Summary of Revisiting Playerank, by Louise Schmidt and Cristian Lillo and Javier Bustos
Revisiting PlayeRank
by Louise Schmidt, Cristian Lillo, Javier Bustos
First submitted to arxiv on: 26 Oct 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 revised PlayeRank performance score aims to correct inconsistencies in the original design by Pappalardo et al. (2019). A Linear Support Vector Machine (SVM) is used to classify events based on their impact on a match’s outcome. Inconsistencies are attributed to including Goal-Scored events during training, which affects the model’s weights. The revised paper presents new weights that can accurately solve this problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers reworked the PlayeRank score, originally designed by Pappalardo et al. in 2019. They looked at how a special machine learning tool (SVM) decided which events made a match more likely to be won. The old results didn’t add up because they included something important during training – scoring goals! This caused problems with the model’s weights. Now, the team has new weights that can help solve this issue. |
Keywords
* Artificial intelligence * Machine learning * Support vector machine
