Predicting Football Match Results Using a Poisson Regression Model

Author:

Loukas Konstantinos1,Karapiperis Dimitrios2ORCID,Feretzakis Georgios1,Verykios Vassilios S.1ORCID

Affiliation:

1. School of Science and Technology, Hellenic Open University, 26335 Patras, Greece

2. School of Science and Technology, International Hellenic University, 57001 Thermi, Greece

Abstract

Currently, several techniques based on probabilities and statistics, along with the rapid advancements in computational power, have deepened our understanding of a football match result, giving us the capability to estimate future matches’ results based on past performances. The ability to estimate the number of goals scored by each team in a football match has revolutionized the perspective of a match result for both betting market professionals and fans alike. The Poisson distribution has been widely used in a number of studies to model the number of goals a team is likely to score in a football match. Therefore, the match result can be estimated using a double Poisson regression model—one for each participating team. In this study, we propose an algorithm, which, by using Poisson distributions along with football teams’ historical performance, is able to predict future football matches’ results. This algorithm has been developed based on the Premier League’s—England’s top-flight football championship—results from the 2022–2023 season.

Publisher

MDPI AG

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