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The Importance of Betting Early

Author

Listed:
  • Alessandro Innocenti
  • Tommaso Nannicini
  • Roberto Ricciuti

Abstract

We evaluate the impact of timing on decision outcomes, when both the timing and the relevant decision are chosen under uncertainty. Sports betting provides the testing ground, as we exploit an original dataset containing more than one million online bets on games of the Italian Major Soccer League. We find that individuals perform systematically better when they place their bets farther away from the game day. The better performance of early bettors holds controlling for (time-invariant) unobservable ability, learning during the season, and timing of the odds. We attribute this result to the increase of noisy information on game day, which hampers the capacity of late (non-professional) bettors to use very simple prediction methods, such as team rankings or last game results. We also find that more successful bettors tend to bet in advance, focus on a smaller set of events, and prefer games associated with smaller betting odds. JEL codes: D81, D83. Keywords: sports betting, decision timing, information overload, forecasting.

Suggested Citation

  • Alessandro Innocenti & Tommaso Nannicini & Roberto Ricciuti, 2013. "The Importance of Betting Early," Working Papers 502, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:502
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    References listed on IDEAS

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    1. Figlewski, Stephen, 1979. "Subjective Information and Market Efficiency in a Betting Market," Journal of Political Economy, University of Chicago Press, vol. 87(1), pages 75-88, February.
    2. Donald B. Hausch & William T. Ziemba & Mark Rubinstein, 1981. "Efficiency of the Market for Racetrack Betting," Management Science, INFORMS, vol. 27(12), pages 1435-1452, December.
    3. Buhagiar, Ranier & Cortis, Dominic & Newall, Philip W.S., 2018. "Why do some soccer bettors lose more money than others?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 85-93.
    4. Rodney J. Paul & Andrew P. Weinbach, 2005. "Bettor Misperceptions in the NBA," Journal of Sports Economics, , vol. 6(4), pages 390-400, November.
    5. Davis, Fred D. & Lohse, Gerald L. & Kottemann, Jeffrey E., 1994. "Harmful effects of seemingly helpful information on forecasts of stock earnings," Journal of Economic Psychology, Elsevier, vol. 15(2), pages 253-267, June.
    6. Erik Angner, 2006. "Economists as experts: Overconfidence in theory and practice," Journal of Economic Methodology, Taylor & Francis Journals, vol. 13(1), pages 1-24.
    7. Andersson, Patric & Edman, Jan & Ekman, Mattias, 2005. "Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts," International Journal of Forecasting, Elsevier, vol. 21(3), pages 565-576.
    8. Fister, Iztok & Ljubič, Karin & Suganthan, Ponnuthurai Nagaratnam & Perc, Matjaž & Fister, Iztok, 2015. "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 178-186.
    9. Palomino, Frederic & Renneboog, Luc & Zhang, Chendi, 2009. "Information salience, investor sentiment, and stock returns: The case of British soccer betting," Journal of Corporate Finance, Elsevier, vol. 15(3), pages 368-387, June.
    10. Han Bleichrodt & Ulrich Schmidt, 2002. "A Context-Dependent Model of the Gambling Effect," Management Science, INFORMS, vol. 48(6), pages 802-812, June.
    11. Boeri, Tito & Severgnini, Battista, 2011. "Match rigging and the career concerns of referees," Labour Economics, Elsevier, vol. 18(3), pages 349-359, June.
    12. Brad M. Barber & Terrance Odean, 2002. "Online Investors: Do the Slow Die First?," The Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 455-488, March.
    13. Enrico Diecidue & Ulrich Schmidt & Peter P. Wakker, 2004. "The Utility of Gambling Reconsidered," Journal of Risk and Uncertainty, Springer, vol. 29(3), pages 241-259, December.
    14. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    15. Richard H. Thaler & Shlomo Benartzi, 2001. "Naive Diversification Strategies in Defined Contribution Saving Plans," American Economic Review, American Economic Association, vol. 91(1), pages 79-98, March.
    16. Evan Osborne, 2001. "Efficient Markets? Don’t Bet on It," Journal of Sports Economics, , vol. 2(1), pages 50-61, February.
    17. Kaufmann, Christine & Weber, Martin, 2013. "Sometimes less is more – The influence of information aggregation on investment decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 95(C), pages 20-33.
    18. Atanasov, Pavel & Witkowski, Jens & Ungar, Lyle & Mellers, Barbara & Tetlock, Philip, 2020. "Small steps to accuracy: Incremental belief updaters are better forecasters," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 19-35.
    19. Gerd Gigerenzer & Daniel G. Goldstein, 2011. "The recognition heuristic: A decade of research," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 100-121, February.
    20. repec:bla:econom:v:56:y:1989:i:223:p:323-41 is not listed on IDEAS
    21. Snyder, Wayne W, 1978. "Horse Racing: Testing the Efficient Markets Model," Journal of Finance, American Finance Association, vol. 33(4), pages 1109-1118, September.
    22. Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
    23. Golec, Joseph & Tamarkin, Maurry, 1995. "Do Bettors Prefer Long Shots because They Are Risk-Lovers, or Are They Just Overconfident?," Journal of Risk and Uncertainty, Springer, vol. 11(1), pages 51-64, July.
    24. Anthony Ziegelmeyer & Marie-HÈlËne Broihanne & FrÈdÈric Koessler, 2004. "Sequential Parimutuel Betting in the Laboratory," Journal of Risk and Uncertainty, Springer, vol. 28(2), pages 165-186, March.
    25. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.
    26. Pavlo Blavatskyy, 2009. "Betting on own knowledge: Experimental test of overconfidence," Journal of Risk and Uncertainty, Springer, vol. 38(1), pages 39-49, February.
    27. Ladd Kochman & Randy Goodwin & David Bray, 2015. "So Easy a Caveman Can Beat the Football Betting Market," The American Economist, Sage Publications, vol. 60(2), pages 225-228, September.
    28. Conlisk, John, 1993. "The Utility of Gambling," Journal of Risk and Uncertainty, Springer, vol. 6(3), pages 255-275, June.
    29. Russell Golman & David Hagmann & George Loewenstein, 2017. "Information Avoidance," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 96-135, March.
    30. Crawford, Vincent P & Sobel, Joel, 1982. "Strategic Information Transmission," Econometrica, Econometric Society, vol. 50(6), pages 1431-1451, November.
    31. Asch, Peter & Malkiel, Burton G & Quandt, Richard E, 1984. "Market Efficiency in Racetrack Betting," The Journal of Business, University of Chicago Press, vol. 57(2), pages 165-175, April.
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    Keywords

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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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