In team sports, athletes are required to work together. They have to be able to read each other’s body language, communicate effectively and make smart decisions in high-pressure situations. They must also learn to overcome adversity and deal with setbacks, all of which are essential life skills. In addition to building character, playing team sport also helps to improve an athlete’s mental health. Studies have shown that children who play team sports are more likely to graduate high school and have better GPAs than non-athletes.
In terms of the physical benefits, playing team sports teaches kids that they need to be active in order to stay healthy. In addition, team sports teach kids that hard work pays off and it is important to stick with a plan even when things don’t go well. The discipline and work ethic learned from team sports can be carried over into the classroom.
The practice of team sports teaches children to respect others, and it helps them to develop leadership and problem-solving abilities. It also teaches them to be resilient in the face of failure, and it shows that every person has his or her own strengths and weaknesses. In addition to these benefits, playing a team sport is fun and builds self-esteem.
Tracking systems data provides practitioners with the ability to provide detailed and objective descriptions of competition characteristics. However, the selection of appropriate metrics is key to understanding how tracking systems can support training outcomes. Practitioners should use a critical thinking process with a healthy dose of scepticism and awareness of appropriate theoretical frameworks when choosing metrics to monitor the performance of team sport athletes .
Examining the physical output of team sport athletes via aggregate parameters is challenging. For example, it is unlikely that periods of higher match intensity would be detected when examining the rolling average or peak average of high-speed running (HSR) data. Additionally, the volatility of team sport matches makes identifying meaningful changes on a per-second basis challenging. The use of time-series analysis removes the requirement to rely on fixed duration windows, allowing for the detection of precisely when a metric experiences a significant change .
It is also important that the metrics chosen are relevant to the sport in question. For example, the absolute value of HSR may be of less interest to American football linemen than wide receivers. Likewise, the relative value of accelerations and decelerations may be of greater interest to defenders than forwards.
Finally, incorporating the tactical context into monitoring is essential to understanding how tracking system data can support coaching decision making. This can be achieved by utilising features extracted from raw GPS or LPS data and combining them with an understanding of the game-specific rules. For example, the detection of defensive positioning and the timing of transitions between attack and defence could be used to inform a metric to identify when a player has reached his or her match intensity during a competitive game in Australian football .