Team sports performance functions as a complex system, and players from different positions exhibit a tendency to form team synergies into coordinated patterns contingent upon task orientation, whether in the attacking or defensive phases. This inherent organizational process manifests self-organizing, nonlinear, and dynamic characteristics.
In a traditional way, match performance analysis has mainly focused on event data with frequency tables and sequential systems. Such isolated, single-dimensional, and descriptive analysis of high-level football performance provides limited information about what actually constitutes individual behaviors and teams’ collective performance, and will inevitably hinder practitioners in applying the scientific findings.
The evolution of sports performance analysis has closely paralleled advancements in modern technologies. The integration of electronic performance & tracking systems (EPTS) in team sports is widely utilized to track multiple players and balls in real-time. Employing technologies like the global navigation satellite system (GNSS), optical tracking systems, and local position system, it yields a profusion of spatial-temporal data and presents substantial opportunities for enhancing the predictive and interpretative capabilities of sports performance analysis in team sports. Therefore, modeling and analyzing spatial-temporal information helps quantify dynamic coordination among players, explain collective behaviors and reveal playing patterns. Moreover, the utilization of this has offered researchers increased insights into team performances, facilitating practical applications essential for coaches and athletes.
This Research Topic extends an inclusive invitation for researchers in various team sports such as football, basketball, volleyball, and others. The objective of this Research Topic is to compile applied research from experts in the field of team sports performance analysis. Researchers are invited to submit their work in various formats, including original research, narrative reviews, systematic reviews, and commentaries. Submissions should evaluate and address how Spatial-temporal metrics contribute to deepening the understanding of self-organizing, nonlinear, and dynamic organizational behaviors in team sports. We welcome, but are not limited to, the following topics:
• Spatial-temporal metrics applied to performance analysis in team sports;
• complex system in team sports performance analysis;
• social network analysis in team sports;
• collaborative behavior in team sports;
• spatial dynamics and patterns of team sports performance;
• team formation and tactical behaviors;
• pitch network theory and application;
• pitch control theory and application.
Team sports performance functions as a complex system, and players from different positions exhibit a tendency to form team synergies into coordinated patterns contingent upon task orientation, whether in the attacking or defensive phases. This inherent organizational process manifests self-organizing, nonlinear, and dynamic characteristics.
In a traditional way, match performance analysis has mainly focused on event data with frequency tables and sequential systems. Such isolated, single-dimensional, and descriptive analysis of high-level football performance provides limited information about what actually constitutes individual behaviors and teams’ collective performance, and will inevitably hinder practitioners in applying the scientific findings.
The evolution of sports performance analysis has closely paralleled advancements in modern technologies. The integration of electronic performance & tracking systems (EPTS) in team sports is widely utilized to track multiple players and balls in real-time. Employing technologies like the global navigation satellite system (GNSS), optical tracking systems, and local position system, it yields a profusion of spatial-temporal data and presents substantial opportunities for enhancing the predictive and interpretative capabilities of sports performance analysis in team sports. Therefore, modeling and analyzing spatial-temporal information helps quantify dynamic coordination among players, explain collective behaviors and reveal playing patterns. Moreover, the utilization of this has offered researchers increased insights into team performances, facilitating practical applications essential for coaches and athletes.
This Research Topic extends an inclusive invitation for researchers in various team sports such as football, basketball, volleyball, and others. The objective of this Research Topic is to compile applied research from experts in the field of team sports performance analysis. Researchers are invited to submit their work in various formats, including original research, narrative reviews, systematic reviews, and commentaries. Submissions should evaluate and address how Spatial-temporal metrics contribute to deepening the understanding of self-organizing, nonlinear, and dynamic organizational behaviors in team sports. We welcome, but are not limited to, the following topics:
• Spatial-temporal metrics applied to performance analysis in team sports;
• complex system in team sports performance analysis;
• social network analysis in team sports;
• collaborative behavior in team sports;
• spatial dynamics and patterns of team sports performance;
• team formation and tactical behaviors;
• pitch network theory and application;
• pitch control theory and application.