IDEAS home Printed from https://ideas.repec.org/p/rdg/emxxdp/em-dp2020-03.html
   My bibliography  Save this paper

Betting markets for English Premier League results and scorelines: evaluating a forecasting model

Author

Listed:
  • J. James Reade

    () (Department of Economics, University of Reading)

  • Carl Singleton

    () (Department of Economics, University of Reading)

  • Leighton Vaughan Williams

    () (Nottingham Business School, Nottingham Trent University, UK)

Abstract

Using betting odds from two recent seasons of English Premier League football matches, we evaluate probability and point forecasts generated from a standard statistical model of goal scoring. The bookmaker odds show significant evidence of the favourite-longshot bias for exact scorelines, which is not generally present for match results. We find evidence that the scoreline probability forecasts from the model are better than what the odds of bookmakers imply, based on forecast encompassing regressions. However, when we apply a simple betting strategy using point forecasts from the model, there are no substantial or consistent financial returns to be made over the two seasons. In other words, there is no evidence from this particular statistical model that the result, scoreline, margin of victory or total goals betting markets are on average inefficient.

Suggested Citation

  • J. James Reade & Carl Singleton & Leighton Vaughan Williams, 2020. "Betting markets for English Premier League results and scorelines: evaluating a forecasting model," Economics Discussion Papers em-dp2020-03, Department of Economics, Reading University.
  • Handle: RePEc:rdg:emxxdp:em-dp2020-03
    as

    Download full text from publisher

    File URL: http://www.reading.ac.uk/web/FILES/economics/emdp202003.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Fair, Ray C & Shiller, Robert J, 1989. "The Informational Context of Ex Ante Forecasts," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 325-331, May.
    3. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    4. Erik Snowberg & Justin Wolfers, 2010. "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?," Journal of Political Economy, University of Chicago Press, vol. 118(4), pages 723-746, August.
    5. Strumbelj, E. & Sikonja, M. Robnik, 2010. "Online bookmakers' odds as forecasts: The case of European soccer leagues," International Journal of Forecasting, Elsevier, vol. 26(3), pages 482-488, July.
    6. Andreas Heuer & Oliver Rubner, 2012. "How Does the Past of a Soccer Match Influence Its Future? Concepts and Statistical Analysis," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    7. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
    8. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    9. Elaad, Guy & Reade, J. James & Singleton, Carl, 2020. "Information, prices and efficiency in an online betting market," Finance Research Letters, Elsevier, vol. 35(C).
    10. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    11. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    12. Karlis, Dimitris & Ntzoufras, Ioannis, 2005. "Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i10).
    13. Michael Cain & David Law & David Peel, 2000. "The Favourite‐Longshot Bias and Market Efficiency in UK Football betting," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(1), pages 25-36, February.
    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. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    16. Julia Campos & David F. Hendry & Hans‐Martin Krolzig, 2003. "Consistent Model Selection by an Automatic Gets Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
    17. 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.
    18. Giovanni Angelini & Luca De Angelis & Carl Singleton, 2019. "Informational efficiency and behaviour within in-play prediction markets," Economics Discussion Papers em-dp2019-20, Department of Economics, Reading University.
    19. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    20. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. James Reade & Carl Singleton & Alasdair Brown, 2019. "Evaluating Strange Forecasts: The Curious Case of Football Match Scorelines," Economics Discussion Papers em-dp2019-18, Department of Economics, Reading University.
    2. 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.
    3. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    4. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    5. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    6. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
    7. 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.
    8. 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.
    9. Giovanni Angelini & Luca De Angelis & Carl Singleton, 2019. "Informational efficiency and behaviour within in-play prediction markets," Economics Discussion Papers em-dp2019-20, Department of Economics, Reading University.
    10. 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.
    11. 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.
    12. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," International Journal of Forecasting, Elsevier, vol. 36(3), pages 916-932.
    13. 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.
    14. O'Leary, Daniel E., 2017. "Crowd performance in prediction of the World Cup 2014," European Journal of Operational Research, Elsevier, vol. 260(2), pages 715-724.
    15. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    16. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    17. Brown, Alasdair & Reade, J. James, 2019. "The wisdom of amateur crowds: Evidence from an online community of sports tipsters," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1073-1081.
    18. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, Open Access Journal, vol. 9(1), pages 1-9, January.
    19. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    20. Christoph Buehren & Tim Meyer & Christian Pierdzioch, 2020. "Experimental Evidence on Forecaster (anti-) Herding in Sports Markets," MAGKS Papers on Economics 202038, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

    More about this item

    Keywords

    Forecasting; Statistical modelling; Regression models; Prediction markets;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rdg:emxxdp:em-dp2020-03. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carl Singleton). General contact details of provider: https://edirc.repec.org/data/derdguk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.