Estimating Collective Belief in Fixed Odds Betting

  • Weiyun Chen
  • Xin Li
  • Daniel Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6749)

Abstract

Fixed odds betting is a popular mechanism in sports game betting. In this paper, we aim to decipher actual group belief on contingent future events from the dynamics of fixed odds betting. Different from previous studies, we adopt the prospect theory rather than the expected utility (EU) theory to model bettor behaviors. Thus, we do not need to make assumptions on how much each bettor stake on their preferred events. We develop a model that captures the heterogeneity of bettors with behavior parameters drawn from beta distributions. We evaluate our proposed model on a real-world dataset collected from online betting games for 2008 Olympic Game events. In the empirical study, our model significantly outperforms expert (bookmaker) predictions. Our study shows the possibility of developing a light-weight derivative prediction market upon fixed odds betting for collective information analysis and decision making.

Keywords

fixed odds betting prediction markets computational experiments 

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References

  1. 1.
    Arrow, K.J., et al.: The Promise of Prediction Markets. Science 320(5878), 877–878 (2008)CrossRefGoogle Scholar
  2. 2.
    Schrieber, J.M.: The Application of Prediction Markets to Business. Engineering Systems Division (2004)Google Scholar
  3. 3.
    Servan-Schreiber, E., et al.: Prediction Markets: Does Money Matter? Electronic Markets 14(3), 243–251 (2004)CrossRefGoogle Scholar
  4. 4.
    Gray, P.K., Gray, S.F.: Testing Market Efficiency: Evidence from the NFL Sports Betting Market. Journal of Finance 52(4), 1725–1737 (1997)CrossRefGoogle Scholar
  5. 5.
    Kuypers, T.: Information and Efficiency: An Empirical Study of a Fixed Odds Betting Market. Applied Economics 32(11), 1353–1363 (2000)CrossRefGoogle Scholar
  6. 6.
    Steven, D.L.: Why are gambling markets organised so differently from financial markets? Economic Journal 114(495), 223–246 (2004)CrossRefGoogle Scholar
  7. 7.
    Erik, S., Justin, W.: Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions? In: Chicago, IL, ETATS-UNIS, vol. 118, pp. 723–746. University of Chicago Press, Chicago (2010)Google Scholar
  8. 8.
    Quandt, R.E.: Betting and Equilibrium. The Quarterly Journal of Economics 101(1), 201–207 (1986)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Kahneman, D., Tversky, A.: Prospect Theory: An Analysis of Decision under Risk. Econometrica 47(2), 263–291 (1979)CrossRefMATHGoogle Scholar
  10. 10.
    Justin, W., Eric, Z.: Interpreting Prediction Market Prices as Probabilities. Institute for the Study of Labor, IZA (2006)Google Scholar
  11. 11.
    Tversky, A., Kahneman, D.: Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4), 297–323 (1992)CrossRefMATHGoogle Scholar
  12. 12.
    Bruno, J., Bernard, S.: Estimating Preferences under Risk: The Case of Racetrack Bettors. Journal of Political Economy 108(3), 503–530 (2000)CrossRefGoogle Scholar
  13. 13.
    Wang, F.-Y.: Toward a Paradigm Shift in Social Computing: The ACP Approach. IEEE Intelligent Systems 22(5), 65–67 (2007)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Wang, F.-Y., et al.: Social Computing: From Social Informatics to Social Intelligence. IEEE Intelligent Systems 22(2), 79–83 (2007)CrossRefGoogle Scholar
  15. 15.
    Chen, Y., et al.: Information markets vs. opinion pools: an empirical comparison. In: 6th ACM Conference on Electronic Commerce. ACM Press, Vancsouver (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Weiyun Chen
    • 1
  • Xin Li
    • 2
  • Daniel Zeng
    • 1
  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Department of Information SystemsCity University of Hong KongHong Kong, China

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