Company / Club

Deutsche Sportschule Köln

Position inside the Company/Club

Professor and head of the Institute of Cognitive and Team/Racket Sport

Speaker Biography

Daniel Memmert is a professor and head of the Institute of Cognitive and Team/Racket Sport Research, German Sport University Cologne, Cologne, Germany, with a visiting assistant professorship 2014 at the University of Vienna (Austria). He studied physical education for high school teaching (PE, math, sport, and ethic) and has trainer licences in soccer, tennis, snowboard, and skiing. Memmert received his PhD (basic cognition in team sports) and habilitation (creativity in team sports) in sport science from the Elite University of Heidelberg. In 2010 he was awarded 3rd place with Germany's most renowned German Olympic Sports Confederation (DOSB) Science Award. His special research areas of interests are cognitive science, human movement science, computer science, and sport psychology. He has 16 years of teaching and coaching experience, has an H-index of 27, and has authored or co-authored more than 150 publications, 40 books or book chapters, and he is an ad-hoc reviewer for several international psychology and sport psychology journals. In addition, he gave more than 80 invited talks, 90 scientific talks on conferences, and more than 100 teaching courses for PE teachers and trainers. He collaborates with researchers from the US, Canada, Brazil, and Spain. He transfers his expertise to business companies and professional soccer clubs (e.g., 1. Bundesliga, Champions-/European League) and organize the first international master in “Performance Analysis/Game analysis”.




Presentation Title

Big Data in Soccer: New KPIs in German 1. Bundesliga based on Position Data

Presentation synopsis

State of the art of research as well as public interest are calling for a detailed and objective scientific analysis of soccer matches (Memmert et al., 2016). The main aim of this talk is the quick and valid identification of key performance indicator (KPI) in German 1. Bundesliga. Here, some novel objective analysis tools come into play, e.g., neural networks (for an overview, Perl & Memmert, 2012), which can identify tactical pattern based on position data. In the last couple of years, we developed a hierarchy of several artificial neural networks that allow for a rapid identification and classification of complex tactical patterns in soccer (Memmert & Perl, 2009a,b). Based on the position data of 22 players and the ball, we can find the characteristic movement and interaction patterns of each team and characteristic interaction patterns between both teams (Grunz et al., 2012). Characteristic means that several slightly distinct realizations of movements on the soccer field are summarized in only one movement pattern. If a team attacks always in a similar fashion, the algorithm will reduce these attacks to a pattern. For example, if a team attacks always on the left side, we obtain movement patterns describing the movements on the left wing. That means, the frequency of attacks on the wings / via the center, or the number of attacks that were conducted by means of short / long passes (always including the respective probabilities of success). Such statistics could lead to more elaborate findings than the average information that are usually discussed (e.g., percentage of ball possession) but still collected manually. Our complex characteristic patterns or KPIs (e.g., pressing, space control, packing) can be calculated automatically in a very short time (less than three seconds). In an additional step this pattern can be visualized on a drawn soccer field and be presented to coaches (Perl, et al., 2013).