Towards a foundation large events model for soccer

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Mendes Neves, T (Autor) Outra; Meireles, L (Autor) Outra; João Mendes-Moreira (Autor) FEUP

Revista

Título: Machine Learning

Vol. 113

Páginas: 8687-8709

ISSN: 0885-6125

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23