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Predicting goal probabilities with improved xG models using event sequences in association football
Ishara Bandara1,2, Sergiy Shelyag1,3, Sutharshan Rajasegarar1
1School of IT, Deakin University, Melbourne, Australia.
Plos One
|October 30, 2024
Summary
Predicting football shots is improved by a new framework analyzing preceding events. This enhances expected goals (xG) accuracy, offering better performance evaluation and strategy design.
Area of Science:
- Sports Analytics
- Football Performance Metrics
Background:
- Predicting shot outcomes in association football is crucial for performance analysis and strategy.
- Existing Expected Goals (xG) models offer valuable insights but can be enhanced.
Purpose of the Study:
- To propose a novel framework for improving the accuracy of Expected Goals (xG) metrics.
- To incorporate temporal features from preceding events into xG modeling.
Main Methods:
- Utilized a random forest model incorporating previously explored and new temporal features.
- Introduced novel features such as "advancement factor" and "player position column".
Main Results:
- The proposed framework demonstrated superior performance compared to single-event-based models.
- Significant improvements in model accuracy were achieved by including preceding event information.
Conclusions:
- The novel framework enhances xG prediction accuracy by considering the sequence of events.
- Identified key event sequences, such as build-up from the 18-yard box sides and passes to the far post, that improve xG.
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