A novel method for the analysis of sequential actions in team handball

Performance in team sports crucially depends on the knowledge about the own and the opponents strengths and weaknesses. Since the analysis of single actions only provides restricted information on the game process, the analysis of sequential actions is from great importance to understand team tactics. In this paper, we introduce a novel method to analyze tactical behavior in team sports based on action sequences of positional data which are subsequently analyzed with artificial neural networks. We present custom-made software which allows annotating single actions with accurate manual position information. The process of building action sequences with the notational information of single actions in team handball is described step by step and the accuracy of the position determination is evaluated. The evaluation revealed a mean error of 0.16m (± 0.17m) for field positions on a handball field. Interand intra-rater reliability for identical camera setups are excellent (ICC=0.92 and 0.95 resp.). However, tests revealed that position accuracy is depending on camera setup (ICC=0.36). The results of the study demonstrate the applicability of the described method to gain action sequence data with accurate position information. The combination with neural networks gives an alternative approach to T-patterns for the analysis of sport games.