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.

翻译：期望进球模型已获得广泛应用，但其可解释性常受限于黑箱训练方法。可解释人工智能工具的出现提升了模型透明度，并能针对单一观测或全体观测提取描述性知识。然而，在某些领域，针对特定观测群组解释黑箱模型可能更为有效。本文提出在球员和球队层面实现绩效分析的期望进球模型全局-局部（介于局部与全局之间）解释方法，通过采用SHAP值的聚合版本与部分依赖剖面实现。这使得能从期望进球模型中提取针对球员或球队的知识，而非仅针对单次射门。此外，我们通过真实数据应用案例验证了聚合SHAP值与聚合剖面的实用性。论文最后讨论了这些解释方法在足球数据分析中用于绩效分析的潜力。