A Foundation Model for Soccer
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910536557395968 |
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| author | Baron, Ethan Hocevar, Daniel Salehe, Zach |
| author_facet | Baron, Ethan Hocevar, Daniel Salehe, Zach |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Foundation Model for Soccer Baron, Ethan Hocevar, Daniel Salehe, Zach Machine Learning 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. |
| title | A Foundation Model for Soccer |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.14558 |