Adaptive Training Closed Beta Update

LOL - too funny!! I read the DCRainMaker article last night and can’t believe Shimano botched the transition / shutdown of Cyclo-Sphere site so badly.

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I wonder what criteria? Would it help if I asked @Nate_Pearson for his Venmo info? :rofl:

Did an outdoor workout this afternoon, got a “Checking for Adaptations” popup immediately followed by one with “No Adaptations Required” which I expected since the PL for endurance stayed the same plus I’m doing the Polarised Plan so not sure if adaptations would actually be applied anyway.

The workout was done just with a HRM, no PM on that bike, and the eTSS is about 50% above that worked out by Intervals.icu

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Yeah, it sounds like they added some more transparency to when it is checking for things and when it thinks it should adapt or when it thinks it’s got you on the right track. I kinda like that it will provide those updates so you know when you’ve done something that’s given it an idea for the future.

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If I had to guess, they profile certain users eg uploads from Strava, unstructured outdoor rides, users of certain devices etc and target areas where they need more data to tease out problems. I imagine that indoor users using plans almost exclusively are not going to find bugs as easily.

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It would be interesting to know how this accounts for different run types. Hill vs Track vs Trail

Got my entry in this week. Wow!
Fitness profile explains everything I’ve suspected about myself. I’m a VO2max monster with no ability at threshold. It’s adapted my plan with the Threshold workout levels lowered and some of the VO2max raised. Hopefully long term I can raise my Threshold and Sweet Spot levels.

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The profile is really only what workouts did you do lately or thats how I see it. My sweet spot has fallen as I dont do sweet spot workouts at this time of the year. The decay rate to me doesnt yet make sense.
Endurance is also low vs what I did and what I know are very easy rides for me. In my recovery week it was offering me a very low PL ride…well I picked a ride that was still just an easy spin and at 3.5 it was above my PL. I do VO2 workouts now with long rides outside and that is obvious from what the profile shows. My outside rides dont factor into any of the ratings.

I am mixed on this being a fitness profile at this point. It may get there as AT evolves and outside rides get factored in.

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So they should all be let in by now as it’s already working for them :ok_hand:

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The team is still working through bugs on workouts pushed to Garmin and done outdoors not showing and PL progression – based on my experience last week and now again this week. Interestingly, my Mon and Wed outdoor workouts this week did provide PL increases, but now again today’s did not. Today I did use an alternate stretch threshold workout with a higher PL than in my plan target (3.0 vs 2.4 planned) as I completed a 2.4 Saturday that didn’t show anything when it uploaded and ran through AT.

On today’s 3.0 Threshold I believe I was within target range, but got the struggle survey. Indicated no struggle, rated the next survey very hard, but no increment in PL. My support team buddy:) has all the info.

Learned from support that the evaluation is binary – either you hit the workout or not. So all my hard work to blow out the top end of the target range on intervals apparently has no impact (that does seem different that the concept of a super pass discussed a while back. You will never get an increase in PL that takes you above the level of the workout you’re doing.

Others may know the info above, but I’m working to learn exactly how the process works.

Ugh. Maybe they should just get rid of the up/down adjustments of difficult while they are at it :slight_smile:
I hope this is a temporary limitation, as I think this has things way too tightly tied to there being a ‘workout’ involved. I think the main case where tying it to a workout is the struggle/fail case, where it is important to know what it was you struggled/failed at. If that workout was too hard, then easier workouts should be given in the category.
In the pass case, I don’t think the assigned workout really matters - your PL should be adjusted based on what you actually did. If I go out and do a ‘ride’ that is really 3x20 at power X, this should count the same is if I do a workout that 3x20 at power X.
I realize that tying it to workouts simplifies things a fair bit, but once outside rides are analyzed effectively then this really should be more flexible. That part isn’t there yet, but I really think in 1+ years things should no longer be so rigidly tied to the selected workout.

Is this restricted to Outside Workouts?

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Had my reservations but credit where credit is due, the approach with progression levels is working surprisingly well. It is simple yet elegant and shifts the singular (and obsessive) focus from FTP to the whole power curve. Also find that it leads to more variety in the workouts that I am doing, where in the past I limited myself to SSBLV 1&2. Great job!
So far I did not detect anything in the Beta that would require machine learning, but might be missing something.

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I don’t know @mcneese.chad ; I am only doing outside workouts so didn’t ask.

I’m right with you on that (get credit where credit is due, yes?) but what I understood from the emails with support is you only get what the structured workout is designed to deliver. Would love to be wrong on that – or even better, have it work as we’re discussing on actual results.

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Two weeks ago structured outside workouts had to be a TR workout exported to Garmin, in order to update Progression Levels.

Currently my Garmin is getting workouts from TrainingPeaks.

Today a have 1 hour tempo at 75-85% workout.

If Progression Levels still requires a TR workout executing on Garmin, that means I would need to pick a TR workout and send that to my Garmin. For example both of these have outside versions:

and based on the course I’d pick White +1. Also, all my levels are 1.0 and I am ignoring the Not Recommended.

Do you know if TR-structured-on-Garmin is still a requirement? Or should I roll the dice, do the TP-on-Garmin, and just deal with possible no progression updating?

At the very least: 1) assigning workout levels to the workout library was done via ML, but that’s more of a static prerequisite to AT rather than part of the adaption process; and 2) classifying workout success/failure may use ML also.

Whether the actual adaptation process uses it right now, I’m not sure - since it seems quite simple in terms of picking an alternate from the plan that matches your PLs. But there could be more to it than that, and even if there isn’t currently, there definitely could be in future.

Roll the dice and see what happens.

I assume that AT needs a structured workout in TR’s database to determine which energy systems are getting worked and to calculate the PL. I think it then compares your ride (power, HR, etc) to the planned workout to determine if you passed or struggled.

As long as you have linked your TP and TR accounts, then rides created in TP get imported into TR. I would think AT treats these like any other custom workout.

I’m making a lot of assumptions but this is how I’d set it up.

I’m going to see what happens when both TP and TR are pushing workouts to Garmin Connect. Hoping worlds don’t collide on my 530 head unit!

image

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Think both points are unlikely.
Regarding 1), that would assume the TR team just created a whole bunch of workouts and then subsequently used ML to rate them… That would be very inefficient and unnecessary. If you start mapping you will find that there are strong linear correlations between the workout levels and simple metrics like IF and total kJ. No need for ML, fairly simple analytics suffice to get the levels. Or reverse, this can be programmed into a workout creator to help guide the process (which is what the TR likely did; analytics are different for different zones).
On point number 2), that is a pretty basic problem to solve analytically as well, especially as the consequences of an error are very limited. So no need to create a big data set including human judgement (think physicians rating a diagnostic image) to feed a ML optimization function.