Catapult Fundamentals: Using internal and external load to answer performance questions

Introducing you to the science behind our technology, the Catapult Fundamentals series explains sports science concepts and principles, and looks at the ways athlete monitoring systems can be used to improve player performance.

A well-designed training programme will expose athletes to a range of stresses, all of which will induce fatigue and adaptations to that stress to differing degrees. Without an objective measure of the stress being imposed on the athlete, or their response to that stress, coaches and sports scientists are unable to quantify the true effectiveness of their interventions.

The consequences of failing to correctly measure those loads can be under- or overloading of athletes, either of which can lead to injury or illness, contributing to sub-optimal performance levels. So how can practitioners measure the load being placed on their athletes? More importantly, how can they use information to derive meaningful insights to help address performance questions and support the work of coaching staff?


At a basic level, external load can be characterised as the sum of the work completed by an athlete during a particular training drill, session or period. In terms of Catapult’s technologies, measures which we think of as locomotive (e.g. distance covered, average velocity, number of sprints etc.) and mechanical (e.g. PlayerLoad) are all measures of external load.

External load may be more easily observable for practitioners, but it is internal load (the cardiovascular and metabolic stresses placed on an athlete during a bout of work) which determines the overall outcome and an athlete’s subsequent adaptation to that stress. Ultimately, the majority of teams will look at the relationship between internal and external load metrics to measure athlete efficiency, something that can shed light on an individual’s state of readiness or fatigue. Similarly, the relationship between the mechanical stresses experienced by the body and distance covered can shed light on athlete fatigue levels.


When interpreting data related to athlete load, sports scientists will typically ask two questions:

  1. How much work have my athletes performed?
  2. How hard have my athletes worked?

These might sound like similar questions, but there are key differences. In essence, the practitioner wants to know what the volume and intensity of a given session was. Addressing these questions is central to the purpose of all athlete monitoring systems, irrespective of the budget or complexity of the organisation.

The table below outlines three levels of athlete monitoring systems to measure volume and intensity:

Athlete Monitoring Levels

The monitoring of intensity of sessions at Level 1 is conducted using a metric called Session RPE. RPE stands for Rating of Perceived Exertion, and requires a subjective assessment from the athlete of how hard each drill or session was based on a scale 1-10.

At Level 2, a method of quantifying internal training volume is introduced. Heart Rate Exertion (sometimes known as Training Impulse) breaks down athlete heart rate into a series of bands proportionally related to an individual’s maximum heart rate. The factor value increases with increasing cardiovascular demand, with a multiplying factor then being applied for the time spent in each heart rate zone.

As would be expected, the level of athlete monitoring complexity grows with the sophistication of the available technology. The job of the practitioner also becomes more complex, as they need to ensure that information is being relayed to the coaching staff in an easily digestible manner.

In an environment where there is sophisticated monitoring technology, a good starting point is to relate metrics back to the work an athlete usually does in a match, then report training data relative to match equivalents. For example, a training session reported as 60:80 for volume:intensity would mean that the athlete has performed 60% of the work they would do in a match, with the average training intensity being 80% of a match. In terms of distilling internal and external load data down into actionable insights, this is as good a starting point as any.

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