A knowledge discovery framework for the assessment of tactical behaviour in soccer based on spatiotemporal data
|Titel||A knowledge discovery framework for the assessment of tactical behaviour in soccer based on spatiotemporal data|
|Journal||Mathematical and Computer Modelling of Dynamical Systems|
This paper addresses the problem of designing an explanatory computational model for the assessment of individual tactic skills in team sports. The modeling approach tackles the complexity and difficulty of this problem by fusing fuzzy human-like knowledge related to tactical behavior 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. Conceptually this layer can be represented by a state-transition system. 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. Behavioral patterns are then extracted using unsupervised clustering techniques in the feature space induced by the transition moments. 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. This provides the coach with a more holistic and sophisticated view of the players' performance.