What?! Please link me where we said this, and I will preempt and say if we did say this we were wrong.
I’m going to share some SSB HV data tomorrow on the podcast. It’s very good at making some athletes faster. It’s also only for around 7% of our athletes.
We definitely have a problem with athletes picking plans that have too much volume for them. We need to put more guard rails into the system to help them self select down.
There is a vibe on the internet that “if the plan didn’t work for me, then it doesn’t work for anyone.” That is not the case.
I think you should think of it as a “Ramp Test Result” rather than your FTP. That helps us get out of all of these “what is your FTP and how to measure it” debates.
It’s more of “What can you do, and how can you improve it”. The ramp test gets you on the way to getting there and it’s fairly repeatable.
If you have a 90 minute workout and all the non-interval periods are the same, then 5x10 is the same IF/TSS as 1x50. At most there might be a 2-3 difference depending on the NP window and where you put the workouts.
It’s definitely not enough to demonstrate the difference between 5x10 and 1x50.
As of now, all of it is used for AT or at least looked at. We try to identify and throw out rides with faulty equipment; ie sustained 2000 watt spikes. Stuff like that.
And yes, it was totally time consuming! This is one of the reasons why we’ve been doing this for three years.
Yes, subjective feedback should improve the model. But that doesn’t mean there’s no value in the data today.
Interesting. I would love to see what you can share on the predictive (or it sounds like non-predictive) value of these two metrics, as they are so embedded in the cycling training culture. So weaning people off of these will take a lot of education.
Exactly this. I’ve seen this other places with ML: the answer can be very counter intuitively, and it isn’t always obvious how to explain the answer / direction it gives you to draw human understandable “heuristics”.
This is going to be the other challenge: AT’s recommendations will be a true “black box”, without human explainable reasons for the recommendations. So comparing what it is recommending vs. what a human would recommend and why isn’t going to be easy, or probably possible at all.
I might have missed the explanation in the podcast, but I see TrainNow, I do not see the “achievable/reaching” bubble and I don’t see the “alternatives” beside “variants”. Is that because they’re coming in the future, or because some of those are reliant on having used TrainNow to select workouts previously, meaning I have to use it at least once to get it to start working for me?
Train now is a feature for people who want to “workout” without being on a plan based on your past workouts.
You won’t see the other features
(achievable, etc) until the AI training is launched. This is just a suggestion for workouts to do each day of the week. But it is up to you how intense you want it. So if your following a plan already, it’s pointless to switch workouts. Also because train now is more for someone not currently following a plan.
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I’m just going to add that this is it’s a long, the lines of what we did with
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our high volume of sweet-spot base.
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It was a request.
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We didn’t really think the science was there.
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We didn’t really want to prescribe it, but we got a lot of requests and it was
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like, well, a lot of people want to do it.
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Let’s let them do it.
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Let’s see what happens.
Im glad some people find value in that. My comment was specifically pointing out that you guys offered that plan based on commercial interest and not on the evidence that is a good practice.
