Predicting Football Match Results Using a Poisson Regression Model

Autor: Konstantinos Loukas, Dimitrios Karapiperis, Georgios Feretzakis, Vassilios S. Verykios
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Applied Sciences, Vol 14, Iss 16, p 7230 (2024)
Druh dokumentu: article
ISSN: 2076-3417
DOI: 10.3390/app14167230
Popis: 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.
Databáze: Directory of Open Access Journals