Quantification of Turnover Danger with xCounter

Counterattacks in soccer are an important strategical component for goal scoring. Previous work in the literature has described their impact and has formulated descriptive advice on successful actions during a counterattack. In contrast, in this work, we propose the notion of expected counter, i.e., quantifying forward progress by the ball-winning team at the moment of the turnover. Therefore, we apply a previously proposed framework for understanding complex sequences in soccer. Using this framework, we perform a novel feature-specific assessment that yields (a) critical feature values, (b) relevant feature pitch zones, and (c) feature prediction capabilities. The insights from this assessment step allow for creating concrete guidelines for optimal behavior in and out of possession. Thus, we find that preparing horizontally spaced pass options facilitates an own counterattack in case of a ball win while moving as a compact unit prevents an opposing counterattack in case of a ball loss. As a final step, we generalize our results by creating a predictive XGBoost model that outperforms a location-based baseline but still shows room for improvement.