Introducing a really cool and useful new feature, available now to all TR athletes: Machine Learning-Based TSS Estimation, for outside rides with heart rate but no power data.
Training Stress Score (TSS) is a convenient way to track training stress over time. But TSS relies on power data, and almost none of us are lucky enough to ride with a power meter all the time. For outdoor rides without power it can almost feel like you don’t get credit for your work, or like the ride somehow doesn’t count.
RPE and Heart rate-based TSS calculations have long been available for these rides, but these estimates use simple mathematical formulas and tend to be wildly inaccurate, and we knew we could improve this with insights from our enormous data set of real-world rides. After analyzing hundreds of thousands of workouts with machine learning models, we developed a more nuanced and accurate TSS estimator, derived from heart rate and other metrics recorded during outside rides. No, it’s not a replacement for power in all your training. But as a supplement for rides when power is unavailable, it’s a powerful tool that helps you track ALL your hard work.
ML-Based TSS estimation can be activated via TrainerRoad.com (click on “Account” and then head to “TSS Estimation” under the “Profile” menu). You can choose to apply it to individual rides, all future rides with heart rate but no power, or all past and future rides that meet these criteria (existing RPE-based or manual TSS estimates on past rides won’t be automatically overwritten). Once you set it up, it automatically syncs to the latest versions of all TrainerRoad apps, but you can always adjust or turn it off, too.
For more info and a short FAQ, head over to the feature announcement on our blog:
Machine Learning-Based TSS Estimation for Rides Without Power Data
Give it a try on your next power meter-less outside ride, and let us know what you think!