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Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters

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  • Martin Spann

    (School of Business and Economics, University of Passau, Germany)

  • Bernd Skiera

    (School of Business and Economics, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany)

Abstract

This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profits systematically in a betting market. We present the results of an empirical study that uses data from 678-837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting-based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule-based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state-owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profits if punters exploited the information from prediction markets and bet only on a selected number of games. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
  • Handle: RePEc:jof:jforec:v:28:y:2009:i:1:p:55-72
    DOI: 10.1002/for.1091
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    File URL: http://hdl.handle.net/10.1002/for.1091
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    Cited by:

    1. Dilger, Alexander, 2016. "Bedingte Aktiengeschäfte," Discussion Papers of the Institute for Organisational Economics 08/2016, University of Münster, Institute for Organisational Economics.
    2. Edoardo Gaffeo, 2013. "Using information markets in grantmaking. An assessment of the issues involved and an application to Italian banking foundations," DEM Discussion Papers 2013/08, Department of Economics and Management.
    3. Bart Stemmet, 2011. "Hedging one’s happiness – Should a sports fan bet on the opponent?," Working Papers 20/2011, Stellenbosch University, Department of Economics.
    4. Alasdair Brown & Dooruj Rambaccussing & James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair," Economics & Management Discussion Papers em-dp2016-01, Henley Business School, Reading University.
    5. Siemroth, Christoph, 2014. "Why prediction markets work : the role of information acquisition and endogenous weighting," Working Papers 14-29, University of Mannheim, Department of Economics.
    6. Adrian R. Bell & Chris Brooks & David Matthews & Charles Sutcliffe, 2012. "Over the moon or sick as a parrot? The effects of football results on a club's share price," Applied Economics, Taylor & Francis Journals, vol. 44(26), pages 3435-3452, September.
    7. Franck, Egon & Verbeek, Erwin & Nüesch, Stephan, 2010. "Prediction accuracy of different market structures -- bookmakers versus a betting exchange," International Journal of Forecasting, Elsevier, vol. 26(3), pages 448-459, July.
    8. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Ine!ciencies: Evidence from Twitter and Betfair," Working Papers 2016-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    9. Bossan, Benjamin & Jann, Ole & Hammerstein, Peter, 2015. "The evolution of social learning and its economic consequences," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 266-288.
    10. 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.
    11. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    12. Leitner, Christoph & Zeileis, Achim & Hornik, Kurt, 2010. "Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008," International Journal of Forecasting, Elsevier, vol. 26(3), pages 471-481, July.
    13. Jennifer Brown & Dylan B. Minor, 2011. "Selecting the Best? Spillover and Shadows in Elimination Tournaments," NBER Working Papers 17639, National Bureau of Economic Research, Inc.

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