A Foundation Model for Soccer

2024/07/18 by Ethan Baron, Baron, Ethan, Daniel Hocevar +3 · 2 citations
Economics, Econometrics and Finance · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sports Analytics and Performance

paper · pdf · doi:10.48550/arxiv.2407.14558

openalex publication_date 2024/07/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a foundation model for soccer, which is able to predict subsequent actions in a soccer match from a given input sequence of actions. As a proof of concept, we train a transformer architecture on three seasons of data from a professional soccer league. We quantitatively and qualitatively compare the performance of this transformer architecture to two baseline models: a Markov model and a multi-layer perceptron. Additionally, we discuss potential applications of our model. We provide an open-source implementation of our methods at https://github.com/danielhocevar/Foundation-Model-for-Soccer.

Cited by

• Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos
• Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football

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