Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer

Music City Center 

Expected goals (xG) models are widely used to evaluate team match performance in goal-based sports such as soccer and hockey. However, these models rely exclusively on observed shots, omitting crucial information about near-chances, shots nullified by fouls, and other unrealized shot attempts. Just as xG enhances our understanding of match outcomes by modeling the randomness of goal-scoring, we propose a method that accounts for the randomness of shot occurrence. We first fit a multinomial shot probability model to estimate the likelihood of different shot types occurring during a possession, enabling us to account for possessions where a shot could have occurred but did not. We then integrate these probabilities with existing xG models to construct a re-weighted expected goals metric that more accurately reflects team offensive performance and better aligns with intuitive evaluations of how they played. Finally, we evaluate the effectiveness of our approach by comparing its descriptive and predictive power against standard xG models. We demonstrate that our refined metric provides a more comprehensive and accurate assessment of team quality and scoring potential.

Expected Goals

Sports Analytics

Statistical Learning

Team Performance

Multinomial Models 

Section on Statistics in Sports