Decoding attacking success in soccer – a data-driven analysis of expected possession value in the Bundesliga

Understanding the key factors driving attacking success represents a critical challenge in soccer match analysis. A promising approach to address this issue is the application of expected possession value (EPV) models. Therefore, this paper aims to develop an EPV model with high explainability to provide detailed practical insights into the keys to attacking success. Tracking and event data of the Bundesliga season 2022/23 were analyzed (306 matches). From three main categories (match performance offense, defense, & match situation context), 21 features were carefully selected by professional match analysts. Afterward, machine learning classifiers were used (e.g. Random Forest, XGBoost) to predict the goal probability of possessions. The selected EPV model showed satisfactory prediction performance (xGBoost: Accuracy = 0.99, Recall = 0.06, F1-Score = 0.10, AUC = 0.85, logloss = 0.05, ECE = 0.01). The most important features in predicting attacking success were the distance (1st) and angle (2nd) to the goal, the offensive space control in the final third (3rd), and the relative pitch position (4th, defined by the number of outplayed opposing formation lines). By applying the presented EPV model and interpreting its most important features in individual match situations or over a whole match highly practice-relevant information about the tactical match performance of players can be gained.