Tracking Data in Team Sport

Team sport

Team sport provides children with a variety of benefits, including improved physical health and well-being. It also allows them to develop social skills and learn how to work in a group. They are able to form lasting friendships and build trust with their teammates. This experience can help them in their academic and personal lives as they mature.

Team sports are a popular activity amongst high school students. The most common sports in this age group are basketball, soccer and football.

In addition to the obvious physical benefits, sports provide a sense of belonging and social integration, as well as teaching them how to respect teammates and coaches. They can develop communication skills, build confidence and leadership qualities and promote a healthy lifestyle.

The number of participants in team sports is growing rapidly, with a recent study revealing that 45% of American high school students participate in one or more sports outside of their physical education classes. This is a significant rise from just ten years ago, where only 10% of students participated in team sports.

This increase in participation is likely to be due to increased awareness of the benefits of team sports, which have been cited by a number of studies. These include: a reduction in depression and stress; fewer behavioral problems; and better psychosocial well-being.

Tracking data analysis is increasingly used in team sports to monitor and predict performance. Specifically, it can be used to evaluate training load, and to assess the impact of competitions on the athlete’s ability to perform in a given situation (e.g., match intensity, speed or skill).

Identifying and assessing the importance of specific physical movements to physical preparation and team sport performance requires a thorough understanding of the nature of these activities. This includes the court size, number of players and type of movement required (e.g., turnovers, cuts, close outs and defensive shuffles).

As such, tracking systems can be an effective tool for identifying and quantifying these movements in real-time. However, there are several challenges in interpreting this data. In particular, the visualisation of raw trace data is complex and requires the encoding of thousands of data points. This process should be clearly communicated to ensure practitioners have a clear understanding of the data that are selected and analysed.

For instance, a time-series analysis approach can be applied to the velocity traces from an athlete’s GPS or LPS devices, to detect how physical output changes during a match, as a function of time. This can be utilised to profile athletes’ physical output, as well as their skilled output, during matches [73].

Metrics from tracking systems are increasingly being used in team sports to monitor and quantify training load, as organisations seek to obtain a competitive advantage and manage the risk of injury. This is expected to support objective decision-making for the prescription and manipulation of training load.

There are a wide range of metrics available from different tracking systems, which vary widely in their definitions, calculations and ecological validity. Practitioners need to select the most relevant and specific metrics for each sport. This needs a critical thinking and analytical process, with a healthy dose of scepticism in the selection process to ensure that metrics are appropriate for the context of a sport and the athletes who represent it.