Do you still use monotony and strain to monitor your athletes for overtraining?
Well in this weeks #PowerBiForSportScience tutorial I will show you how to calculate monotony and strain to power up your monitoring reports.
\[\\[0.1in]\]
\[\\[0.1in]\]
We have spoken a lot about the #ACWR in a couple of videos and posts. But before the #ACWR, monotony and strain was often used to monitor athletes for overtraining. What are these metrics?
There is a great article on simplifaster1 which talks through the concepts. But simply put, monotony shows the variation in training load, by using a training load average divided by the standard deviation of that load, over a specific period. In the example of our video, we used 7-days.
Monotony = Training Load Average / Standard Deviation
Whereas strain, uses the load on a given day times the monotony on that day.
Strain = Training Load * Monotony
Original research2 highlighted that a high percentage of illnesses could be attributed to the strain of exercise, particularly when it became highly monotonous. Using a combination of both #ACWR and monotony/strain might be beneficial to your monitoring going forward to identify athletes most at risk of overtraining injuries or illnesses.
As always, if you enjoy this video and post, make sure you hit like on the video and subscribe here for more videos to help you Power Performance Through Data.
Enjoy the video and if you have any questions, please leave a comment below.
Thanks!