Summary of Investigating Fouling Efficiency in Football Using Expected Booking (xb) Model, by Adnan Azmat et al.
Investigating Fouling Efficiency in Football Using Expected Booking (xB) Model
by Adnan Azmat, Su Su Yi
First submitted to arxiv on: 16 Jan 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 This paper introduces the Expected Booking (xB) model, a novel metric for estimating the likelihood of a foul resulting in a yellow card in football. The xB model leverages ensemble methods and an expanded dataset to demonstrate improved performance, even with additional features. Validation through analysis of FIFA World Cup 2022 data shows the model’s efficacy in providing insights into team and player fouling tactics, aligning with actual defensive performance. This research fills a gap in fouling efficiency examination by emphasizing defensive strategies often overlooked. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to measure how likely it is for a football foul to result in a yellow card. The new method, called the Expected Booking model, is better at making these predictions when you add more information and use a bigger dataset. It even works well with data from the 2022 FIFA World Cup! This helps us understand what teams and players do during games. |
Keywords
* Artificial intelligence * Likelihood
