A Foundation Model for Soccer
<|reference_start|>A Foundation Model for Soccer: 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.<|reference_end|>
@article{baron2024a,
    title={A Foundation Model for Soccer},
    author={Ethan Baron and Daniel Hocevar and Zach Salehe},
    journal={arXiv preprint arXiv:2407.14558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14558},
    primaryClass={cs.LG}
}