In the previous module, we reviewed the strong correlation between training load and performance level.
As such, one of your most fundamental strategies for becoming a faster cyclist is increasing your training load.
Before you set out to do so, it is a good idea to get an idea of what your current load actually is. In this lesson we look at three simple tools for quantifying training load.
Tools to control your training load
Most cyclists will gain a ballpark estimate of their training load by adding up weekly kilometers ridden, monthly training hours etc. And these metrics are indeed of some use.
However, training volume does not account for the intensity of your training. And as we discussed in the previous lesson, intensity greatly influences training load.
Which is why you should strongly consider quantifying you training load by measure that accounts for both training hours and intensity.
At this point, I might add that you don’t need to go all Matt Damon “I’m-going-to-have-to-science-the-s**t-out-of-this“. I would suggest it is MORE important to understand the concept of training load, and how it relates to performance development, than to quantify it.
However, that being said you might find it very useful to keep a basic summary of your recorded training load.
Here are some simple tools that might help you in that regards.
Rate of Perceived Exertion (RPE) and Arbitrary Units
In the scientific literature, a common way of calculating training load is by the use of arbitrary units (AU).
This involves the rider assigning each workout a number from 1-10 based on how strenous the workout was as a whole. This value is called rate of perceived exertion (RPE).
You find your RPE value by asking the question:
“How hard was the workout as a whole?”
- Very, very easy
- Somewhat hard
- Very hard
Arbitrary Units (AU) are then calculated by multiplying the RPE value for the workout by the duration of the workout as a whole (in minutes).
E.g. a 90 minute interval session (warm-up and cool-down included) with an RPE of 7 would result in a session training load of 90 x 7 = 420 AU.
Similarly, a 120 minute continuous low-intensity ride with an RPE of 2 would result in a session training load of 120 x 2 = 240 AU.
This method might seem subjective and inaccurate. However, research has demonstrated that the Arbitrary Units represent a valid and reliable way of quantifying training load (1).
Recall the training data from Tour de France rider Tibault Pinot from lesson 2? How did the scientists monitor his training load?
You guessed it – by using Arbitrary Units (2).
However, if you are already in possession of a heart rate monitor or power meter, common apps for logging your training allows you to harvest data from your computers electronically.
Monitoring training load in Strava and Trainingpeaks
The training app Strava offers it’s premium users numerous features that can be used to monitor training load.
The obvious one is called “Training Load” and is based on the relationship between your session power output and your FTP.
A different value is called Suffer Score. This is calculated from the time spent on different heart rate values per workout, which can also be viewed as an expression of training load.
Training Peaks offer a not dissimilar concept called Training Stress Score (TSS). TSS will estimate your training load based on your workout duration, power recordings and your threshold power.
So, what do you do with these values?
Either AU, “Training Load”, Suffer Score or TSS can be used to track the progress of your training load.
For starters, I would recommend simply noting your load per session on a sheet of paper, on your phone, or in a diary. And then add them all to a spread sheet on a weekly or monthly basis.
This allows for an easy an low-tech way of monitoring your training. Which can then serve as a reference point for future manipulation of training load.
- Haddad M et al. Session-RPE Method for Training Load Monitoring: Validity, Ecological Usefulness, and Influencing Factors. Frontiers in Neuroscience, 2017;11:612
- Pinot J og Grappe F. A six-year monitoring case study of a top-10 cycling Grand Tour finisher. Journal of Sports Sciences, 2015; 33(9); 907-914