Evidence · How we know what we know
What we know.
And what we won’t claim.
Every training app makes claims. Some can be checked. Most can’t. This page is our list of both: what our data supports, what we tested and couldn’t back up, and the numbers behind each one, so you can judge for yourself.
127,730
rides tested
552
riders
6
claims we make
7
claims we refuse
By Jim Camut · Former pro and Johan Bruyneel Cycling Academy racer · Updated September 11, 2026
Why we show you a range, not a number
Same rider, same power, twelve weeks.
The dots are what a heart-rate monitor reports ride to ride. The line is what fitness actually did. The change you care about is smaller than the noise around it.
Drawn to the measured numbers: a 6.8 bpm ride-to-ride swing around a real effect of one to two beats. The swing was measured across 127,730 rides from 552 riders, then repeated on a second group with the same result.
How we test our own claims
None of this is exotic. All of it is unusual in training apps.
- 01
Predict first. Then look.
We write down what we expect to find before we run a test, and lock the file. It's easy to find a pattern in ride data after the fact. It's hard to find one you called in advance.
- 02
Beat doing nothing.
Every result has to outperform the dumbest alternative, like drawing a straight line through your recent numbers. A surprising number of training claims don't.
- 03
Audit the wins, too.
We re-check the results we liked, not just the ones we didn't. That review overturned three conclusions we had already reached, one of them days from publishing. Nothing on this page skipped it.
What we’re willing to tell you
Six things that survived a test
we set up in advance.
Each one was written down as a prediction, then checked against ride data. Here’s what held, and what it rests on.
Measured
What a break actually costs
Extra beats per minute at the same power when you come back, by how long you were away. The steps are small, and they are real.
449 riders, 1,461 separate breaks. Riders who never took a break were run through the same test as a check, and showed no step at all. That is how we know the step comes from the break, not from our method.
Measured
About one rider in four
Most riders barely move after a break. About 27 in 100 drop a lot further.
380riders with three or more breaks each. We can’t tell which group you’re in from a form, so the coach measures you instead of assuming.
We'll tell you
01Take a break and your aerobic fitness dips. It comes back.
After ten days or more off the bike, your heart works a little harder at the same power. Longer breaks cost more: a couple of weeks is a small dip, a month is a bigger one. It fades back to normal after about three weeks of riding.
Based on 449 riders and 1,461 separate breaks, in a public dataset anyone can download.
We'll tell you
02About one rider in four loses noticeably more than the rest.
Most riders barely move after a break. Roughly a quarter drop much further. We can't tell which one you are from a form, so the coach measures you instead of assuming.
Based on 380 riders with at least three breaks each.
We'll tell you
03Your heart rate bounces around a lot more than your fitness does.
Ride to ride, at the same power, heart rate swings by several beats for reasons that have nothing to do with training: sleep, heat, caffeine, stress, how the day went. Real fitness change is smaller than that swing. It's the single most important fact about reading your own data, and it's why we show you a range instead of a number that jumps around.
Based on 127,730 rides from 552 riders. Measured twice, on two separate groups, with the same answer.
We'll tell you
04“Nothing changed” is a real answer, and sometimes the right one.
A steady rider under steady training holds steady. When we can see that clearly, we say so. We won't invent movement to make a screen look busy.
Based on Six months of one rider's data where the honest read was: no change.
We'll tell you
05Weeks of consistent riding pay off about a month later.
More training now shows up as an easier heart rate four to six weeks from now. The effect is real, and it is small. One thing it does not say: it describes a pattern we can see looking back, not a promise about your next month.
Based on 117 scored weeks from one rider with several seasons of history.
We'll tell you
06Time off costs you faster than most riders expect.
Ten days is enough to show up in the data. Most people assume they have a couple of weeks of grace. They don't, and knowing that is usually the difference between a gentle return and an ugly one.
Based on Our own riders, measured across seven seasons. One of them is the honest exception: they don't detrain over their usual breaks.
What we won’t tell you
Seven things a training app could say to you.
We tested each one. None of them held.
Several of these are on other apps’ home screens today. Here’s what happened when we tried to back them up with data.
We won't say
“Here's what your fitness will be next month.”
We built the forecast. Then we tested it against simply drawing a straight line through your recent numbers. It won about half the time. That's a coin flip, so we deleted it. Plenty of apps still show one.
Our forecast won49A coin flip50riders out of 100 · full bar = 100We won't say
“We've learned your personal recovery timeline.”
It sounds great. It isn't measurable. We tried 20 different settings for how fast a rider absorbs training. For the typical rider, 14 of the 20 fit the data equally well. When that many answers look equally right, you don't have an answer. Published studies run into the same wall.
