Tracking technology, such as global positioning systems (GPS), local positioning systems (LPS), and vision-based systems, can quantify a team-sport athlete’s external burden. During a match or training session, these technologies enable for the calculation of displacement, velocity, and acceleration. It is crucial to accurately quantify these factors in order to detect meaningful changes in team-sport athlete external load.

A Brief introduction

Scientists and practitioners are interested in quantifying athlete external load for training and competition planning and monitoring. Accelerometers, global positioning systems (GPS), local positioning systems (LPS), and optical tracking systems can all be used to measure the external load of team-sport athletes. These systems, with the exception of accelerometers, calculate displacement, velocity, and acceleration over time. The word “activity profile” refers to the analysis of external demand over the course of a match or training session (Aughey, 2011a). The activity profile data is utilised to track changes over the course of a competitive season or tournament (Bradley et al., 2009; Jennings, D. et al., 2012) and to create customised training routines (Boyd et al., 2013).

Athletes in field-based team sports have a well-documented activity profile (Aughey, 2011a; Mooney et al., 2011; Jennings, D. H. et al., 2012; Bradley et al., 2013). Time spent in velocity or acceleration zones is usually included in activity profile analysis. These zones are established by threshold values that are arbitrarily determined by tracking system proprietary software or indicated in relation to a physiological test. There is currently no agreement on how to calculate a velocity or acceleration threshold. There are significant differences in how a sprint effort is classified. As a result, comparing activity profiles between and within team sports is challenging.

The goal of this narrative study is to look at the different velocity and acceleration criteria that are used to measure external load in team sports athletes. Individual differences are not taken into consideration when using a global velocity or acceleration threshold. While specific thresholds can be set, physiological tests that include continuous or linear movement do not account for changes in direction or acceleration. As a result, the present methodologies for analysing external load are ineffective. Alternative methods are considered, including unsupervised data mining techniques. These methods identify patterns in external data and may be useful in determining thresholds.

Technologies for Tracking Athletes

Tracking systems measure the external burden on team-sport athletes. Manual video analysis is a low-cost way of calculating external load. Athletes are filmed by cameras placed around the playing field, and the footage is subjectively categorised into locomotor categories (Spencer et al., 2004). Manual video analysis necessitates a significant amount of time to observe activity. Due to the subjective estimate of athlete movement, validity has not been established. A tracking system must be accurate in order to detect substantial changes in an athlete’s activity profile. A key constraint of manual video analysis is a human’s ability to reliably reproduce results. Semi-automated tracking systems were created to take the guesswork out of athlete activity classification. ProZone (Di Salvo et al., 2006) and Amisco (Castellano et al., 2014) are commercial systems that can detect the position of many team-sport athletes. The essential equipment, on the other hand, is both expensive and non-portable. As a result, without the complex infrastructure, activity profiles cannot be collected. Athlete movement is also recorded on a two-dimensional plane, with vertical movement unnoticed (Barris and Button, 2008).

Accelerometers are small, wearable sensors that measure athlete load in three dimensions. Accelerometers have been used in team sports on the field (Mooney et al., 2013) and on the court (Cormack et al., 2014), but they cannot quantify an athlete’s position relative to a playing area. As a result, the time and distance covered by an athlete at various speeds cannot be measured. The use of GPS to measure distance and velocity in field-based team sports is well-documented (Buchheit et al., 2010b; Jennings, D. H. et al., 2012; Varley et al., 2013b). A recent study looked into the aspects that influence the setup, analysis, and reporting of GPS data for team sports (Malone et al., 2016).