<abstract xmlns="http://www.w3.org/1999/xhtml">

Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.
Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.
Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.
Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.
Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.
</abstract>

Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.
Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.
Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.
Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.
Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.

Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccer

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting...

Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccer

Publié en ligne: 08 mai 2021

Pages: 43 - 54

DOI: https://doi.org/10.2478/ijcss-2021-0003

Mots clés
STRATEGY, KPI, OFFENCE DYNAMICS, BALL CONTACT, SPACE CONTROL, CORRELATION, EFFICIENCY

© 2021 J. Perl et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccer

Publié en ligne: 08 mai 2021

Pages: 43 - 54

DOI: https://doi.org/10.2478/ijcss-2021-0003

Mots clésSTRATEGY, KPI, OFFENCE DYNAMICS, BALL CONTACT, SPACE CONTROL, CORRELATION, EFFICIENCY

© 2021 J. Perl et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
STRATEGY, KPI, OFFENCE DYNAMICS, BALL CONTACT, SPACE CONTROL, CORRELATION, EFFICIENCY