Match Analysis 4.0 with Big Data: From Studies to Experiments
The lecture 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 the analysis of frequencies of certain game events, it is now not only possible to calculate specific metrics, but they are applied already (Memmert & Rein, 2018). These metrics make it possible to picture the performance of teams and individual players as well as the interaction dynamics between teams (Memmert et al., 2017; Low et al., 2019). It is shown that the actual significance for the performance of many of these new performance indicators (KPIs) is often still insufficiently scientifically proven (Memmert & Raabe, 2018). In one of the largest big data field studies conducted so far, the DFL-funded big data field study (Memmert et al., 2016) therefore defined various KPIs in professional football and validated them in the first steps. The lecture offers an outlook on why it is necessary to develop models in order to specify the link between big data and a “match analysis 4.0” (Rein & Memmert, 2016) and to make the resulting 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.
Low, B., Coutinho, D., Gonçalves, B., Rein, R., Memmert, D., & Sampaio, J. (2019). A systematic review of collective tactical behaviours in football using positional data. Sports Medicine, 1-43.
Memmert, D., & Rein, R. (2018). Match analysis, Big Data and tactics: current trends in elite soccer. German Journal of Sport Medicine, 69, 65-72.
Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Positional Data Collection, Modelling and Analysis. Abingdon: Routledge.
Memmert, D., Lemmink, K. A. P. M., & Sampaio, J. (2017). Current approaches to tactical performance analyses in soccer using position data. Sports Medicine, 47, 1-10.
Memmert, D., Raabe, D., Knyazev, A., Franzen, A., Zekas, L., Rein, R., Perl, J. & Weber, H. (2016). Big Data im Profi-Fußball. Analyse von Positionsdaten der Fußball-Bundesliga mit neuen innovativen Key Performance Indikatoren. Leistungssport, 46, 1-13.
Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5, 1410.