Micro-electromechanical systems (MEMS) have reduced drastically in size, cost, and power consumption, while improving accuracy. The combination of different sensor technologies is considered a promising step in the monitoring of athletes. Those “wearables” enable the capturing of relevant physiological and tactical information in individual and team sports and thus replacing subjective, time-consuming and qualitative methods with objective, quantitative ones. Prior studies mainly comprised sports categories such as: targeting sports, batting and fielding games as well as net and wall games, focusing on the detection of individual, non-locomotive movements. The increasing capabilities of wearables allow for more complex and integrative analysis expanding research into the last category: invasion sports. Such holistic approaches allow the derivation of metrics, estimation of physical conditions and the analysis of team strategic behavior, accompanied by integrative knowledge gains in technical, tactical, physical, and mental aspects of a sport. However, prior and current researchers find the precise measurement of the actual movement within highly dynamic and non-linear movement difficult. Thus, the present article showcases an overview of the environments in which the wearables are employed. It elaborates their use in individual as well as team-related performance analyses with a special focus on reliability and validity, challenges, and future directions.
The presented field experiment in an 11 vs. 11 soccer game set-up is the first to examine the impact of different formations (e.g. 4-2-3-1 vs. 3-5-2) on tactical key performance indicators (KPIs) using positional data in a controlled experiment. The data were gathered using player tracking systems (1 Hz) in a standardized 11 vs. 11 soccer game. The KPIs were measured using dynamical positioning variables like Effective Playing Space, Player Length per Width ratio, Team Separateness, Space Control Gain, and Pressure Passing Efficiency. Within the experimental positional data analysis paradigm, neither of the team formations showed differences in Effective Playing Space, Team Separateness, or Space Control Gain. However, as a theory-based approach predicted, a 3-5-2 formation for the Player Length per Width ratio and Pressure Passing Efficiency exceeded the 4-2-3-1 formation. Practice task designs which manipulate team formations therefore significantly influence the emergent behavioral dynamics and need to be considered when planning and monitoring performance. Accordingly, an experimental positional data analysis paradigm is a useful approach to enable the development and validation of theory-oriented models in the area of performance analysis in sports games.
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