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.
