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Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014

Author

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  • Groll Andreas

    (Department of Mathematics, Ludwig-Maximilians-University, Theresienstr. 39, 80333 Munich)

  • Schauberger Gunther

    (Department of Statistics, Ludwig-Maximilians-University, Munich, Bavaria, Germany)

  • Tutz Gerhard

    (Department of Statistics, Ludwig-Maximilians-University, Munich, Bavaria, Germany)

Abstract

In this article an approach for the analysis and prediction of international soccer match results is proposed. It is based on a regularized Poisson regression model that includes various potentially influential covariates describing the national teams’ success in previous FIFA World Cups. Additionally, within the generalized linear model (GLM) framework, also differences of team-specific effects are incorporated. In order to achieve variable selection and shrinkage, we use tailored Lasso approaches. Based on preceding FIFA World Cups, two models for the prediction of the FIFA World Cup 2014 are fitted and investigated. Based on the model estimates, the FIFA World Cup 2014 is simulated repeatedly and winning probabilities are obtained for all teams. Both models favor the actual FIFA World Champion Germany.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:2:p:97-115:n:1
    DOI: 10.1515/jqas-2014-0051
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    References listed on IDEAS

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    1. 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.
    2. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    3. 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.
    4. Dobson,Stephen & Goddard,John, 2011. "The Economics of Football," Cambridge Books, Cambridge University Press, number 9780521517140, January.
    5. 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.
    6. D Dyte & S R Clarke, 2000. "A ratings based Poisson model for World Cup soccer simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 993-998, August.
    7. Eugster Manuel J. A. & Gertheiss Jan & Kaiser Sebastian, 2011. "Having the Second Leg at Home - Advantage in the UEFA Champions League Knockout Phase?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-11, January.
    8. 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.
    9. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

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    2. Gunther Schauberger & Andreas Groll & Gerhard Tutz, 2018. "Analysis of the importance of on-field covariates in the German Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1561-1578, July.
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    6. 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.

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