足球休息防守的成功因素——专家访谈、跟踪数据和机器学习的混合方法。

IF 2.4 2区 医学 Q2 SPORT SCIENCES
Leander Forcher, Leon Forcher, Stefan Altmann, Darko Jekauc, Matthias Kempe
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引用次数: 0

摘要

虽然足球运动员的战术行为在特定的比赛阶段(进攻、防守、进攻转变、防守转变)是不同的,但在防守转变(从进攻到防守的转换行为)中,球员的成功行为却鲜为人知。因此,本研究旨在分析在防守转换中,休息防守的群体战术(即使在控球的情况下,部分球员在失球的情况下保持快速反击)。采用混合方法,包括定性和定量分析。对7位专业足球教练进行了半结构化的专家访谈,以定义休息防守。在定量分析中,基于2020/21赛季德甲联赛的153场比赛的跟踪和事件数据,计算了几个kpi,以预测机器学习方法中休息防守情况的成功。定性访谈表明,休息防守可以定义为持球时最深处防守者的位置,以防止对方在失球后的反击。例如,休息防守球员创造了1.69±1.00的数字优势,并在休息防守区域给进攻方11.51±9.82[%]的空间控制。最终的机器学习模型对休息防御成功的预测性能令人满意(Accuracy: 0.97, Precision: 0.73, f1-Score: 0.64, AUC: 0.60)。对单个kpi的分析揭示了球员在休息防守中的成功行为,包括控制深空和危险的反击。该研究得出结论,在丢球后尽快夺回控球权是防守转变中最重要的成功因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Success Factors of Rest Defense in Soccer - A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning.

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.

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来源期刊
CiteScore
5.60
自引率
6.20%
发文量
56
审稿时长
4-8 weeks
期刊介绍: The Journal of Sports Science and Medicine (JSSM) is a non-profit making scientific electronic journal, publishing research and review articles, together with case studies, in the fields of sports medicine and the exercise sciences. JSSM is published quarterly in March, June, September and December. JSSM also publishes editorials, a "letter to the editor" section, abstracts from international and national congresses, panel meetings, conferences and symposia, and can function as an open discussion forum on significant issues of current interest.
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