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Predicting Football Results: a Bayesian Approach with a ZeroModified Poisson Distribution
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Orientador(es)
Resumo(s)
Football is a global phenomenon, and as such, it was only a matter of time until statistics joined the
field.
This dissertation explores the application of the Zero-Modified Poisson (ZMP) model within a Bayesian
framework for predicting football match outcomes. Traditional Poisson-based approaches often fail to
account for irregularities in goal distributions, such as zero inflation or zero deflation, which are common
in football scoring patterns. The incorporation of the Zero-Modified Poisson model provides the flexibility to handle varying frequencies of zero outcomes while accommodating unequal means and variances
in contrast with the standard Poisson, which assumes equaldispersion.
Using match data from the 2022/23 Italian Serie A, a Bayesian hierarchical model is implemented via
the NIMBLE r package, where team-specific parameters for attack, defense, and home advantage were
estimated using Markov Chain Monte Carlo (MCMC) techniques.
After the parameter estimation, two simulation methods were employed to generate probabilistic
forecasts: a round-by-round and a full-season one. In order to check the performance of the model the
first simulation method was replicated with 3 other Poisson models to check if the handling of zero
inflation and deflation really gave an advantage in the predictive power of the ZMP
Descrição
Tese de mestrado, Bioestatística, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Futebol Poisson Modificada em Zero Estatística Bayesiana Modelos Preditivos Inferência Estatística Teses de mestrado - 2025