IDEAS home Printed from https://ideas.repec.org/a/buc/jgbeco/v5y2011i2p42-56.html
   My bibliography  Save this article

Revisting the "Hot Hand" Hypothesis in the NBA Betting Market Using Actual Sportsbook Betting Percentages on Favorites and Underdogs

Author

Listed:
  • Rodney Paul

    (Syracuse University)

  • Andrew Weinbach

    (Coastal Carolina University)

  • Brad Humphreys

    (University of Alberta)

Abstract

The “hot hand” hypothesis was first investigated in sports betting markets by Camerer (1989) and Brown and Sauer (1993), who examined if professional basketball teams truly could become “hot”, implying a change in their actual skill level, and if the betting market believes teams become “hot” and over bet the teams on winning streaks. Both assumed that book makers operated a balanced book. Recent evidence suggests that book makers do not set point spreads to balance betting on either side of games. Book makers may price as a forecast or shade point spreads to exploit known biases. The “hot hand” could exist, but closing point spreads may not reflect this bias due to an unbalanced book. Using a 6 season sample of NBA betting market data, we show wagering against the “hot hand” does not win more than implied by efficiency. However, OLS and two-stage least squares regression models show that bettors believe in the hot hand, as teams on streaks attract a significantly higher number of bets. This illustrates that the public believes in the hot hand, reflecting an actual behavioral bias. This bias exists even though the closing price serves as an optimal and unbiased forecast of outcomes.

Suggested Citation

  • Rodney Paul & Andrew Weinbach & Brad Humphreys, 2011. "Revisting the "Hot Hand" Hypothesis in the NBA Betting Market Using Actual Sportsbook Betting Percentages on Favorites and Underdogs," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 5(2), pages 42-56, July.
    • Handle: RePEc:buc:jgbeco:v:5:y:2011:i:2:p:42-56
    as

    Download full text from publisher

    File URL: http://www.ubplj.org/index.php/jgbe/article/view/569
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jeremy M. Losak & Andrew P. Weinbach & Rodney J. Paul, 2023. "Behavioral Biases in Daily Fantasy Baseball: The Case of the Hot Hand," Journal of Sports Economics, , vol. 24(3), pages 374-401, April.
    2. Justin Cox & Adam L. Schwartz & Bonnie F. Van Ness & Robert A. Van Ness, 2021. "The Predictive Power of College Football Spreads: Regular Season Versus Bowl Games," Journal of Sports Economics, , vol. 22(3), pages 251-273, April.
    3. Benjamin Waggoner & Daniel Wines & Brian P. Soebbing & Chad S. Seifried & Jean Michael Martinez, 2014. "“Hot Hand” in the National Basketball Association Point Spread Betting Market: A 34-Year Analysis," IJFS, MDPI, vol. 2(4), pages 1-12, November.
    4. 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.
    5. Corey A. Shank, 2018. "Is the NFL betting market still inefficient?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(4), pages 818-827, October.
    6. Andre Boik, 2017. "The empirical effects of competition on third‐degree price discrimination in the presence of arbitrage," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(4), pages 1023-1036, November.
    7. Corey A. Shank, 2019. "NFL betting market efficiency, divisional rivals, and profitable strategies," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 36(3), pages 567-580, September.
    8. Steven Salaga & Katie M Brown, 2018. "Momentum and betting market perceptions of momentum in college football," Applied Economics Letters, Taylor & Francis Journals, vol. 25(19), pages 1383-1388, November.

    More about this item

    Keywords

    censored regression; gambling; consumer behavior;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:buc:jgbeco:v:5:y:2011:i:2:p:42-56. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dominic Cortis, University of Malta (email available below). General contact details of provider: http://www.ubpl.co.uk/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.