A Sequential Markov Chain Model of FIFA World Cup Winners
Authors: Nehemiah A. Ikoba
In this paper, a sequential Markov chain conceptualization of the winners of the FIFA World Cup is presented. The aim was to capture the dynamics of the World Cup and predict the future winner via Markov chain analysis. A sequentially incremented state-space Markov chain is used to approximate the process of winning the FIFA World Cup. The corresponding Markov chains at every epoch where the state space increases were computed. The result of the analysis showed a close predictive ability of the model to predict the previous World Cup winners. It is predicted that a new winner may emerge in the 2022 World Cup in Qatar. However, if a new winner does not emerge, on the basis of both the sequential Markov chain and the first passage matrix of the conceptualized model, then Brazil is the most probable winner of the 2022 World Cup, followed by Italy and Germany. The sequential Markov chain approach can be applied to other sporting events and scenarios in which there is only a small probability that the number of observed states may increase from a small set of states.
stochastic processes, sports analytics; transition probability matrix, mean first passage time, mean recurrence time