Am I the only one not bothered about Adaptive Training?

If you go into the calendar and click on the scheduled workout, you have the option of choosing “Indoor” or “Outdoor”. If you choose outdoor, it will push the workout to your head unit–then you get on the bike, follow the workout on the head unit, etc…

Manually would be just going out and knocking out a 2x20 or whatever without running the workout on your Garmin/Wahoo/etc…


One step closer to free-ride analysis that TR has stated as being top priority. Can’t wait for that to come out. Then it’ll work for me.

For the OP question, not not the only one. I’m in AT but not actively using it. It’s fun to play around with it, but haven’t done much other than watching the levels and seeing hoe they move up and down.

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I think you have misunderstood the thread. “Outside workouts” is just that, outside workouts not free rides. That means you are pushing a workout to your head unit and doing a structured session. I have listened to the podcasts as well as read the blog posts. Nowhere does it indicate that there will be a calculating against off-plan rides.
Again, I think that there might be some potential to blend their current model with traditional TSS/CTL/TSB measurements that they have historically used in the future, but they have been clear, they are pursuing a new paradigm here. The premise behind the model is that the rider follows a structured training plan and it serves up workouts based on success rate to previous workouts relevant to the rider level. It isn’t going to account for your noodle rides, group rides, or races. They are ultimately irrelevant. If those rides impact your success rate on your next workout, it will account for that and subsequently adapt future workouts. It accounts for outdoor rides indirectly, not directly.
What Nate was referring to in the podcast I think speaks to more of what I was referring to. This would apply to Zones 1-4 where many of the plans don’t have as many touchpoints during parts of the season. Endurance being a primary example of this. I don’t see any success at anything over threshold.


I’m in the beta but its pretty useless for me as well…

The outside rides thing is a huge part of it but the main thing is the fact it only adapts Plan Builder created plans. There is a really VERY good part though - the Progression Levels and ranking and searching for workouts by PL and whether it would be Achievable, Productive, Stretch etc. Makes finding and choosing a session easy and effective.

Hoping the rest is sorted for next winter…


AT is amazing for me and I suspect people like me.

I used to ride trainer maybe 2 months out of the year here in CA when it’s “cold” and go ride outside rest of the year. I feel like I lacked the discipline to ride structured. I’d just attempted PR’s on hard days and just chill and spin easy on recovery days. I feel like I never hit my potential. I would feel the progress but it was hard to quantify.

I now choose to do 3 (LV) intervals every week indoors no matter what and I’m able to track progress via workout levels so it’s more quantifiable and objective. I’m also able to JUST finish these rides instead of having it be just a little easier than I want or just hard enough that I get wrecked and feel like I failed.

Outdoor rides on top of those are unstructured, no pressure, topping on the cake / bonus and feel pure fun now. I don’t depend on them for progression although they do add to my fitness. If it’s too cold? A little too late in the day? Don’t feel like it that day, it’s no big deal cause I can depend on AT to progress forward that week even if 3 rides is all I do. Slower progression, yes, but progression anyway.

I wouldn’t sweat not being in AT but I think it’s pretty huge improvement on an already great product.

I think the issue (or lack of enthusiasm) mentioned in the OP is about trainer riding in general and nothing to do with AT to be honest. and I shared that view in the past before I fell in love with the efficiency and convenience of indoors training.


Off-plan workouts are already considered, but not adapted AFAIK.

It has been stated by TR staff, multiple times, in various threads, that unstructured riding is very high on the list of things to achieve.

We can only take that as the given intent. They understand that without this they can’t be truly adaptive.

TrainNow already looks at all cycling work, as far as it’s recommended workout focus areas. But not comprehensive enough for adaptive recommendations.

Here’s just one of many statements. These statements go back as far as the AT launch and even farther back in terms of winks and hints.

Emphasis on all, in this quote, in response to a post about unstructured rides.

Timeline not stated but it is a key component that many of us need/want.

For my presumptive comment with fierce insistence…

In order to properly account for outside workouts you need to account for the free-ride. They go hand in hand. What is measured by the lap button is not comprehensive.

Your outside workout may just be a small portion of your ride. A coach says ride 2400kj and then hit some intervals. The programmed workout may just be the interval portion. A system with blinders on is useless. A live coach would consider everything.

We know that plans and events on the calendar are needed to gauge the phase and focus areas. But, that is independent of things like progression levels. They are related but not tied.

