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Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008

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  • Leitner, Christoph
  • Zeileis, Achim
  • Hornik, Kurt

Abstract

Different methods for assessing the abilities of participants in a sports tournament, and their corresponding winning probabilities for the tournament, are embedded in a common framework and their predictive performances compared. First, ratings of abilities (such as the Elo rating) are complemented with a simulation approach which yields winning probabilities for the full tournament. Second, tournament winning probabilities are extracted from bookmakers' odds using a consensus model, and the underlying abilities of the competitors are then derived by an "inverse" application of the tournament simulation. Both techniques are employed for forecasting the results of the European football championship 2008 (UEFA EURO 2008), for which the consensus model based on bookmakers' odds outperforms methods based on both the Elo rating and the FIFA/Coca Cola World rating. Moreover, the bookmaker consensus model correctly predicts that the final will be played by the teams from Germany and Spain (with a probability of about 20.5%), while showing that both finalists profit from being drawn in groups with relatively weak competitors.

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  • 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.
    • Handle: RePEc:eee:intfor:v:26:y::i:3:p:471-481
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    2. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
    3. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    4. 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.
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    6. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
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    8. Roberto Gásquez & Vicente Royuela, 2016. "The Determinants of International Football Success: A Panel Data Analysis of the Elo Rating," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 125-141, June.
    9. Constantinou Anthony Costa & Fenton Norman Elliott, 2013. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 37-50, March.
    10. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    11. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    12. L.F.M. Groot & J. Ferwerda, 2014. "Soccer jersey sponsors and the world cup," Working Papers 14-07, Utrecht School of Economics.
    13. Groll Andreas & Schauberger Gunther & Tutz Gerhard, 2015. "Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(2), pages 97-115, June.
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    15. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.

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