kaplan & Stanley J. Garstka, 2001. "March Madness and the Office Pool," Management Science, INFORMS, vol. 47(3), pages 369-382, March.
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    Cited by:

    1. Phillip E. Pfeifer & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "The Promise of Prediction Contests," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 264-270, November.
    2. Stekler Herman O. & Klein Andrew, 2012. "Predicting the Outcomes of NCAA Basketball Championship Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-10, March.
    3. Martin B. Haugh & Raghav Singal, 2021. "How to Play Fantasy Sports Strategically (and Win)," Management Science, INFORMS, vol. 67(1), pages 72-92, January.
    4. George Chang & James Feigenbaum, 2023. "Smart Money in the NCAA Men’s Basketball Tournament," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 15(8), pages 1-14, August.
    5. David Bergman & Carlos Cardonha & Jason Imbrogno & Leonardo Lozano, 2023. "Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 304-317, March.
    6. Phillip E. Pfeifer, 2016. "The promise of pick-the-winners contests for producing crowd probability forecasts," Theory and Decision, Springer, vol. 81(2), pages 255-278, August.
    7. David Bergman & Jason Imbrogno, 2017. "Surviving a National Football League Survivor Pool," Operations Research, INFORMS, vol. 65(5), pages 1343-1354, October.

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