## 1 Introduction
Despite the emphasis on the need for richer predictive features and more robust validation, many studies still rely on existing features [26]. Factors such as the nature of the features used, the complexity of the predictive problem, and the size of the dataset significantly influence prediction accuracy. This paper aims to position itself uniquely within the realm of football match outcome prediction by shifting the focus from predicting specific match outcomes to analyzing a team's overall performance throughout the year. The main novelties of this study include identifying key parameters that contribute to the overall scoring performance of a team and utilizing the extensive dataset collected during the regular season of the top division in 11 European countries. When referencing a team's overall scoring performance, we are referring to the average goal difference for the team throughout the entire season. Moreover, this research employs a comprehensive feature dataset comprising 160 parameters, providing a richer context for analysis and potentially enhancing the accuracy and insights derived from the study. Moreover, one of the major challenges with using AI in sports performance analysis is the lack of transparency and interpretability of the results. Traditional AI models, such as neural networks, can be difficult to understand and interpret, making it hard to explain how and why a particular decision or prediction was made. This is where explainable AI (XAI) comes in. Explainable AI is a subfield of AI that focuses on creating models that can provide clear and interpretable explanations for their predictions and decisions [27]. One of the most popular methods for explainable AI is SHapley Additive exPlanations (SHAP), which is a unified method for interpreting the predictions of any machine learning model [28]. It is based on the concept of Shapley values from cooperative game theory, which provides a way to fairly distribute a value among a group of individuals, such as the players on a football team. Recently, two studies [29; 30] have employed SHAP as a post-hoc explainability tool to evaluate the impact of each feature on the final outcome, specifically in the context of match-specific score prediction. Both studies primarily concentrated on individual match predictions and were limited to data from a single league, utilizing a moderately sized feature set. The objective of this paper is to use XAI to identify the key team-level performance metrics that are most important in predicting a football team's performance. By concentrating on overall performance and identifying critical parameters influencing scoring performance (average goal difference over the season), this paper seeks to offer a broader understanding of the factors that determine success in football. The use of an expansive dataset from multiple European leagues, combined with an extensive set of features, sets this study apart from previous research and enhances its potential to uncover novel insights and patterns in football performance. This approach allows for a more transparent and interpretable understanding of how a machine learning model is making its predictions, which can be particularly useful in high-stakes decision-making scenarios such as predicting a team's performance in a football match. By leveraging explainable AI, coaches and analysts can gain a deeper understanding of team performance and make more informed decisions about player recruitment and opposition analysis. The structure of the paper is as follows: In Section 2, the proposed methodology and characteristics of the dataset are presented. The results of the study, including the performance of the proposed ML model and the explanations generated at both the global and local levels, are discussed in Section 3. The implications and limitations of the study are discussed in Section 4, and conclusions are presented in Section 5.