A knowledge discovery framework for the assessment of tactical behaviour in soccer based on spatiotemporal data

This paper addresses the problem of designing an explanatory computational model for the assessment of individual tactic skills in team sports. The modelling approach tackles the complexity and difficulty of this problem by fusing fuzzy human-like knowledge related to tactical behaviour with time-continuous position data from a tracking system. For this purpose, a hierarchical architecture is proposed. The bottom layer is represented by physically meaningful variables derived from time-continuous position data at specific time instances. Based thereupon, we introduce a temporal segmentation layer that relates the physical variables to game-situation-specific temporal phases. We show how the vague and imprecisely defined linguistic description of the task at hand can be transferred to fuzzy rules in order to get a meaningful temporal segmentation of the time-continuous position data. Finally, the resulting clusters are interpreted in terms of performance indicators in the top layer in order to provide a meaningful explanatory model for the assessment. We show the usefulness of our approach for the task of player evaluation. We do not only provide the coach with a single number to describe the players' performance but also relate this number to the measurement variables, presenting a more holistic and sophisticated view of the players' performance.