# Glocal Explanations of Expected Goal Models in Soccer

arXiv: https://arxiv.org/abs/2308.15559 · HTML: https://mlsanity.com/paper/2308.15559/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [GLocalX -- From Local to Global Explanations of Black Box AI Models](https://mlsanity.com/paper/2101.07685/) (2021)
- [DALEX: explainers for complex predictive models](https://mlsanity.com/paper/1806.08915/) (2018)
- [Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring](https://mlsanity.com/paper/2009.13384/) (2020)
- [A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions](https://mlsanity.com/paper/2011.09426/) (2020)
- [Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study](https://mlsanity.com/paper/2103.09627/) (2021)
- [Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations](https://mlsanity.com/paper/2211.11426/) (2022)
- [Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies](https://mlsanity.com/paper/2012.08333/) (2020)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set](https://mlsanity.com/paper/2110.13369/) (2021)
- [A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)](https://mlsanity.com/paper/2301.13052/) (2023)
- [Generalized SHAP: Generating multiple types of explanations in machine learning](https://mlsanity.com/paper/2006.07155/) (2020)
- [Revealing drivers and risks for power grid frequency stability with explainable AI](https://mlsanity.com/paper/2106.04341/) (2021)
- ["Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer](https://mlsanity.com/paper/2105.13778/) (2021)
- [Variable importance without impossible data](https://mlsanity.com/paper/2205.15750/) (2022)
- [Explainable expected goal models for performance analysis in football analytics](https://mlsanity.com/paper/2206.07212/) (2022)
- [Exploring deterministic frequency deviations with explainable AI](https://mlsanity.com/paper/2106.09538/) (2021)
- [mSHAP: SHAP Values for Two-Part Models](https://mlsanity.com/paper/2106.08990/) (2021)
- [Data-Driven Approach for Log Instruction Quality Assessment](https://mlsanity.com/paper/2204.02618/) (2022)

## Built on by (most cited first, 4 of 4)

- [CohEx: A Generalized Framework for Cohort Explanation](https://mlsanity.com/paper/2410.13190/) (2024)
- [Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation](https://mlsanity.com/paper/2410.14082/) (2024)
- [Implet: A Post-hoc Subsequence Explainer for Time Series Models](https://mlsanity.com/paper/2505.08748/) (2025)
- [Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition](https://mlsanity.com/paper/2605.10796/) (2026)

## Abstract (arXiv)

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.