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Soccer analyses by means of artificial neural networks, automatic pass recognition and Voronoi-cells: An approach of measuring tactical success

Success in a soccer match is usually measured by goals. However, in order to yield goals, successful tactical pre-processing is necessary. If analyzing a match with the focus on "success", promising tactical activities including vertical passes with control win in the opponent's penalty area have to be the focus. Whether or not a pass is able to crack the opponent's defence depends on the tactical formations of both the opponent's defence and the own offence group. The methodical part of the contribution consists of three steps: (1) The first step describes how to analyze the formations of tactical groups by means of an artificial neural network, which is integrated in the DyCoN-too\. I.e. the positions of the players are condensed to those of tactical groups, and the formations of the tactical groups are mapped to a small number of characteristic patterns. In this way, the teams' activities can be reduced to interactions of tactical patterns, making it much easier to automatically detect regular and/or striking tactical features. Those tactical features build the context for measuring success, as described in the following steps: (2) Successful passes from a passing to a receiving player of a team are necessary preconditions for opening or continuing successful attacks. The software SOCCER is able to automatically calculate those passes based on the position data of the players and the ball. Along with the information from (1) it can be recognized, which types of formation interaction are helpful for generating successful or "dangerous" passes. (3) Up to this point, "successful" just means that the receiving player effectively achieves control of the ball. Based on the Voronoi-approach, the pass is seen as more successful and actually "dangerous", if it causes a higher rate of spatial control in the opponent's penalty area. The Voronoi-too\ calculates tactical space control and therefore helps to measure the tactical success of a pass. The combination of the described steps of automatic position-based analyses can be helpful for a deeper understanding of match dynamics and measuring tactical success. The tools DyCoN, SOCCER and Voronoi have been developed by J. Perl, University of Mainz, in cooperation with D. Memmert, DSHS Cologne.
© Copyright 2016 Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS). Julkaistu Tekijä Springer. Kaikki oikeudet pidätetään.

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Aihealueet: biologiset ja lääketieteelliset tieteet urheilukilpailut
Tagging: neuronale Netze
Julkaisussa: Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS)
Toimittajat: P. Chung, A. Soltoggio, C. W. Dawson, Q. Meng, M. Pain
Julkaistu: Cham Springer 2016
Sarja: Advances in Intelligent Systems and Computing, 392
Sivuja: 77-84
Julkaisutyypit: artikkeli
Kieli: englanti (kieli)
Taso: kehittynyt