<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The players’ positions of tactical groups in soccer can be mapped to formation-patterns by means of artificial neural networks (<xref ref-type="bibr" rid="j_ijcss-2018-0012_ref_003_w2aab3b7b7b1b6b1ab1ab3Aa">Kohonen, 1995</xref>). This way, the hundreds of positional situations of one half of a match can be reduced to about 20 to 30 types of formations (<xref ref-type="bibr" rid="j_ijcss-2018-0012_ref_002_w2aab3b7b7b1b6b1ab1ab2Aa">Grunz, Perl &amp; Memmert, 2012</xref>; <xref ref-type="bibr" rid="j_ijcss-2018-0012_ref_006_w2aab3b7b7b1b6b1ab1ab6Aa">Perl, 2015</xref>), the coincidences of which can be used for describing and simulating tactical processes of the teams (<xref ref-type="bibr" rid="j_ijcss-2018-0012_ref_006_w2aab3b7b7b1b6b1ab1ab6Aa">Memmert, Lemmink &amp; Sampaio, 2017</xref>): Developing and changing formations in the interaction with the opponent activities can be understood as a tactical game in the success context of ball control, space control and finally generating dangerous situations. As such it can be simulated using mathematical approaches like Monte Carlo-simulation and game theory in order to generate optimal strategic patterns. However, in accordance with results from game theory it turns out that in most cases the one optimal strategy does not exist (e.g. see <xref ref-type="bibr" rid="j_ijcss-2018-0012_ref_001_w2aab3b7b7b1b6b1ab1ab1Aa">Durlauf &amp; Blume, 2010</xref>). Instead, a variety of partial strategies with different frequencies were necessary – an approach that is mathematically interesting but has nothing to do with soccer reality. An alternative approach, which is developed in the following, is to interrupt the strictness of a single strategic concept by creative elements, which improves flexible response to opponent activities as well as prevents from being analyzed by the opponent team.</p><p>The results of respective simulation reach from improving strategic behaviour to recognizing strategic patterns and in particular to analyzing role and meaning of creative elements.</p></abstract>

The players’ positions of tactical groups in soccer can be mapped to formation-patterns by means of artificial neural networks (Kohonen, 1995). This way, the hundreds of positional situations of one half of a match can be reduced to about 20 to 30 types of formations (Grunz, Perl & Memmert, 2012; Perl, 2015), the coincidences of which can be used for describing and simulating tactical processes of the teams (Memmert, Lemmink & Sampaio, 2017): Developing and changing formations in the interaction with the opponent activities can be understood as a tactical game in the success context of ball control, space control and finally generating dangerous situations. As such it can be simulated using mathematical approaches like Monte Carlo-simulation and game theory in order to generate optimal strategic patterns. However, in accordance with results from game theory it turns out that in most cases the one optimal strategy does not exist (e.g. see Durlauf & Blume, 2010). Instead, a variety of partial strategies with different frequencies were necessary – an approach that is mathematically interesting but has nothing to do with soccer reality. An alternative approach, which is developed in the following, is to interrupt the strictness of a single strategic concept by creative elements, which improves flexible response to opponent activities as well as prevents from being analyzed by the opponent team.The results of respective simulation reach from improving strategic behaviour to recognizing strategic patterns and in particular to analyzing role and meaning of creative elements.

Formation-based modelling and simulation of success in soccer

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Formation-based modelling and simulation of success in soccer"}

The players’ positions of tactical groups in soccer can be mapped to formation-patterns by means of artificial neural networks (Kohonen, 1995). This way, the...

Formation-based modelling and simulation of success in soccer

Pubblicato online: 31 dic 2018

Pagine: 204 - 215

DOI: https://doi.org/10.2478/ijcss-2018-0012

Parole chiave
FORMATION, SUCCESS, STRATEGY, CREATIVITY, ARTIFICIAL NEURAL NETWORK

© 2018 Perl J., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Formation-based modelling and simulation of success in soccer

Pubblicato online: 31 dic 2018

Pagine: 204 - 215

DOI: https://doi.org/10.2478/ijcss-2018-0012

Parole chiaveFORMATION, SUCCESS, STRATEGY, CREATIVITY, ARTIFICIAL NEURAL NETWORK

© 2018 Perl J., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
FORMATION, SUCCESS, STRATEGY, CREATIVITY, ARTIFICIAL NEURAL NETWORK