In other words they will need to gauge work done, even if a workout was a fail. 4 hours into my 5 hour workout, I crumble. That doesn’t mean everything I did before the failure doesn’t count nor does it mean a success. (I believe SeanHurly has mentioned this somewhere as well). All of these are inter-related. You don’t always need to have an intended target or known intent in order to gauge work. Work done is just that, and it matters for the total picture. If I have a 1x60 at tempo, SS, threshold, whatever, on the calendar how is that different than hitting a 1x60 in a free-ride? Intent matters but it is not as finely crafted to be put on a pedestal as you are implying. The work done impacts corresponding energy system is impacted as well as it’s related systems.

We also know, from things like the podcast, where TR doesn’t want the feedback adjustment to be based heavily on pass/fail. They want you to pass and not overreach, so there needs to be a full accounting. I can’t recall which podcast this was in, but it was mentioned and my interpretation is subjective. What currently is may not be what will be the release. We don’t know.

A system that reacts purely, is not adaptive.

I won’t comment further in this manner as I just don’t know the code nor the model/architecture. I can infer, but that will just be confusing for everyone involved.

@IvyAudrain or @SeanHurley can follow up if they want to.


This is exactly what I’ve been doing. Or more specifically, I do some of the workouts solo outdoors on my Garmin and follow them as if they were indoor. Then if it’s a team or group ride where there is no hope of sticking to structure I follow teddygram’s system.

But AT, as others have said, is in Beta. If you’re not in it (yet) think of those who are as the lab rats spinning the wheel (literally!) to get the data to refine the algorithm for you when it’s retail. It’s entirely possible, for example, that the current AT models are detraining all the beta testers compared to those who are using some other system. We’ll have a bunch of cool numbers on a screen and you will drop us on the first kicker.

What would you want it to say about your outdoor rides? It’s job is to adjust your planned rides based on performance on other structured rides. Im pretty sure your outdoor rides would say almost nothing about what your next threshold workout should look like. I think people think because it doesn’t adjust progression levels for outdoor rides that it can’t be accurate based on the rides it does analyze. And, if you are doing that much unstructured riding that you don’t have a base of structured rides for it to use, why would you care anyways?


The presence of the survey on unstructured outside rides suggests to me that they’ll eventually be taken into account. Assuming they aren’t already?

That is not correct: they explicitly included unstructured outdoor rides in addition to outdoor workouts. Unstructured rides means anything other than a TR workout, indoors or outdoors. They are currently testing feature this internally at TR, and it is not included in the closed beta.


If you think that I’m guessing you probably don’t listen to the podcast.


Just a quick follow-up: the exchange between @Hampstenfan and TrainerRoad’s @IvyAudrain covers this very explicitly. In the @Hampstenfan’s initial post he has a neat chart that covers all six possibilities (indoors vs. outdoors, TR catalog workout, custom workout, non-workout/unstructured ride). Referring to unstructured/free rides, indoors and outdoors, @IvyAudrain wrote:

I hope that clarifies things for you.

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Occasionally, but it’s 90% just repeating the same information that doesn’t apply to me so I don’t listen too much anymore.

Well you’d have heard comments from Jonathan along the lines of I can’t remember the exact quote. But basically that when they had to turn it off he felt like he was back in the Stone Age.

There’s good reason for the FOMO because it’s been hyped up an awful lot.


I just thought it was marketing hyperbole.

I don’t need to be “clarified” I understand the content that is out there. You are not understanding my premise, if you did, you wouldn’t continue to repeat and link the same things. My original comment spoke to current state and you proceeded to tell me why it was not correct. My comment IS correct and reflects the machine learning as it is - and likely will be for some time. What you are interpreting as the future state intent per the “roadmap” is VERY unlikely to be correct. It would stand at odds with the tenets of structured training and periodization - things Chad has been explicit about. Intensity based work have very explicit ratios of working set to recovery. Even taking an extra 15 seconds of rest between sets changes the workout benefit. You’re no longer comparing apples to apples. Trying to retrofit free rides to glean a match to structured work is not going to be effective. Now if someone went out and did hill repeats with very consistent intervals, that might be usable and something TR should be able to fold in to AT very handily. Even Strava can detect intervals (laps) and populate a workout graph.

