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Expected goals in football: Improving model performance and demonstrating value.

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Summary

This study enhances expected goals (xG) modeling in football using machine learning and new features. The improved xG metric predicts team success better than traditional stats and industry standards.

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Area of Science:

  • Sports Analytics
  • Football Performance Analysis
  • Machine Learning in Sports

Background:

  • Football analytics increasingly uses novel metrics like expected goals (xG) for performance evaluation and financial decisions.
  • Traditional xG models lack crucial features such as player/team ability and psychological effects, limiting their trust and accuracy.
  • There is a need for more robust xG models that incorporate advanced features and are widely accepted.

Purpose of the Study:

  • To develop an enhanced expected goals (xG) model using machine learning techniques.
  • To incorporate previously untested features, including player/team ability and psychological factors, into xG modeling.
  • To compare the predictive performance of the new xG metric against traditional football statistics and industry benchmarks.

Main Methods:

  • Implementation of machine learning algorithms to model expected goals (xG) values.
  • Inclusion of novel features (e.g., player/team ability, psychological effects) not typically found in standard xG models.
  • Comparative analysis of predictive accuracy between the enhanced xG model, traditional statistics, and existing industry models.

Main Results:

  • The developed xG models demonstrated competitive error values compared to optimal benchmarks in existing literature.
  • Several newly incorporated features were found to significantly impact the output of the expected goals models.
  • The enhanced expected goals metric proved to be a superior predictor of future football team success compared to traditional statistics.

Conclusions:

  • Machine learning can significantly improve the accuracy and scope of expected goals (xG) modeling in football.
  • Incorporating advanced features enhances the predictive power of xG, leading to more reliable performance evaluations.
  • This study's enhanced xG model outperforms both traditional metrics and current industry-leading approaches in predicting football outcomes.