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Which team will win the 2014 FIFA World Cup? A Bayesian approach for dummies

Author

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  • Andrés Ramírez Hassan
  • Johnatan Cardona Jiménez

Abstract

This paper presents several "ex ante" simulation exercises of the 2014 FIFA World Cup. Specifically, we estimate the probabilities of each national team advancing to different stages, using a basic Bayesian approach based on conjugate families. In particular, we use the Categorical-Dirichlet model in the first round and the Bernoulli-Beta model in the following stages. The novelty of our framework is given by the use of betting odds to elicit the hyperparameters of prior distributions. Additionally, we obtain the posterior distributions with the Highest Density Intervals of the probability to being champion for each team. We find that Brazil (19.95%), Germany (14.68%), Argentina (12.05%), and Spain (6.2%) have the highest probabilities of being champion. Finally, we identify some betting opportunities with our simulation exercises. In particular, Bosnia & Herzegovina is a promising, whereas Australia shows the lowest betting opportunities return.

Suggested Citation

  • Andrés Ramírez Hassan & Johnatan Cardona Jiménez, 2014. "Which team will win the 2014 FIFA World Cup? A Bayesian approach for dummies," Documentos de Trabajo de Valor Público 10898, Universidad EAFIT.
  • Handle: RePEc:col:000122:010898
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    References listed on IDEAS

    as
    1. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    2. Thomas, Seemon & Jacob, Joy, 2006. "A generalized Dirichlet model," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1761-1767, October.
    3. 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.
    4. 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.
    5. Shin, Hyun Song, 1993. "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims," Economic Journal, Royal Economic Society, vol. 103(420), pages 1141-1153, September.
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    More about this item

    Keywords

    Bayesian Approach; Conjugate Families; Simulation; World Cup;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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