Statistical modeling in political analysis is used recently to describe electoral behaviour of political party. In this chapter we propose aWeibull mixture model that describes the votes obtained by a political party in Brazilian presidential elections. We considered the votes obtained by the Workers’ Party in five presidential elections from 1994 to 2010. A Bayesian approach was considered and a random walk Metropolis algorithm within Gibbs sampling was implemented. Next, Bayes factor was considered to the choice of the number of components in the mixture. In addition the probability of obtain 50% of the votes in the first round was estimated. The results show that only few components are needed to describe the votes obtained in this election. Finally, we found that the probability of obtaining 50% of the votes in the first ballot is increasing along time. Future developments are discussed.