Summary of Contextual Sprint Classification in Soccer Based on Deep Learning, by Hyunsung Kim et al.
Contextual Sprint Classification in Soccer Based on Deep Learning
by Hyunsung Kim, Gun-Hee Joe, Jinsung Yoon, Sang-Ki Ko
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 A deep learning framework is proposed to automatically classify high-intensity runs (sprints) in soccer into contextual categories, addressing the scalability limitation of manual classification by human experts. The model combines Set Transformers and a bidirectional GRU to handle permutation-invariant and sequential nature of multi-agent trajectories in soccer. Trained with category labels from human annotators and rule-based classifier, the model achieves an accuracy of 77.65% on the test dataset, demonstrating potential for large-scale analysis of soccer sprints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to automatically sort soccer plays into different categories based on their purpose. This helps analyze how players move during games. The method uses special computer models and training data from human experts. It’s more efficient than having people do this work by hand. In tests, the model was able to correctly categorize most of the sprints in a dataset. |
Keywords
* Artificial intelligence * Classification * Deep learning
