How to Improve FTP in 8 Weeks With AI Endurance: Success Story

How to Improve FTP in 8 Weeks With AI Endurance: Success Story


Published May 10, 2020 · Updated Jul 25, 2020

We recap the results of following AI Endurance’s cycling training plan that saw our FTP grow according to AI Endurance’s predictions, following the instructions on how to improve FTP.

We - Dominik and Markus of AI Endurance - have been training for Paris to Ancaster 2020 , a gravel grinder race that was supposed to happen on April 26th 2020 but was cancelled because of Covid-19.

Nevertheless, we stuck to AI Endurance’s 8 week cycling training plan and saw great improvements. If you missed earlier posts about our journey, you can find them here:

Results

Dominik grew his FTP to 283 W in 8 weeks, slightly better than the prediction of 261 W, achieving a new PB with AI Endurance’s individualized training plan. His thoughts:

Markus grew his FTP to 299 W in 8 weeks, also slightly better than AI Endurance’s prediction of 293 W. His thoughts:

Check out Markus’ FTP test on Strava:

20 min FTP test - strava

Also, see how his FTP improved over time compared to AI Endurance’s predictions:

how to improve ftp

How to improve FTP - Getting Started

Don’t waste your time with one-size-fits-all training plans. Use AI Endurance’s predictive data-driven approach instead to improve your FTP and get that PB! Get your own personalized training plan today!

Share on:

More Blog Posts


Gear checklist for optimal data flow into AI Endurance

Gear checklist for optimal data flow into AI Endurance

AI Endurance is a data-driven training platform. In order to maximally benefit from the training and have the program be most personalized to you, you'll want the best possible data to flow into the platform. Here's a few recommendations on how to achieve this.

Published Sep 22, 2023
Which training really works?

Which training really works?

We compare polarized training, threshold training and AI optimized endurance training. AI optimized training yields the best results, followed by polarized training with threshold training in third. The results are inline with current exercise physiology research. If the training composition is not optimized to the individual athlete, substantially smaller gains are to be expected.

Published Apr 15, 2021
Real-time readiness with alphaHRV and AI Endurance

Real-time readiness with alphaHRV and AI Endurance

by Stefano Andriolo, Markus Rummel and Iñigo Tolosa. We present a new real time feature of evaluating readiness to train based on in-activity heart rate variability (HRV) measurements during the warm up of your activity. You can use this feature in the newest version of the alphaHRV Garmin Connect IQ app at no additional cost.

Published Aug 28, 2024
Determining the power  and DFA alpha 1 relationship accurately

Determining the power  and DFA alpha 1 relationship accurately

by Stefano Andriolo. Building on previous work, we refine a method to accurately determine the relationship between DFA alpha 1 and power. This method can be used to track fitness and thresholds of an athlete. We find in some cases ramp detection tends to overestimate thresholds, a finding mirrored in recent physiological papers. On the other hand, thresholds based on clustering of DFA alpha 1 values tend to agree well with this new method. We propose a hybrid lab and everyday workout experiment to further study the relationship.

Published Apr 26, 2024