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.
@article{arxiv.2407.14558,
title = {A Foundation Model for Soccer},
author = {Ethan Baron and Daniel Hocevar and Zach Salehe},
journal= {arXiv preprint arXiv:2407.14558},
year = {2024}
}