Key variables from EPTS devices
Sports medicine professionals need to consider key values when analysing data from EPTS devices
The advent of wearable and tracking technologies, commonly grouped under EPTS (Electronic Performance & Tracking Systems), has transformed how professional football clubs monitor athlete workload, performance, and injury risk. Devices such as WIMU Pro, Catapult, Fitogether, among others, provide a wealth of data. But for that data to be meaningful, sports-medicine professionals must know which variables to prioritise, and how to interpret them.
EPTS data generally fall into two broad categories. First, external load variables (eTL), which quantify the mechanical and locomotor stress placed on the athlete. These include metrics such as total distance covered, distance at high velocity or sprinting, number of accelerations/decelerations, player load, and neuromuscular load.
Second, internal load variables (iTL), reflecting the biological stress experienced by the athlete, for instance, physiological parameters (heart rate, oxygen consumption, metabolic markers) or perceived exertion, when the device or complementary systems allow.
Among external load metrics, certain parameters have shown particular relevance in elite football. Studies tracking high-speed running, sprint distance, accelerations/decelerations and overall “player load” correlate these metrics with risk of overload and injury.
For example, excessive high-intensity load without adequate recovery may predispose to muscle strains or overuse injuries.
However, not all measurement systems are equally reliable across all variables. A review of local positioning systems (LPS, including UWB-based ones) demonstrated acceptable validity for measuring distances (< 3.5 % difference compared to reference systems), but greater variability and lower reliability when assessing instantaneous speeds, accelerations, or decelerations, particularly in sport-specific movements with changes of direction.
Given these limitations, sports medicine professionals must carefully interpret EPTS data and avoid overreliance on any single metric. In practice, analysing a combination of variables offers more robust insight. For instance, coupling external load metrics (e.g. high-speed distance, number of sprints) with internal load indicators or subjective markers of fatigue better reflects the athlete’s true stress and recovery status.
Moreover, temporal context matters. Monitoring raw data across sessions is less informative than establishing individual baselines (e.g. typical weekly load distribution, pre-season load, match load) and detecting deviations. Also, trend analysis over time (accumulated load, spikes in intensity, insufficient recovery periods) is more valuable than isolated snapshots.
Finally, data quality and consistency are crucial. It is advisable to maintain the same tracking technology (e.g. same device type, positioning system) across sessions to avoid biases introduced by differing measurement systems. Some studies have shown that different devices or technologies may register divergent values even for identical sessions.
In a nutshell, for EPTS-based data to inform medical or performance decisions, practitioners should: select validated systems, prioritise a set of key external and internal load metrics, contextualise data longitudinally rather than cross-sectionally, and combine objective measures with subjective or physiological markers.
Through adopting such rigorous data-driven monitoring, VALITICA can support clubs in making informed, evidence-based decisions about training load, recovery protocols, and injury prediction strategies, ultimately enhancing athlete welfare and performance longevity.
