**Check out our RPE for Cycling Guide at this link**

Power data has transformed what it means to both describe and prescribe effort.  Training lexicon like Functional Threshold Power (FTP) enables intensity prescription down to the watt while providing a language to describe cycling performance in great detail [1].

Singular Focus

Understanding the world of cycling in watts and kJ’s has huge advantages, but relying on ride data alone to plan training and race strategy is a mistake.  When watts are our singular focus we fail to respect the role our perception of effort plays in shaping our performance on the bike.

Stress, ambient temperature, caffeine, and many others; they all contribute to our rate of perceived exertion (RPE).  If RPE had no bearing on performance we might dismiss it, but in many cases, it defines our best performances [2, 3].

Becoming Fluent

Because RPE is so important, we want to integrate it into everything from workout design to race strategy.  Most of us are proficient in the language of power, the next step is to become more fluent in RPE.

The language of RPE was first developed by Swedish psychologist, Gunnar Borg, who mapped out the original scale modeled off heart rate (HR) [4].  With a resting HR around 60 bpm and maximal HR around 200 bpm he came up with a scale of perceived effort that ranged from 6-20.

The RPE system looks nice but how well does it work in approximating specific training intensities?  In short, really well.  A recent study found a very strong relationship between “RPE and exercise intensity, assessed by blood lactate, and heart rate…” [5].

In other words, when subjects are doing laboratory based assessments of different training intensities such as their lactate threshold, the intensity that a subject perceives, most often lines up with the actual intensity being measured.  Power meters are fancy and accurate, but RPE works pretty well too.

Training can be confusing. In our free eBook, we’ll show you four ways to use your data and insights from science to ride better than ever.


While the 20-point scale does a great job of providing a system to describe a wide range of exercise intensities, it also has a tendency to be a bit overly complex.  For this reason, we’ll break down cycling effort on a simple scale of 1-10.

The graphic below illustrates how our RPE scale might translate into the common languages of training zones and % FTP.

Practical Suggestions

There are many ways to more fully utilize RPE in your cycling, but here are a few suggestions:

  • In place of power targets on a really hot day
  • In lieu of power targets when carrying high levels of life stress
  • To get a break from data while more fully enjoying your ride
  • In a competitive environment, when holding a wheel or making it first to the line matter more than a number on your Garmin
  • For shorter/more explosive intervals where power is less relevant
  • When you don’t have access to a power meter


  • Understanding the language of power is invaluable, but don’t lose sight of RPE
  • RPE has the power to shape cycling performance
  • Get comfortable with a simple 10 point RPE scale
  • Try to plan you hardest workouts during times when your RPE might be low
  • Shift your workouts from power to RPE when life stress is high
  • Don’t let power targets hold you back, let competition drive your performance to new heights

Training can be confusing. In our free eBook, we’ll show you four ways to use your data and insights from science to ride better than ever.


  1. Allen, H. and A. Coggan, Training and racing with a power meter. 2nd ed. 2010, Boulder, Colo.: VeloPress. xviii, 326 p.
  2. Van Cutsem, J., et al., The Effects of Mental Fatigue on Physical Performance: A Systematic Review. Sports Med, 2017. 47(8): p. 1569-1588.
  3. Noakes, T.D., Time to move beyond a brainless exercise physiology: the evidence for complex regulation of human exercise performance. Appl Physiol Nutr Metab, 2011. 36(1): p. 23-35.
  4. Borg, G.A., Psychophysical bases of perceived exertion. Med Sci Sports Exerc, 1982. 14(5): p. 377-81.
  5. Scherr, J., et al., Associations between Borg’s rating of perceived exertion and physiological measures of exercise intensity. Eur J Appl Physiol, 2013. 113(1): p. 147-55.