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Do Bookmakers Possess Superior Skills to Bettors in Predicting Outcomes?

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  • Michael A. Smith
  • David Paton
  • Leighton Vaughan Williams

Abstract

In this paper we test the hypothesis that bookmakers display superior skills to bettors in predicting the outcome of sporting events by using matched data from traditional bookmaking and person-to-person exchanges. Employing a conditional logistic regression model on horse racing data from the UK we find that, in high liquidity betting markets, betting exchange odds have more predictive value than the corresponding bookmaker odds. To control for potential spillovers between the two markets, we repeat the analysis for cases where prices diverge significantly. Once again, exchange odds yield more valuable information concerning race outcomes than the bookmaker equivalents.

Suggested Citation

  • Michael A. Smith & David Paton & Leighton Vaughan Williams, 2009. "Do Bookmakers Possess Superior Skills to Bettors in Predicting Outcomes?," Post-Print hal-00684229, HAL.
  • Handle: RePEc:hal:journl:hal-00684229
    DOI: 10.1016/j.jebo.2009.03.016
    Note: View the original document on HAL open archive server: https://hal.science/hal-00684229
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    References listed on IDEAS

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    Cited by:

    1. Król, Michał, 2012. "Product differentiation decisions under ambiguous consumer demand and pessimistic expectations," International Journal of Industrial Organization, Elsevier, vol. 30(6), pages 593-604.
    2. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    3. Barge-Gil, Andrés & García-Hiernaux, Alfredo, 2019. "Staking plans in sports betting under unknown true probabilities of the event," MPRA Paper 92196, University Library of Munich, Germany.
    4. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2013. "Inter-market Arbitrage in Betting," Economica, London School of Economics and Political Science, vol. 80(318), pages 300-325, April.
    5. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2011. "Sentimental Preferences and the Organizational Regime of Betting Markets," Southern Economic Journal, John Wiley & Sons, vol. 78(2), pages 502-518, October.
    6. Jason P. Berkowitz & Craig A. Depken II & John M. Gandar, 2018. "The Conversion of Money Lines Into Win Probabilities," Journal of Sports Economics, , vol. 19(7), pages 990-1015, October.
    7. Mills, Brian M. & Salaga, Steven, 2018. "A natural experiment for efficient markets: Information quality and influential agents," Journal of Financial Markets, Elsevier, vol. 40(C), pages 23-39.
    8. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
    9. Vincenzo Candila & Antonio Scognamillo, 2019. "On the Longshot Bias in Tennis Betting Markets: The Casco Normalization," Working Papers 3_236, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    10. Egon Franck & Erwin Verbeek & Stephan Nuesch, 2009. "Inter- market Arbitrage in Sports Betting," NCER Working Paper Series 48, National Centre for Econometric Research.
    11. Isabel Abinzano & Luis Muga & Rafael Santamaria, 2019. "Hidden Power of Trading Activity: The FLB in Tennis Betting Exchanges," Journal of Sports Economics, , vol. 20(2), pages 261-285, February.
    12. Andrés Barge-Gil & Alfredo Garcia-Hiernaux, 2020. "Staking in Sports Betting Under Unknown Probabilities: Practical Guide for Profitable Bettors," Journal of Sports Economics, , vol. 21(6), pages 593-609, August.
    13. Flepp, Raphael & Nüesch, Stephan & Franck, Egon, 2017. "The liquidity advantage of the quote-driven market: Evidence from the betting industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 306-317.
    14. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    15. Choi, Darwin & Hui, Sam K., 2014. "The role of surprise: Understanding overreaction and underreaction to unanticipated events using in-play soccer betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 614-629.
    16. Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.
    17. Peeters, Thomas, 2018. "Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results," International Journal of Forecasting, Elsevier, vol. 34(1), pages 17-29.
    18. Berkowitz, Jason P. & Depken II, Craig A. & Gandar, John M., 2018. "Market evidence against widespread point shaving in college basketball," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 283-292.
    19. Goto, Shingo & Yamada, Toru, 2023. "What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 252-270.
    20. Rodney J. Paul & Andrew P. Weinbach, 2011. "Investigating Allegations of Pointshaving in NCAA Basketball Using Actual Sportsbook Betting Percentages," Journal of Sports Economics, , vol. 12(4), pages 432-447, August.
    21. Costa Sperb, L.F. & Sung, M.-C. & Ma, T. & Johnson, J.E.V., 2022. "Turning the heat on financial decisions: Examining the role temperature plays in the incidence of bias in a time-limited financial market," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1142-1157.
    22. Franke, Maximilian, 2020. "Do market participants misprice lottery-type assets? Evidence from the European soccer betting market," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 1-18.
    23. Kauffeldt, Florian & Wiesenfarth, Boris, 2014. "Confidence, Pessimism and their Impact on Product Differentiation in a Hotelling Model with Demand Location Uncertainty," Working Papers 0562, University of Heidelberg, Department of Economics.
    24. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    25. Andrew Grant & Anastasios Oikonomidis & Alistair C. Bruce & Johnnie E. V. Johnson, 2018. "New entry, strategic diversity and efficiency in soccer betting markets: the creation and suppression of arbitrage opportunities," The European Journal of Finance, Taylor & Francis Journals, vol. 24(18), pages 1799-1816, December.

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    More about this item

    Keywords

    D82; G12; G14; betting exchanges; market efficiency; prediction;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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