Rein Robert, Memmert Daniel
Institute of Cognition and Team/Racket Sport Research, German Sport University Cologne, Am Sportpark Müngersdorf 6, 50933 Cologne, Germany.
Springerplus. 2016 Aug 24;5(1):1410. doi: 10.1186/s40064-016-3108-2. eCollection 2016.
Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon.
直到最近，精英足球中的战术分析都是基于观测数据，所使用的变量丢弃了大部分背景信息。然而，团队战术分析需要来自各种来源的详细数据，包括技术技能、个人生理表现和球队阵型等，以呈现团队战术行为背后的复杂过程。因此，对于这些不同因素如何影响精英足球中的团队战术行为，我们知之甚少。在一定程度上，这也是由于缺乏可用数据。然而，越来越多的通过下一代跟踪技术获得的详细比赛记录，以及通过新型微型传感器技术收集的生理训练数据，可供研究使用。然而，这导致了相反的问题，即数据量本身成为了一个障碍，因为团队运动中战术决策的方法指南和理论模型都很缺乏。本文讨论了大数据和现代机器学习技术如何有助于解决这些问题，并协助开发团队运动战术决策的理论模型。正如医学应用的经验所示，必须首先克服数据治理和技术获取方面的重大组织障碍。本文针对精英足球中的战术分析讨论了这些问题，并提出了一个技术栈，旨在将大数据技术引入精英足球研究。随着数据量的不断增加成为一种普遍现象，所提出的方法也可以作为其他体育科学领域的指导方针。
