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!
DFA (detrended fluctuation analysis) alpha 1 is an HRV (heart rate variability) based aerobic and anaerobic threshold estimation method. It only requires a heart rate monitor that tracks HRV data. It has the potential to track your thresholds automatically without dedicated test workouts.
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.
by Grant Paling. In my last blog, I closed the chapter on my European Championship race in Portugal. It definitely was an intense period leading up to, during and after the race. So many emotions, so much effort invested and then…what’s next?
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.
