As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. 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.
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. 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. In parts, this has also been due to the lack of available data. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. 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. Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information.
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