Summary of Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models, by Tiago Mendes-neves et al.
Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models
by Tiago Mendes-Neves, Luís Meireles, João Mendes-Moreira
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces Large Event Models (LEMs), a novel application of AI to soccer analytics. LEMs learn the language of soccer by predicting variables for subsequent events, allowing them to simulate matches and predict player performance across different teams. The authors fine-tune LEMs with the WyScout dataset for the 2017-2018 Premier League season to gain insights into player contributions and team strategies. The models are adapted to reflect the nuances of soccer, enabling the evaluation of hypothetical transfers. The findings demonstrate the effectiveness and limitations of LEMs in soccer analytics, highlighting their ability to forecast teams’ expected standings and explore scenarios like transferring Cristiano Ronaldo or Lionel Messi. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses AI to help predict how well soccer players will do on different teams. It’s like a super smart computer that can understand soccer language! The researchers used a special dataset from the Premier League to teach the computer how to think about player performance and team strategies. They even tested it by pretending that famous players like Cristiano Ronaldo or Lionel Messi were playing for different teams. This showed that while general statistics might make some players seem very different, they actually perform similarly well on their own team. |
