Stay on top of your goals and support our local businesses and charities at the same time. A virtual cycling challenge that comes as close to a race as possible now that social distancing is crucial in slowing down the spread of COVID-19.
During these difficult times AI Endurance wants to do what we can to support the amazing cycling community in Southern Ontario. While we understand there are more important things right now we also don’t want your hard earned fitness to go to waste. Like so many of you, we are all disappointed the upcoming races such as Paris to Ancaster are cancelled because of COVID-19.
Check out our running virtual challenge:
In order to support local businesses (many of which are struggling during this crisis) and charities such as the St Joseph’s Hospital Foundation, we came up with a new virtual “race” format, a virtual cycling challenge, that respects social distancing.
Join today and compete in AI Endurance’s Virtual Martin Road Challenge! The winning prizes are a $25 gift card for the overall fastest female/male times from a local business of your choice or a donation to the St Joseph’s Hospital Foundation. The top 3 of each age-group will get a free subscription to AI Endurance.
The course is the final ascent of Paris to Ancaster up Old Martin “Road”, see Strava segment. The start is at the intersection of Old Martin Road and Mineral Springs Road where you turn of the tarmac road onto the trail. There are some challenging technical parts in the first part, finishing off with the infamous Martin Road climb. The segment finishes after the climb where the finish line of the Paris to Ancaster would be. Make sure you stick to the route exactly as otherwise Strava might not enter your effort into the segments leaderboard and your effort won’t count towards the ranking.
Ride the Strava segment as if you’re in the race. Give it your best effort! Pay attention to traffic - safety first.
Make sure you record your effort on Strava. All efforts recorded between March 22st and April 30th count towards the virtual ranking. Pick the day where you feel best. You can do multiple attempts - we’ll count your best attempt.
Important: We urge you to respect all regulations and restrictions related to COVID-19. For updates on the general situation please consult the authorities resources.
Instead of high-fiving in person after the race, we’ll celebrate each other in the virtual space! #martinroadchallenge #aiendurance
by Grant Paling. I’m back. It’s been a few weeks and ultimately, a lot of time to process what happened. If you’ve been following my European Age Group Championship adventures, you will know that the triathlon went well. Very well. But let me give you a deeper insight into the race, the mentality I took into it and then in following blogs I’ll reflect on how that performance was achieved using AI Endurance.
AI has great potential to help us as endurance athletes improve our training. In this post we will discuss how AI endurance training works.
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.
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.