This does not indicate that adaptations will be made against these. It indicates that a level will be assigned based on work you have done. That is different. If I do a hard crit race, perhaps it will assign me an anaerobic level 5, just for the sake of argument. Great, that doesn’t mean my future workouts should be adapted based on that value. Maybe my current level is 6.5, and the race didn’t demand a 6.5, it demanded a 5 and not more. AT will have no idea if I was all out or doing precisely what I needed to do and no more. Maybe the reverse happened. Maybe my level was a 5 and I did a race at a 6.5. I took risks and burned every match I had and ended up shelled and dropped. Rode way beyond my capability. AT doesn’t know that either. It can try to make some inferences, but again, to my earlier point, without including an element of strain and understanding of intent, adaptations made based on free rides have a very high potential of missing the mark.

Structured training is structured training. Someone riding around 80% of the time without following a plan that expects AT to “figure it out” is not going to be satisfied with what they get. TR is a platform premised primarily around people following a structured plan. Train Now is what those folks should be using, not AT. The assumption that the machine learning will know anything about the intention of any rides not matched against workouts is charging TR with a fool’s errand, or the end user with substantial work after the fact to tell TR what the intent was.

This very statement - how exactly would one expect the platform to know a rider “crumbled” at 4 hours on a free ride? They could just have well intentionally changed their output. In order to incorporate that ride with any relevance to level evaluation, a rider would need to retroactively put the intended structure behind the ride so the machine learning would know the last hour was failure against intent vs intentional. Speaking to just energy utilization is precisely what I said in my earlier comments - specifically being reliant on the fitness/freshness model which I said they should and likely will need to incorporate.

They could absolutely get some usable data from unstructured work. peak power values can be ascertained from races, group rides and segments. Great, and? That’s not new. It also doesn’t tell you anything about repeatability which is fundamental to structured intensity work. If I held 150% FTP for 3 minutes during a race and hit a new peak PR, that doesn’t necessarily indicate that my workout levels should change. It’s about repeatability. A new peak power value does not intrinsically change how my working sets should be structured. It’s not even clear what that indicates in isolation. Could it be a change in my power curve in Z5+? Maybe. It can also be a leading indicator that my FTP has increased and raw wattages above threshold are being carried with it.
Suffice it to say, AT will never be what people are asking for it to be in this thread. To be clear, that is not an assault on the capabilities of TR, it’s asking them to solve for X while missing half of the equation. Even machine learning in conjunction with a coach can’t do what you’re suggesting here. Ideally the platform would be capable of combining multiple elements: Fitness/Fatigue, Power profiles/curves. pre-workout user feedback, and AT generated workout levels to make the appropriate adaptations. That’s some way off, but that is far more realistic, more complicated, but the more variables, the better machine learning can understand and suggested good recommendations. I don’t want AT to guess what I was doing on my group rides and mess with my plan based strictly on power output. I would however, like to see it understand that an unplanned hard ride (by IF or TSS) on a scheduled recovery day Wednesday could impact my adherence to my intervals on Thursday. It could ask me when I log in how recovered I feel and based on that response adapt my workout. That’s what a coach would do and is the mission of AT.


Nope, you aren’t.

Except that TR already has that running internally, so we are not talking about vaporware and I wouldn’t call it “future state intent”. It has progressed far more than that, it is real code that is being tested against real rides. It might be very hard to get it to production-level quality (a failure rate of, say, just 1 % will lead to a huge number of rides being classified incorrectly) and to work out edge cases, I don’t know the current state of the project.

You are conflating structured training with fitness assessment, I think: I can assess my fitness on unstructured rides or races no problem (without ML). The ML algorithms that rate unstructured rides only do a fitness assessment.

That is different from intent, which may or may not fit into the way TR structures training — unstructured rides are your responsibility and not all “unstructured rides” are devoid of clear training goals. Can an unstructured ride be unproductive for your training? Sure. But unstructured rides are your responsibility. But you don’t need to know intent to draw useful conclusions for training. And they may have benefits that are not visible in power numbers, e. g. practicing group riding on the road, descending or pacing over a certain distance, they all do not fit into the “percentage of FTP” mold.

For example, you wrote:

In that case I just expect AT to conclude that the ride or race was well within your capabilities, i. e. an easy ride for you. If, say, your ride was an 8.0, then it will use that data point to change your future workouts.

It is interesting that you mention chronic training load and fatigue. I’d add things like TSS and IF as well. These are nothing but an earlier attempt to quantify efforts based on power numbers by taking weighted averages of your rides. The weighting is arbitrary in the sense that they assume your capabilities decrease at a certain rate (and decay type) and your freshness increases at a certain rate. I think TR’s ML models have had much more systematic testing than that. If you think about it, it is an ancestor of what TrainerRoad is doing now with new technology.

