Research article - (2023)22, 707 - 725
DOI:
https://doi.org/10.52082/jssm.2023.707
The Success Factors of Rest Defense in Soccer – A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning
Leander Forcher1,2,, Leon Forcher1,2, Stefan Altmann1,3, Darko Jekauc1, Matthias Kempe4
1Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
2TSG 1899 Hoffenheim, Zuzenhausen, Germany
3TSG ResearchLab gGmbH, Zuzenhausen, Germany
4Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands

Leander Forcher
✉ Institute of Sports and Sports Science, Engler-Bunte-Ring 15, 76133 Karlsruhe, Karlsruhe Institute of Technology, Karlsruhe, Germany
Email: leander.forcher@kit.edu
Received: 15-06-2023 -- Accepted: 27-10-2023
Published (online): 01-12-2023

ABSTRACT

While the tactical behavior of soccer players differs between specific phases of play (offense, defense, offensive transition, defensive transition), little is known about successful behavior of players during defensive transition (switching behavior from offense to defense). Therefore, this study aims to analyze the group tactic of rest defense (despite in ball possession, certain players safeguard quick counterattacks in case of ball loss) in defensive transition. A mixed-methods approach was used, involving both qualitative and quantitative analysis. Semi-structured expert interviews with seven professional soccer coaches were conducted to define rest defense. In the quantitative analysis, several KPIs were calculated, based on tracking and event data of 153 games of the 2020/21 German Bundesliga season, to predict the success of rest defense situations in a machine learning approach. The qualitative interviews indicated that rest defense can be defined as the positioning of the deepest defenders during ball possession to prevent an opposing counterattack after a ball loss. For instance, the rest defending players created a numerical superiority of 1.69 ± 1.00 and allowed a space control of the attacking team of 11.51 ± 9.82 [%] in the area of rest defense. The final machine learning model showed satisfactory prediction performance of the success of rest defense (Accuracy: 0.97, Precision: 0.73, f1-Score: 0.64, AUC: 0.60). Analysis of the individual KPIs revealed insights into successful behavior of players in rest defense, including controlling deep spaces and dangerous counterattackers. The study concludes regaining possession as fast as possible after a ball loss is the most important success factor in defensive transition.

Key words: Team sports, performance analysis, tactics, defensive play, football

Key Points
  • Combination of qualitative expert interviews and up-to-date quantitative data analysis using tracking data and machine learning revealed insightful results of successful tactical behavior in defensive transition in soccer
  • According to experts, rest defense can be defined as behavior of the deepest defending players during ball possession with the goal to prevent an opposing counterattack after a ball loss during defensive transition
  • To be successful in defensive transition, players in rest defense should control deep spaces and dangerous counterattackers to successfully prevent dangerous opposing counterattacks
  • Most important success criterion in defensive transition is to regain possession after a ball loss as quickly as possible to stop an opponent’s counterattack in the early stages








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