Analyzing sports like football is interesting not only for the sports team itself, but also for the public and the media. Both have recognized that using more detailed analyses of the teams’ behavior increases their attractiveness and also their performance. For this reason, the games and the individual players are recorded using specially developed tracking systems. The tracking solution usually comes with elementary analysis software allowing for basic statistical information extraction. Going beyond these simple statistics is a challenging task. However, it is worthwhile when it provides a better view into the tactics of team or the typical movements of an individual player. In this paper an approach for the recognition of movement patterns as an advanced analysis method is presented, which uses the players’ trajectories as input data. Besides individual movement patterns it is also able to detect patterns in relation to group movements. A detailed description is followed by a discussion of the approach, where different experiments on real trajectory datasets, even from other contexts than football, show the method’s benefits and features.
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