Summary of The Strain Of Success: a Predictive Model For Injury Risk Mitigation and Team Success in Soccer, by Gregory Everett et al.
The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
by Gregory Everett, Ryan Beal, Tim Matthews, Timothy J. Norman, Sarvapali D. Ramchurn
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel approach for selecting soccer teams, taking into account the uncertainty of player injuries. By modeling player-specific information learned from real-world data, the model optimizes long-term team performance across a season using Monte-Carlo Tree Search. The approach is validated against benchmark solutions for the 2018/19 English Premier League season, achieving similar expected points while reducing first-team injuries by 13% and inefficient spending on injured players by 11%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to pick soccer teams in a way that considers when players might get hurt. By using real data from professional soccer games, the researchers created a special model that can choose teams for games based on which players are likely to get injured and be unavailable. This is important because it could help reduce injuries and save money on treatments. The model was tested against other ways of picking teams and showed promising results. |
