Aim. Our research examined the predictive capabilities of mathematical models that are solely based on the expected goal statistics obtained from a publicly available database. Method. We collected match and expected goals data for 310 matches from three European Leagues (Bundesliga, La Liga, and Serie A). We created three probabilistic models based on the expected goals statistic and compared them with two well-established probabilistic models using binomial deviance, squared error, and profitability in the betting market as evaluation metrics. Results. Our best model adjusted the expected goal statistics for homefield advantage and outperformed the two probabilistic models used for comparison. Two of our models were profitable under certain betting conditions. Limitations. Our models explored a simplistic integration of expected goals into a Poisson based probabilistic model and did not include other contributing factors such as a team’s defensive prowess. The number of games simulated was also limited due to the premature closure of the European Leagues due to the COVID-19 pandemic. Conclusions. The use of a probabilistic model based solely on expected goals score statistic can provide some meaningful insight into forecasting the outcome of a football match and can develop useful betting strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.