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Expected Frequencies in Goodness-of-Fit Tests01:19

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Toward interpretable expected goals modeling using Bayesian mixed models.

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This study introduces a simple, interpretable Bayesian model for predicting eXpected Goals (xG) in soccer matches. The model achieves performance comparable to complex methods, offering valuable insights for sports analytics and betting.

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Area of Science:

  • Sports Analytics
  • Statistical Modeling
  • Machine Learning in Sports

Background:

  • Sports teams and bookmakers seek to understand player/team activity and match outcomes.
  • eXpected Goals (xG) models offer insights into performance but often lack interpretability.
  • Complex statistical and machine learning models are currently used for outcome prediction.

Purpose of the Study:

  • To develop a simple and interpretable eXpected Goals (xG) modeling approach.
  • To compare the performance of the proposed model against existing methods.
  • To leverage transfer learning for analyzing team strengths/weaknesses with limited data.

Main Methods:

  • Bayesian generalized linear mixed-effects model for xG.
  • Utilized seven key variables: shot type, position, and opponent proximity.
  • Employed transfer learning for pre-trained models.

Main Results:

  • The Bayesian xG model demonstrated comparable performance to the StatsBomb model (AUC = 0.781 vs. 0.801).
  • The model effectively uses a limited set of variables for prediction.
  • Pre-trained models facilitated identification of team strengths/weaknesses with small datasets.

Conclusions:

  • A simple, interpretable Bayesian model for xG is feasible and effective.
  • This approach enhances the practical application of advanced analytics in sports.
  • Transfer learning offers a powerful tool for analyzing team performance dynamics.