Biases in Expected Goals Models Confound Finishing Ability
Jesse Davis Pieter Robberechts
Expected Goals (xG) has emerged as a popular tool for evaluating finishing
skill in soccer analytics. It involves comparing a player's cumulative xG with
their actual goal output, where consistent overperformance indicates strong
finishing ability. However, the assessment of finishing skill in soccer using
xG remains contentious due to players' difficulty in consistently outperforming
their cumulative xG. In this paper, we aim to address the limitations and
nuances surrounding the evaluation of finishing skill using xG statistics.
Specifically, we explore three hypotheses: (1) the deviation between actual and
expected goals is an inadequate metric due to the high variance of shot
outcomes and limited sample sizes, (2) the inclusion of all shots in cumulative
xG calculation may be inappropriate, and (3) xG models contain biases arising
from interdependencies in the data that affect skill measurement. We found that
sustained overperformance of cumulative xG requires both high shot volumes and
exceptional finishing, including all shot types can obscure the finishing
ability of proficient strikers, and that there is a persistent bias that makes
the actual and expected goals closer for excellent finishers than it really is.
Overall, our analysis indicates that we need more nuanced quantitative
approaches for investigating a player's finishing ability, which we achieved
using a technique from AI fairness to learn an xG model that is calibrated for
multiple subgroups of players. As a concrete use case, we show that (1) the
standard biased xG model underestimates Messi's GAX by 17% and (2) Messi's GAX
is 27% higher than the typical elite high-shot-volume attacker, indicating that
Messi is even a more exceptional finisher than people commonly believed.