A tracker that agrees with itself is not the same as one that is right

Devices reproduce their own numbers beautifully and can still be wrong about the athlete in the same direction every night.

Two wrists, two identical devices, two nights, near-identical output. Everyone in the room takes that as proof the thing works. It is proof of one property only, and it is the less interesting one.

Reliability means a device repeats itself. Validity means it repeats the truth. A watch estimating sleep from movement and pulse can be beautifully reliable and consistently wrong, because the error is built into the algorithm rather than sprinkled randomly over the readings. Lie still and read for forty minutes and most wrist devices will score you asleep. They will do it every night, at the same time, with the same confidence, and the consistency of that mistake is precisely why nobody catches it.

The direction of the bias is what matters for a recovery schedule. If a device systematically credits quiet wakefulness as sleep, then the athletes it flatters most are the ones who lie awake without thrashing, which is a large share of anxious travellers on a road trip. Those are the people you would most want the system to surface, and the system is reporting them at eight hours.

Now put that inside a decision. Session load gets adjusted using recovery scores. The scores are biased high for a specific subgroup, so that subgroup gets the heavier prescriptions, night after night, and the error is not diluted by averaging because it is not random. Averaging fixes noise. It does nothing whatsoever to bias, and monitoring systems are built by people who mostly think in terms of noise.

I would trade a good deal of precision for one week of honest comparison. Ask the squad to keep a two-line paper diary alongside the device: lights out, best guess at time asleep, times woken. Crude and subjective. But when the paper and the wrist disagree in the same direction for the same six players, you have learned more about your data than a year of dashboards will tell you.

The vendors do publish validation work, and much of it is genuine, but it is typically done on healthy adults sleeping at home on an ordinary schedule. Athletes on an eleven-hour flight with a night game behind them are outside that sample in every respect that would affect the algorithm.

A number that never wobbles is comfortable to look at. Comfort is not evidence, and a stable wrong answer is harder to notice than an unstable one.