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Predicting bookmaker odds and efficiency for UK football

Author

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  • I. Graham
  • H. Stott

Abstract

The efficiency of gambling markets has frequently been questioned. In order to investigate the rationality of bookmaker odds, we use an ordered probit model to generate predictions for English football matches and compare these predictions with the odds of UK bookmaker William Hill. Further, we develop a model that predicts bookmaker odds. Combining a predictive model based on results and a bookmaker model based on previous quoted odds allows us to compare directly William Hill opinion of various teams with the team ratings generated by the predictive model. We also compare the objective value of individual home advantage and distance travelled with the value attributed to these factors by bookmakers. We show that there are systematic biases in bookmaker odds, and that these biases cannot be explained by William Hill odds omitting valuable, or excluding extraneous, information.

Suggested Citation

  • I. Graham & H. Stott, 2008. "Predicting bookmaker odds and efficiency for UK football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 99-109.
  • Handle: RePEc:taf:applec:v:40:y:2008:i:1:p:99-109
    DOI: 10.1080/00036840701728799
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    Citations

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    Cited by:

    1. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Babatunde Buraimo & David Peel & Rob Simmons, 2013. "Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 1(4), pages 1-15, December.
    3. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630, April.
    4. 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.
    5. 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.
    6. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    7. Jinook Jeong & Jee Young Kim & Yoon Jae Ro, 2019. "On the efficiency of racetrack betting market: a new test for the favourite-longshot bias," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5817-5828, November.
    8. Nilsson, HÃ¥kan & Andersson, Patric, 2010. "Making the seemingly impossible appear possible: Effects of conjunction fallacies in evaluations of bets on football games," Journal of Economic Psychology, Elsevier, vol. 31(2), pages 172-180, April.
    9. Bernardo, Giovanni & Ruberti, Massimo & Verona, Roberto, 2015. "Testing semi-strong efficiency in a fixed odds betting market: Evidence from principal European football leagues," MPRA Paper 66414, University Library of Munich, Germany.
    10. Bernardo, Giovanni & Ruberti, Massimo & Verona, Roberto, 2019. "Semi-strong inefficiency in the fixed odds betting market: Underestimating the positive impact of head coach replacement in the main European soccer leagues," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 239-246.
    11. 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.
    12. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630.
    13. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.

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