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arXiv · 2407.14558

A Foundation Model for Soccer

Ethan Baron·Daniel Hocevar·Zach Salehe
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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.

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Ethan Baron, Daniel Hocevar, Zach Salehe. 2024-07-18. A Foundation Model for Soccer. https://arxiv.org/abs/2407.14558

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