Team sport is a form of organised sport that involves a group of athletes, typically of varying abilities and from diverse backgrounds, who compete against each other to achieve a common goal. It is a popular pastime that helps a multitude of people improve their health, mental health, social and work life, with the added benefit of promoting community spirit. In addition, it teaches invaluable lessons that go beyond the court, rink, field or arena to help children understand the value of commitment, training, setting and achieving goals, as well as the importance of hard work. It also teaches them to bounce back from losses, learning from them as unique opportunities for growth.
The majority of team sports involve a large number of players and require high levels of physical contact. These characteristics have contributed to the high prevalence of injuries in team sports and have led to increasing emphasis on physical preparation and conditioning in order to mitigate the risks associated with these activities. Practitioners utilise tracking systems to quantify and analyse these training and competition characteristics in order to support objective decision-making in the prescription of external load for optimal performance, injury risk reduction and player wellbeing.
Tracking systems enable practitioners to examine the external load imposed on players through drill, game and macro-cycle level training plans. This is a critical first step in the application of these technologies to enhance and optimise training outcomes, particularly with respect to injury reduction [1].
However, assessing training and match-day outputs through aggregate parameters such as distance covered per drill or on-field rotation can obscure more subtle changes in external load within a session or game. This is especially evident in team sports, where a large variation in the distribution of external loads between different positions exists [2].
Descriptive data provide practitioners with information about the characteristics and requirements of their athletes. Practitioners then use this knowledge to plan the external load that will elicit their desired training adaptations and responses.
This is a complex process that requires the consideration of many variables, and entails both an objective and subjective understanding of an athlete’s training history, current abilities, injury status and performance requirements.
Sport scientists can go beyond reporting aggregate parameters by examining raw GPS or LPS trace data and utilising time series analysis. This technique allows the examination of individual athlete performance in a given context, for example by identifying when and where Australian football athletes obtain peak match intensity as a function of time during a match. Combined with an underlying theoretical framework, such as ecological dynamics, this approach could yield rich insights into the dynamic and non-linear nature of both training and match-day performance in team sports.
