Summary of Expected Possession Value Of Control and Duel Actions For Soccer Player’s Skills Estimation, by Andrei Shelopugin
Expected Possession Value of Control and Duel Actions for Soccer Player’s Skills Estimation
by Andrei Shelopugin
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed extensions to the expected possession value (EPV) model aim to address challenges in estimating football players’ skills. The revised model incorporates a decay effect, assigning greater weights to events preceding shots, and more accurately considers possession risk and effective playing time. Additionally, individual player ability is assessed by evaluating aerial and ground duels. The extended EPV model predicts player metrics for the upcoming season, taking into account opponent strength. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Football players’ skills are hard to estimate in sports analytics. This paper improves a popular method called expected possession value (EPV). It makes the model better by considering what happened right before shots were taken and how likely players are to lose the ball. The improved EPV also looks at how well players do in one-on-one situations like aerial and ground duels. Using this new model, the authors predict which players will perform well next season and take into account who they’ll be playing against. |
