ABSTRACT
Two interlinked challenging tasks characterize the problem of analysing sports games: recording complex process game data and transforming them into useful information. These days, a significant part of the first task can be carried out using automatic position recording, thus placing increased emphasis on the second task; tracking a soccer game at 25 frames per second results in about 135,000 frames per game, which sums to about 6 million x-y coordinate data per game. Nevertheless, an experienced coach can filter significant information from these data and recognize patterns of player constellations in the playing processes. Neural networks using self-organizing maps (SOMs; see Kohonen, 1995) can recognize patterns in large data sets too and hence net-based data analysis can support the coach’s work. The first ideas of net-based analysis of sports games date back a few years, with recent advances in automatic position recording lending increased attention to complex games like soccer, handball, or basketball. Following a brief introduction to net-based game analysis, the chapter introduces the basics of net-based handling of process data and reports three instances detailing conceptual and methodical approaches of current research projects investigating handball, basketball, and soccer, respectively.