14 of 20 settings fit equally well
We won't say
“This block will add X% to your five-minute power.”
We can't give you a number here, in either direction. When we measured it, the answer changed depending on which weeks we compared, and riders who did no intervals at all showed similar moves. We'll tell you a block worked. We won't attach a percentage we can't stand behind.
We won't say
“Polarized training beats the way you're training now.”
We ran this one as a check on ourselves: if our methods were finding patterns that aren't there, this is where it would show up. It came back empty every time, at every size of dataset. Whether polarized training is better is a real debate. Our data doesn't settle it.
We won't say
“Your long rides built your durability.”
True as general physiology, and we'll happily explain it that way with a citation. But as a claim about your specific data, it didn't hold up: right direction for some riders, wrong direction for others. “Science says” and “your data shows” are different sentences.
We won't say
“A hard week hurt your recovery.”
We found this in an early look at a few months of data and got briefly excited. When we re-ran it across the rider's full history, it vanished completely. It was an artifact of where we happened to start looking.
We won't say
“It was the heat.”
Heat is real and we track it. But it explains only a small slice of why a heart rate reads high, and for some riders almost none. We'll mention it as one possibility. We won't hand it to you as the explanation for a bad day.
Everything above is about riders in general. This part is about you.
What the coach learns about you
A small set of numbers, all read from your rides.
Here’s how they get set, what stops them from over-reading thin data, and which changes need your OK before they touch the plan.
- 01
It keeps 15 numbers about you, and every one comes from your rides.
Not a questionnaire. Your repeatable power, how much training you actually absorb in a week, how many hard days suit you, how well you hold power deep into a long ride. They update once a week from the riding you did that week.
- 02
Statistics set the numbers. The AI only explains them.
Every learned value is computed by ordinary statistics, the same kind of math used to track a signal through noisy measurements. The AI never sets one and never adjusts one. It reads the finished numbers and tells you what they mean in plain words.
- 03
Thin data makes it say less, not more.
Each learned value needs a minimum amount of evidence before it gets used. Below that line it quietly falls back to a sensible starting point and tells you nothing personal, instead of dressing up a guess as your number.
- 04
Easing off is automatic. Asking more of you needs your OK.
If your rides say the plan is too much, it backs off on its own. If they say you could take more, that waits for you to approve it. Same rule for your FTP: it can rise by itself, but a drop is your call, and a number you typed in always beats one we estimated.
- 05
It records more than it acts on.
Every interval set you ride is logged with what it actually demanded of you. Today that feeds nothing at all. We collect it so that if we ever do learn something from it, we'll be learning from years of evidence rather than a hunch.
- 06
When something we built doesn't beat a plain number, we switch it off.
One of these learned values was tested head-to-head against simply using a sensible fixed number. The fixed number won, so the learned one is turned off. It survives in the code only so that older records still read correctly.
Your numbers
Updated weekly
Demonstrated weekly load
590TSS
Well-established21 build weeks at 590–675 TSS, none with a failed quality session.
Hard days per week
3days
Calibrated8 weeks where three quality sessions landed and a fourth did not.
Fatigue red line
Using the starting point
Still learning2 weeks of evidence so far. Using the starting point until there is more.
Eased on its own
Hard days per week 4 → 3 days
Backing off never waits.
Proposed · waiting for your OK
Raise critical power 265 → 272 W
A mock-up, built from the same parts as the settings page every athlete gets.
What this means for your training
Three things you’ll notice
in the first week.
Too small to read? It says so.
Missed the set? It checks the plan first.
Formulas do the math. The AI explains it.
See how that compares to the apps you already know, or read why a bad week is so hard to read from your own data.
Common questions
The questions this page usually raises.
Why would a training app publish what it can't do?
Because a claim you can check is worth more than a claim you can't. Every coaching app tells you what it knows. Almost none will tell you where the evidence runs out, which leaves you no way to judge the rest. We'd rather be the one you can verify.
Does AdaptCycling predict my future fitness?
No. We built a fitness forecast, tested it against simply drawing a straight line through a rider's recent numbers, and found it won about half the time. A coin flip isn't a forecast, so we removed it. We do show the training load planned ahead of you, which is arithmetic, not prediction.
Where do these numbers come from?
Two places. Our own athletes' ride histories, several seasons each, and the Golden Cheetah OpenData project, a public set of ride files anyone can download and re-analyze. We wrote down what we expected to find before we looked, so we couldn't quietly move the target afterward.
What happens when a result stops holding up?
We remove the claim and update this page. It has already happened: reviewing our own results overturned three conclusions we had reached earlier, including one we were close to publishing. That is the reason to trust the rest.
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