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.

No abstract

No abstract

Tool use can be considered a particularly useful model to understand the nature of functional actions. In 3 experiments, tool-use actions typified by stone knapping were investigated. Participants had to detach stone flakes from a flint core through a conchoidal fracture. Successful flake detachment requires controlling various functional parameters simultaneously. Accordingly, our goals were twofold: (a) to examine the regulation of kinetic energy by varying the properties of the hammers and the goal, and (b) to characterize the difference in action regulation across skill levels. All groups were able to modify their actions according to changes in task goals, but only experts displayed fine-tuning to functional parameters (i.e., regulate actions according to changes in hammer weight in a manner that left kinetic energy unchanged). Expertise is considered to depend on the identification of the interactions between functional parameters.

No abstract