Summary of A Foundation Model For Soccer, by Ethan Baron and Daniel Hocevar and Zach Salehe
A Foundation Model for Soccer
by Ethan Baron, Daniel Hocevar, Zach Salehe
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This foundation model for soccer leverages a transformer architecture to predict subsequent actions in a match from an input sequence of actions. By training the model on three seasons of data from a professional league, researchers demonstrated its effectiveness through quantitative and qualitative comparisons with two baseline models: Markov and multi-layer perceptron. The model’s potential applications were also discussed. An open-source implementation of the methods is available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer program that can predict what will happen in a soccer game based on what has happened so far. They tested this program using data from three years of professional soccer games and found it was better at predicting the future than simpler programs. The program could be used for things like helping teams make decisions during a game or creating more realistic video game simulations. |
Keywords
* Artificial intelligence * Transformer
