Selecting Metrics for Team Sport

Team sport

Team sport is a popular activity enjoyed by millions of people, whether participating in organised events or just playing at home. It requires a combination of physical fitness, dedication to learning plays, and trust in teammates. For many, it also represents an opportunity to develop social and emotional skills that will benefit them throughout their lifetime. However, for a few, serious injuries can take their toll on the body and mind and even cause them to abandon the game altogether. This article aims to highlight the various considerations when selecting metrics for specific contexts in team sports, thereby ensuring that tracking systems produce data that is meaningful and applicable.

Context will drive what technology (and in turn, metrics) is used to monitor the locomotor characteristics of an athlete. In team sports, this may include a range of factors, such as the playing dimensions and player density of the sport, the position characteristics, and the rules of play. It will also consider the availability of appropriate training surfaces and facilities. The limitations of technology in terms of its cost, size, and accuracy will also be considered.

These factors are important because they provide a framework for selecting metrics that best capture the unique demands of a particular sport, and in turn will allow for the comparison of performance between different team sports. For example, the absolute value of high-speed running (HSR) may be less meaningful to basketball players than to American football linemen due to the difference in court size and their specific positional characteristics. Conversely, the relative value of HSR between athletes within a given sport may be more pertinent when considering training load allocation.

Descriptive data can be useful for guiding the selection of appropriate metrics for individual sport contexts, and should be incorporated into a theoretical framework that guides the planning of training interventions. These theoretical frameworks depict how external load is prescribed to elicit the desired training outcome [33, 34].

Once an objective description of a sport’s physical characteristics has been established, ongoing monitoring can be used to identify changes in these traits over time. For example, the velocity profile of English Premier League footballers has evolved over time, with sprint distances increasing by 35% from one season to the next.

Using raw trace data that has been segmented using time-series analysis, the occurrence of peak match intensity can be identified for individual players and across positions. This will help to distinguish between training and competition intensity, as well as identify the key factors that influence peak performance in a particular sport.

Featuring two teams and nine starting players, baseball is an extremely slow-paced game that requires exceptional coordination between its athletes. The constant interaction required in the pitching and batting phases, as well as the fact that the game is played over nine innings, means that this sport demands an immense level of endurance from its athletes. It is also a highly tactical sport, requiring excellent communication and a strong emphasis on unity between its athletes.