Abstract
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.
Highlights
Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information
Results using Voronoi-diagrams show similar results compared to the team surface area approach (Fonseca et al 2012; Fujimura and Sugihara 2005; Gudmundsson and Wolle 2014; Kim 2004; Taki and Hasegawa 2000) another approach is based on the determination of numerical superiority in a particular pitch area (Silva et al 2014). Together these results indicate that space control is a central aspect of soccer tactics and further highlight the interactive nature underlying soccer games (Duarte et al 2013; Garganta 2009; Grehaigne et al 1997; Tenga et al 2010a, b)
In conclusion, exciting times are emerging for team sports performance analysis as more and more data is going to become available allowing more refined investigations
Summary
Opposition (Beetz et al 2005; Carling et al 2014; Lucey et al 2013a, b; Wang et al 2015). Current suggestions regarding appropriate phase space variables in team game vary widely (Duarte et al 2012a, b; Gréhaigne 2011; Grehaigne et al 1997; Gréhaigne and Godbout 2014; Lames and McGarry 2007) In this regard, a common approach for example is to use the relative phase as a measure to capture coordination phenomena between players (Duarte et al 2013; Goncalves et al 2014; Sampaio and Macas 2012). Recent machine learning models using neural networks have been extended such to allow to incorporate a priori information into the models (Bishop 2013) This might be of great relevance to develop novel approach to model team tactical behaviors as for example insights gained from the studies summarized above might be used to constrain network modeling efforts and at the same time allowing the connection between physiological, tactical and skill related information.
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