The reason why many feel more at home with CTL, etc. is that they have more experience with it. We “know” that on certain occasions an IF > 1 is possible (e. g. on a short crit race) while on others it isn’t. We “know” that not all TSS are created equal and yet have used TSS to structure our training (e. g. to ramp up training intensity and then lower it during rest weeks). That’s just us being used to an older system that attempts to do the exact same thing.

That’s precisely what ML is about: you don’t need to know the equation and solve for X as you put it, because the methods are statistical. (In fact, there was a recent preprint by physicist Max Tegmark who did exactly that, searching for new phenomena with ML when you don’t know the fundamental laws.) You look for predictive factors and generate a model based on them. That’s why ML is hard even though the methods are rock solid: you need to carefully create that model and be sure that your training data set is representative, which is super hard. Otherwise garbage in, garbage out. There is a reason why it took TR >3 years despite having the largest data set of structured training rides in the world or why it was so hard to use ML algorithms to reconstruct images from the merger of two black holes. (The reasons I give examples from physics are not accidental, I’m a mathematical physicist, i. e. I am at the intersection of applied mathematics and theoretical physics.)

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You have no idea what they are running internally. You are assuming a mathematical model from very tenuous statements. I find it unlikely that based on the current algorithm used that outdoor free rides will fit the same model and adapt workouts applying the same weighted value for free rides as is applied to workouts for all the reasons I have already specified.

No, I am speaking directly to periodized training. You can enhance fitness with or without structure. Repeatability and recoverability are enhanced through a structured training model. It has nothing to do with just building FTP or power curves.

That’s not what I said. I said you need to know intent to determine the adaptations which is essentially determining which workouts are achievable, stretch, breakthrough or not-recommended. You absolutely need to know intent to put that kind of context around a workout.

This is short sighted. You don’t want it to conclude the ride or race was well within your capabilities or outside of it. You want it to know, If I hit an 8 in a race, and crumble into a million pieces and am garbage for 3 days, I sure as heck don’t want TR to adapt up my workouts. However, if I fail a workout at a 6.5 and give it feedback that it was all-out (or essentially didn’t select something like equipment failure to have it ignore the data point) I would want TR to effectively hold that data and “see what happens” on my next structured workout. If I fail again, it’s probably time to adjust down. I am clearly working through fatigue or some other concern. This applies logic to levels and adjustments. What you are suggesting they are doing is having TR try to turn unstructured work into some sort of workout on the back end. Your argument does precisely what you accused me of doing - conflating structured training with a fitness assessment. You can draw useful conclusions from unstructured work, the conclusions however, do not logically lead to an adjustment of levels.

Machine learning is not about turning a multivariate problem into a univariate solution. It is short-sighted to think that ML can use one dimension of training and extrapolate that to create a working model that flies in the face of decades of training data and science. AT as it stands does what it does very intelligently and in a way that makes sense from a training perspective - perhaps not the specialty of a mathematical physicist, your choice to include your credentials are at best an appeal to false authority, and in truth, poor form. Human physiology and training do not fit into a math model. The closest ML will be able to get to doing so is to include multiple variables i.e. strain, fitness, fatigue, FTP/Zone modeling, etc. Selecting the right workout against a plan based on adherence/attainment has immense value and TR has damn near nailed it in beta, a few small tweaks could really make this groundbreaking in the industry right out of the box. Follow the plan, the platform learns how competent you are in zones and can adjust intensity days around past performance to ensure that workouts are within the bounds of achievability. If a workout is a stretch or breakthrough it is labeled as such and the athlete can adjust preparation and expectations accordingly. I know unequivocally without even looking at the code that TR has not unlocked the secret sauce that coaches adept at reading power files down to the second in WKO+ have not. The machine learning can grab all the statistics in the world, it won’t know what to do with it unless a human being provides the boundaries (i.e. actual vs target and level evaluation frameworks) OR it is given multiple feedback variables.
Feel free to take the last word here, I have expressed my argument in detail and I understand yours. We are too far apart and continuing circular dialectic is not something I am interested in. As such, I won’t be returning to this conversation. Enjoy your day.

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Meanwhile I can’t get my progression levels to move from 1.0. In the beta for over 10 days and doing mostly structured workouts in the great outdoors. Even substituted a TR workout to try and coax an update levels. Patiently waiting for custom workouts on Garmin to move the level needle. I’ve been working with neural networks and ML for almost 30 years, starting under a couple Stanford PhDs. My proof of concept is still at y=0. Not worth debating the abstract at this point, I’m “from Missouri.”