Team sport is a multi-disciplinary, complex and dynamic activity that promotes physical health, mental health, social well-being and emotional growth. It also provides lifelong skills such as confidence, competence and resilience.
Many Americans participate in team sports, including basketball, softball/baseball and soccer, at rates similar to those of individual sports such as golf, tennis and bowling. They are an important component of the physical education curriculum, and it is vital that high school physical educators include these sports in their programs to provide students with a variety of activities to pursue in their leisure time.
A large percentage of American high school students (80%) participate in team sports outside of their regular physical education class. This activity is not only popular with high school students, but it has been shown to be an important factor in their physical and psychosocial development.
The popularity of team sports is reflected in the number of adult males who play these sports as well. In fact, four of the top five most popular sports among adult males are team sports: basketball, softball/baseball and soccer.
There is a plethora of tracking systems that are used in team sports to capture athlete external load data and produce derived metrics for use by practitioners. These metrics can be applied to describe, plan, monitor and evaluate training and competition characteristics, with an emphasis on addressing athlete fatigue. However, selecting the right metrics for a specific sport is critical and requires careful consideration by practitioners.
Metric selection is influenced by a range of factors, such as playing dimensions, player density, game rules and timing structure. In particular, rapid movements such as turnovers, cuts, close outs and defensive shuffles are crucial to managing injury risk, planning and monitoring training and quantifying competition characteristics.
In ice hockey, for example, high-intensity bouts of skating with rapid changes in speed and direction are required . This, coupled with high technical demands, such as puck control, evading defenders and body checking, creates a challenging task for players to manage. The presence of these dynamic, non-linear demands has meant that tracking data has been recursively used to re-describe and plan external load in line with the evolving nature of the sport.
This type of analysis is being explored in collision sports where the ability to recursively measure head kinematics is being developed to help with concussion management. Using an IMU, pressure sensors and video cameras, the kinematics of multiple scrum engagement techniques in rugby have been assessed with the use of accelerometers and gyroscopes to assist with determining the degree of impact loading on the head during contact.
Alternatively, a Gaussian curve fitting approach has been used with instantaneous velocity data from women’s soccer and other team sports to determine sport-specific speed thresholds for each player, with the intersections between curves revealing a sport-specific velocity profile. Although this technique has been a useful way of identifying the minimum velocities that can be expected for different positions in a team sport, it is not suitable for all team sports due to the limitations of the data collection equipment and the non-linear nature of the sport.