AI FTP Detection Update

I feel it’s important to recognize the intricacies of this process. TR via Ivy, relayed that at least part of the AIFTPD results you have are from “edge case” issues. Those are important to recognize since this may not be the norm or what many others experience.

Of course, it IS what you are experiencing and completely valid to consider and review. But as mentioned TR is now aware of the issue since you contacted them about it (the point of the Early Access / Beta process) so they can take that into view and consider how better to serve your situation and anyone else in the same boat.

Point being that broad criticism of the overall AIFTPD tool based upon an edge case should be avoided. It’s worthwhile to recognize that these exist, but many others are having a solid experience. Hopefully, by relaying these odd results to TR, they can improve the process to better cover the edge cases.



It’s the percent of rides failed.

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We have different personas who use TrainerRoad and I’ll suspect we’ll have different metrics for different people.

There’s for forum people who want ALL THE METRICS. Then there are “peloton” like people who want motivation from their metrics.

We can give both groups amazing training, but what is presented to them might be a bit different.


I’ve got the solution!

if (athlete.IsForumUser == true){
   ftp.DisplayName = "tFTP"
   ftp.DisplayName = "FTP"

Ship it!
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Definitely one for the meme thread!



But what about the Edge cases where the user’s forum_name != training_profile_name

Drive Off Cliff GIFs | Tenor


Agree completely and I’d even argue that most of the users probably don’t need to be educated in order to train well and drive results. Seems like a waste of valuable resources that should be focused on stuff that really matters. If FTP was redefined (or relabeled), people on the forum would still poke holes and debate the merits of different approaches. If everything was buttoned up and perfect, that would be so boring…

I think FTP as a carrot is really what the masses want. A simple number that can be pushed higher. I think I have a decent understanding of what the different flavors of FTP are and how they can be misleading as a fitness measurement, but I’m still guilty of putting too much weight on that number.

If the goal is to more accurately measure fitness, I’d look to expand from a single FTP number to more granular numbers in each zone. Progression levels for each zone gets you part of the way, but you can’t compare progression levels across time without the context of the underlying FTP (or tFTP) that drive the actual work output. Comparing your power curve over time also tells part of the story, but is not really relevant if you have not done workouts focused on max effort for x minutes. When I want a good measure of where my fitness is at, I lean heavily on comparing the results of the same/similar workout at different times. It’s an imperfect science at best, but it’s the best approach I’ve found. I just had a Vo2max workout on Tuesday that I did about the same time last year at the same phase in my training. While my set FTP for the workout was 5 watts higher this year, I was a little fitter last year and the workout showed this (even though the interval power targets were lower for last year’s workout). As much as I’d like to see a more comprehensive way to measure fitness over times (with zone granularity), I think this is another area where it’s not a high priority for most. More carrots are good though, people love carrots.

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My answer would be that training isn’t just muscle perfusion and oxygen delivery. Some athletes might still look forward to the mental toughness of an all out ramp test especially when the pain brings “hey, I can just bail out and TR will still tell me my FTP” into your frontal cortex. I find this is very similar to the “hey, third place is still on the podium” during the final minute of a hilltop finish.


Agreed. Provided the education gets real. There are some great snippets on TR site, like this one:

But if you read the forum, there is a group that dismiss FTP. And some hand waving about getting it right isn’t important, because its compensated by Adaptive Training and a progressive structured training plan driven by AI / Machine Learning that tweaks workouts by using millions of workouts and looking at tons of dimensions (that’s a technical term :wink: :rofl:). Your decision on what to believe.

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Sure, i didn’t know if everyone in the support teams knows that i am a edge case like Ivy said, so on every answer there is a different member of support team on the same ticket

So much I/we figured;-) But what’s the range? 0–1? 0–50 %? Or even 40 %—60 %?

You could make one yourself using excel and intervals icu

I wonder how much of an edge case people really are. I’m sure in all walks of life be it TR power profile or ability to loose weight or learn a language most people aren’t really an edge case, there’s too many edges and most people (myself included) just aren’t special and will be an “average” person.


It sounds like you’re taking a more philosophical approach to this question, but in context

it means “outside the norm in TR’s data”. We don’t have access to their data, so we just have to trust them (or not :laughing:) if they describe a particular scenario as an edge case.

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100% this. Sounds like a really simple way to allow fine tracking and easy comparison (incl’ historically).

Yup, if we think of the bell curve that exists in most things, some people (or use cases) are closer to the middle where the larger population exists, with others more towards the edge of the curve (hence the name). It’s not so much about the person in this instance, but the data that TR is receiving from that user and how it lands in the system (AIFTPD in this case) relative to a larger population of users getting better & desired results.

Those edge cases are legitimate and should be investigated to see if TR can improve to handle those outliers better. But it’s important to recognize that from the many other positive comments about AIFTPD, it seems to be working pretty well overall. The results are likely even better when we note that we tend to hear more about problems than successes because people are more vocal with negative issues and than positive ones.

From what I have seen and what TR tells us, AIFTPD is working well to address a large number of TR users. I replied above to point out what I saw as a broad statement questioning AIFTPD validity on a large scale, based upon the less than ideal results of these edge cases.


Oh definitely more thought experiment!

Just a comment really, and I saw that some of the cases did need looking at and my comment wasn’t really pointed at any one person particularly.

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