Match Analysis 4.0 with Big Data: From Studies to Experiments

Abstract

The paper provides an overview of the development of match analysis in recent years. Based on technological developments in sensor technology, especially in the field of commercial football, coupled with changes in media preparation of sports games, new types of performance evaluation have been established. The massive increase in available data consolidated under the term ‘big data’ makes it possible to calculate more complex performance indicators. Based on the positional data of the individual players and the ball, analyses can be significantly faster than with video-based material. Whereas in the past, the focus was on analyzing frequencies of certain game events, it is now possible to calculate specific metrics. These metrics make it possible to portrait the performance of teams and individual players as well as the interaction dynamics between teams. However, it is shown that the actual significance for the performance of many of these new performance indicators (KPIs) is often still insufficiently scientifically proven. In one of the largest big data field studies conducted so far, a big data field study defined various KPIs in professional football and validated them in the first steps. The paper offers an outlook to make hypotheses empirically verifiable through field experiments based on positional data. Such an experimental paradigm would be appealing, as it would be able to generate real data in an 11 vs. 11 football game, theory-guided (not post-hoc testing), reliable, objective with corresponding KPIs, and extremely fast.