IDEAS home Printed from https://ideas.repec.org/p/inn/wpaper/2016-15.html
   My bibliography  Save this paper

Predictive Bookmaker Consensus Model for the UEFA Euro 2016

Author

Listed:
  • Achim Zeileis
  • Christoph Leitner
  • Kurt Hornik

Abstract

From 10 June to 10 July 2016 the best European football teams will meet in France to determine the European Champion in the UEFA European Championship 2016 tournament (Euro 2016 for short). For the first time 24 teams compete, expanding the format from 16 teams as in the previous five Euro tournaments. For forecasting the winning probability of each team a predictive model based on bookmaker odds from 19 online bookmakers is employed. The favorite is the host France with a forecasted winning probability of 21.5%, followed by the current World Champion Germany with a winning probability of 20.1%. The defending European Champion Spain follows after some gap with 13.7% and all remaining teams are predicted to have lower chances with England (9.2%) and Belgium (7.7%) being the "best of the rest". Furthermore, by complementing the bookmaker consensus results with simulations of the whole tournament, predicted pairwise probabilities for each possible game at the Euro 2016 are obtained along with "survival" probabilities for each team proceeding to the different stages of the tournament. For example, it can be determined that it is much more likely that top favorites France and Germany meet in the semifinal (7.8%) rather than in the final at the Stade de France (4.2%) - which would be a re-match of the friendly game that was played on 13 November 2015 during the terrorist attacks in Paris and that France won 2-0. Hence it is maybe better that the tournament draw favors a match in the semifinal at Marseille (with an almost even winning probability of 50.5% for France). The most likely final is then that either of the two teams plays against the defending champion Spain with a probability of 5.7% for France vs. Spain and 5.4% for Germany vs. Spain, respectively. All forecasts are the result of an aggregation of quoted winning odds for each team in the Euro 2016: These are first adjusted for profit margins ("overrounds"), averaged on the log-odds scale, and then transformed back to winning probabilities. Moreover, team abilities (or strengths) are approximated by an "inverse" procedure of tournament simulations, yielding estimates of probabilities for all possible pairwise matches at all stages of the tournament. This technique correctly predicted the winner of the FIFA 2010 and Euro 2012 tournaments while missing the winner but correctly predicting the final for the Euro 2008 and three out of four semifinalists at the FIFA 2014 World Cup (Leitner, Zeileis, and Hornik 2008, 2010a,b; Zeileis, Leitner, and Hornik 2012, 2014).

Suggested Citation

  • Achim Zeileis & Christoph Leitner & Kurt Hornik, 2016. "Predictive Bookmaker Consensus Model for the UEFA Euro 2016," Working Papers 2016-15, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2016-15
    as

    Download full text from publisher

    File URL: https://www2.uibk.ac.at/downloads/c4041030/wpaper/2016-15.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Achim Zeileis & Christoph Leitner & Kurt Hornik, 2012. "History Repeating: Spain Beats Germany in the EURO 2012 Final," Working Papers 2012-09, Faculty of Economics and Statistics, Universität Innsbruck.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Predictive Bookmaker Consensus Model for the UEFA Euro 2016
      by ? in R-bloggers on 2016-05-31 19:43:00

    Citations

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


    Cited by:

    1. Groll Andreas & Kneib Thomas & Mayr Andreas & Schauberger Gunther, 2018. "On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 65-79, June.
    2. Achim Zeileis & Christoph Leitner & Kurt Hornik, 2018. "Probabilistic forecasts for the 2018 FIFA World Cup based on the bookmaker consensus model," Working Papers 2018-09, Faculty of Economics and Statistics, Universität Innsbruck.

    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. Achim Zeileis & Christoph Leitner & Kurt Hornik, 2014. "Home Victory for Brazil in the 2014 FIFA World Cup," Working Papers 2014-17, Faculty of Economics and Statistics, Universität Innsbruck.
    2. Jonas Hammerschmidt & Fabian Eggers & Sascha Kraus & Paul Jones & Matthias Filser, 2020. "Entrepreneurial orientation in sports entrepreneurship - a mixed methods analysis of professional soccer clubs in the German-speaking countries," International Entrepreneurship and Management Journal, Springer, vol. 16(3), pages 839-857, September.
    3. A. C. Titman & D. A. Costain & P. G. Ridall & K. Gregory, 2015. "Joint modelling of goals and bookings in association football," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 659-683, June.
    4. Jaume García & Levi Pérez & Plácido Rodríguez, 2017. "Forecasting football match results: are the many smarter than the few?," Chapters, in: Plácido Rodríguez & Brad R. Humphreys & Robert Simmons (ed.), The Economics of Sports Betting, chapter 5, pages 71-91, Edward Elgar Publishing.
    5. 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.
    6. 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.
    7. 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," IJFS, MDPI, vol. 1(4), pages 1-15, December.
    8. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.
    9. James Reade, 2014. "Information And Predictability: Bookmakers, Prediction Markets And Tipsters As Forecasters," Journal of Prediction Markets, University of Buckingham Press, vol. 8(1), pages 43-76.
    10. 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).
    11. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    12. Karol Kempa & Hannes Rusch, 2016. "Misconduct and Leader Behaviour in Contests – New Evidence from European Football," MAGKS Papers on Economics 201629, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    13. 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.
    14. Jaiho Chung & Joon Ho Hwang, 2010. "An Empirical Examination of the Parimutuel Sports Lottery Market versus the Bookmaker Market," Southern Economic Journal, John Wiley & Sons, vol. 76(4), pages 884-905, April.
    15. 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).
    16. Carlos Sáenz-Royo, 2017. "A plausible Decision Heuristics Model: Fallibility of human judgment as an endogenous problem," Working Papers 2017/04, Economics Department, Universitat Jaume I, Castellón (Spain).
    17. Groll Andreas & Abedieh Jasmin, 2013. "Spain retains its title and sets a new record – generalized linear mixed models on European football championships," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 51-66, March.
    18. Butler, David & Butler, Robert & Eakins, John, 2021. "Expert performance and crowd wisdom: Evidence from English Premier League predictions," European Journal of Operational Research, Elsevier, vol. 288(1), pages 170-182.
    19. 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.
    20. David Forrest & Ian Mchale, 2007. "Anyone for Tennis (Betting)?," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 751-768.

    More about this item

    Keywords

    consensus; agreement; bookmakers odds; tournament; UEFA European Championship 2016;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

    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:inn:wpaper:2016-15. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Janette Walde (email available below). General contact details of provider: https://edirc.repec.org/data/fuibkat.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.