Towards a foundation large events model for soccer

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Mendes Neves, T (Author) Other; Meireles, L (Author) Other; João Mendes-Moreira (Author) FEUP

Journal

Title: Machine Learning

Vol. 113

Pages: 8687-8709

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-016-ZEZ

DOI: 10.1007/s10994-024-06606-y

Abstract (EN): This paper introduces the Large Events Model (LEM) for soccer, a novel deep learning framework for generating and analyzing soccer matches. The framework can simulate games from a given game state, with its primary output being the ensuing probabilities and events from multiple simulations. These can provide insights into match dynamics and underlying mechanisms. We discuss the framework's design, features, and methodologies, including model optimization, data processing, and evaluation techniques. The models within this framework are developed to predict specific aspects of soccer events, such as event type, success likelihood, and further details. In an applied context, we showcase the estimation of xP+, a metric estimating a player's contribution to the team's points earned. This work ultimately enhances the field of sports event prediction and practical applications and emphasizes the potential for this kind of method.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23