AI FTP detection is smarter than AT

Very true. The funny thing is that I’ve never met a bigger fan or advocate of TR than me, and I said so. It’s kind of like how you only tease people you like. One of my hopes with starting the thread is to get TR to look at possible improvement, although I doubt they’re not already working on it in some way.


Same! I just bumped two threads today to show Ivy and the team that I’ve encountered issues. I do that out of love for the product and team, not angst.

I’ll defend TR but I usually try to point people in the direction of looking into through blog posts or whatever or then getting ahold of support. Humans have a way of Complaining and when they get their answer they’ll find something new to complain about. Now I’m complaining about complaining, I’m not any better. In the end I try to share TrainerRoads information that they have online. Usually it answers questions and if not support hopefully will.

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I think this is an accurate assessment of it’s limitations. Endurance, tempo, sweet spot, and Threshold are all zones that are based of % of FTP and not all out efforts that are FTP dependent - so in my opinion when your FTP increases those zones should not decrease - maybe decay over time like they do now, but they are not tied to FTP as close as Z1-Z4.

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I largely agree with you, but as someone who really struggles with threshold (vs V02 for example), I definitely need the threshold PL to change when the FTP steps. If TR used a format where your threshold floats daily rather than steps every few weeks, lower zone PLs wouldn’t have much use and wouldn’t budge much at all. But I think I prefer the way they have it set up with fixed/stepping FTP and floating PLs.

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Agreed - if you have a bump, bringing threshold down will help you complete those workouts (a 2.0 vs. a 5.0 for instance), but you can do VO2 at any time much higher than a 1.0 - if you’ve done them before and have experience with structure because they’re not tied to %FTP and should be done in resistance mode.

Thank you for coming to my ted talk.

*Cunningham’s Law

Lol, had to do it, it’s the law.


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TrainerRoad !!!

That doesn’t seem correct, they dont rely on each other at all since you can manually test or set ftpa nd have AT adjust PL or the inverse by overriding suggestions.

From a ML management POV they would also need to be separated to a large extent or the moving parts wouldnt iterate. They were also largely based on the user data set before AI FTP was implemented so its not dependent on AI FTP to work well.

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I think you are misreading what I wrote. I meant that they are made to work hand-in-hand even though they are independent modules. AI FTP was mentioned when AT was first unveiled as a part of TR’s ML efforts.

I guess I disagree with this assessment. They don’t seem to be necessarily designed with one another in mind in that they work better together. They work perfectly well independently of one another and depending on use case work better separate, at least in the current iterations without outdoor ride support.


Don’t take my word from it, just re-listen the relevant part of TR’s podcast where AT was first unveiled: @Nate_Pearson had a section explaining that their intention is for athletes to never have to take an FTP test ever again — he was describing AI FTP without calling it as such.

Yes, you can use both independently (I like ramp tests, so I am going to keep doing them for now), but it is part of a larger vision for TR’s future and they have been designed by TR to work in tandem — even though you can use them separately.

The fact that you can use both separately is arguably an advantage: even if you e. g. don’t currently use AT, you can use AI FTP. Conversely, I use AT without AI FTP — I like doing ramp tests. :slight_smile:

Minor nitpick: AI FTP does take outdoor rides into account, AT currently does not.

That’s what I mean by it working better for some people separately. If you ride mostly unstructured outside. You would be worse off relying on AI PL to set your training. You’d be better off only using AI FTP or some other capacitive effort to determine FTP and using a pretermined outdoor training plan or something custom combined with self or paid coaching.

Sure, I’d agree with that…but thats just furthering the case that they are perfectly fine independent of each other and don’t necessarily make the other better. The fact Nate doesn’t want people to have to do FTP tests is an independent outcome in itself as well.

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I think you need to separate how well a ML algorithm works from how simple it is. ML isn’t magic and needs qualified people to implement. E. g. how do you evaluate how well an algorithm works? What variables do you include and what variables do you exclude? This is all quite tricky, because it requires domain expertise (here: exercise physiology) and expertise in ML.

Overall, it seems to me that you perceive something as simple when “all it does is adjust some progression here and there.” AI FTP is “even simpler” from that perspective: it just spits out a number in the end. Does that mean it is simple? I don’t think so.

