Searching tactical patterns in soccer game play by unsupervised machine learning
|R. Leser, T. Hoch, B. Moser, A. Baca. Searching tactical patterns in soccer game play by unsupervised machine learning. volume Book of Abstracts of the 21st annual Congress of the European College of Sport Science (ECSS 2016), pages 548, 7, 2016.|
|Band||Book of Abstracts of the 21st annual Congress of the European College of Sport Science (ECSS 2016)|
Location systems in game sports provide a wealth of data, which is capable of reconstructing game play from a tactical point of view. Precise position measurements up to 50 times per second/player during an entire soccer game enable in depth analyses of individual and collective game behavior.
Small sided games (SSGs) are used in soccer training to simulate specific game situations. By the given constraints (smaller pitch size, lower number of players) SSGs are used to provoke intended player behavior with a higher rate than it occurs in the full size game. Due to these properties SSGs are also suited to analyse successful/unsuccessful tactical patterns in a reasonable framework.