  The expected goal models have gained popularity, but their interpretability
is often limited, especially when trained using black-box methods. Explainable
artificial intelligence tools have emerged to enhance model transparency and
extract descriptive knowledge for a single observation or for all observations.
However, explaining black-box models for a specific group of observations may
be more useful in some domains. This paper introduces the glocal explanations
(between local and global levels) of the expected goal models to enable
performance analysis at the team and player levels by proposing the use of
aggregated versions of the SHAP values and partial dependence profiles. This
allows knowledge to be extracted from the expected goal model for a player or
team rather than just a single shot. In addition, we conducted real-data
applications to illustrate the usefulness of aggregated SHAP and aggregated
profiles. The paper concludes with remarks on the potential of these
explanations for performance analysis in soccer analytics.
