Abstract
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.
Highlights
Just like the ever-increasing importance of football in the media and the fast growth of the related market, the analysis of football games is becoming more and more important
To extract ball movement patterns, which may occur during sequences of passes, [22] proposes a step-wise mining method, which uses different similarity measures to compare the ball’s trajectory and encounters translation, scaling and rotation invariance
It starts with an input, which is the trajectory data provided by some tracking solution
Summary
Just like the ever-increasing importance of football in the media and the fast growth of the related market, the analysis of football games is becoming more and more important. The evaluation usually consists of different analyses, which provide information about the players’ or teams’ performances They increase the knowledge about the players’ or teams’ behavior during the game. The “basic” analyses are of the lowest complexity, but they provide the least knowledge about the players’ movement behavior or tactics. They mainly consist of pure measurements or simple aggregations. ABlyetsiicdael spaudrpaotasbesa.seBaensiadleyssisa tdoaotla, bthaesye apnroavlyidsies atopopls, ftohreyanparloyvziidnge athpepcsufrorrenant amlyaztcinhg, ththeerecfuerrreeenat nmdaatclsho, tthhee nreefxetroeeppaonsdinaglstoeatmhe opposing team based on its last recorded matches Since they track the ball, they are able to carry out ball related analyses, e.g., for passes or goal kicks.
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