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:
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:
Also, see how his FTP improved over time compared to AI Endurance’s predictions:
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!
by Stefano Andriolo. We demonstrate a universal relationship between cycling power and DFA alpha 1 from every day workout data that allows accessible and regular tracking of aerobic and anaerobic thresholds without the need of an exercise lab or even a dedicated testing protocol.
by Markus Rummel. Coaching endurance athletes well takes time. Reviewing data, adjusting plans, responding to how each athlete is recovering and progressing: there is a limit to how many athletes you can closely monitor before the quality of attention starts to drop.
Daily readiness to train is affected by many factors including sleep, illness and training load. Heart rate variability (HRV) readiness to train metrics typically rely on measurements taken immediately upon waking in the morning. We introduce an HRV readiness to train and a durability metric based on DFA alpha 1 (a1) measurements taken during exercise. These new metrics provide additional insights and do not require you to measure HRV upon waking.
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.