TR has released stats that their athletes e. g. have lower failure rates when they use AT, are more consistent and have better outcomes. That doesn’t mean it works equally well for everyone or that it doesn’t have any flaws. (I can list some if you want apart from outdoor workouts not being included at present.) @Nate_Pearson also made public that they have an overall fitness score (in addition to plenty of other markers). The difficult part IMHO is how to judge success. No doubt that future evolutions of AT will revisit that (by e. g. emphasizing certain metrics over others depending on the plan that you chose).

No, a linear regression is not Machine Learning.

No offense, but I think you fundamentally don’t understand what ML is and how it works. You seem to think that something is simple if the output is simple. AI FTP is a complicated function that computes a single number. The difficult problem is finding that function from statistical data without knowing it in advance.

So you could also have a complicated algorithm (whether it is based on ML or not doesn’t matter for the moment) that determines ramp rates. Still, just because the output seems simple to you doesn’t mean the function is.

Why does AI FTP seem more advanced to you?

I think that’s quite simplistic, and is not really what this discussion is about. IMHO we need to tease out whether the premise is “Why does AI FTP work better than AT?” or what @patrickhill means by smarter?

You can go into the technical details (to the degree that they have been disclosed publicly, of course) without being a fanboi. E. g. opining that you shouldn’t use “smarter” in this context is different from claiming that either one or both work wonderfully for users. (When I used AI FTP last after a 4-week hiatus, its estimate was way off.) Ditto for pointing to TR’s public resources.

I took the liberty of emphasizing two words in your post: I completely agree with how you phrased statement, because I just wouldn’t say that one algorithm is “smarter” than another. The latter suggest the techniques used by AI FTP are more sophisticated, but that is not the case. You can have a complicated solution to a problem that doesn’t work very well … :slight_smile:

If we want to discuss how well AI FTP works compared to AT, that’s a great discussion to have.

Except that in this case it arguably does: AT has been designed from the ground up with AI FTP and scoring of outdoor workouts being part of the overarching vision. These were known limitations of the first release of AT, and TR works on filling in the missing pieces. AI FTP is out as a public beta and outdoor workouts is in internal beta. The fact that both provide utility on their own is an added bonus.

PS I hope that in the future AT will not just look at PLs of individual workouts but of time in zone across a week or so: I often tack on 30-minute endurance workouts, but these have ridiculously low PLs. They don’t raise my endurance PL even though they arguably raise my fitness. But I digress.

Linear regression is a fundamental algorithm in the field of supervised machine learning.

Y’all have lost me on whatever it is you are debating.


I don’t want to sidetrack the discussion too much. Linear regression is an elementary technique from statistics, which, yes, is used in machine learning. There are also plenty of sophisticated purely statistical approaches to Big Data (a keyword, which has been replaced by ML in public perception at least), but teasing those differences apart gets tricky.

However, when someone who with very high probability is a non-expert in the field (an assumption I make) uses words like “linear regression”, I think that they think of the simple tool from statistics where you plot 2d data points and “draw a best-fit line” through them so as to minimize the deviation. I could be wrong here, but I think it is reasonable to make that assumption.

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Elementary? It’s a foundational algorithm and can be multivariate / multidimensional. I’m done with the debate. Good luck :+1:


I work in ML, I think it’s very easy to have scenarios where the AI FTP model is more appropriate than AT. Even if they both use the same raw dataset to train. The two programs have fundamentally different outputs: one is an single FTP number, the other is a set of adaptations. Thus making them very different things.

Anyways, knowing how it functions doesn’t help at all in this case. If one tool is more accurate and helpful then I think it make sense to base your training on that in this instance. That said, I’m doubt these micro tweaks make a big difference in the big picture, so unless AT is waaay off, I’d just do what it says and save on mental load.


To me it is elementary, the first fit algorithm I have learnt when analyzing data. I have seen plenty of abuse (including applications to plots with two data points :rofl: in a poster in the geology department, where I had an office for a while, or the obligatory point clouds where no linear dependence between the variables is evident), too.

Why? Unlike me, you have proper expertise in the topic, and I think your opinion would be valuable.

I reckon that scoring is much simpler with AI FTP than with AT, so from that perspective at least, AI FTP seems to be the far simpler problem. On the other hand, it could also mean that this explains why some people in this thread think AI FTP works better than AT.