What the Data Shows Before an Athlete Goes Quiet

We should say this first: we are not coaches. What we have done is build a system that reads every session a squad records and watches how each athlete's training rhythm changes over months. That gives us one narrow, honest thing to offer on the question of why athletes leave: what it looks like from the data side in the weeks before they do.

It does not give us the answer. The data sees the shape of leaving and nothing about the reason. So this piece describes the shape, is clear about where the picture stops, and ends with a question for the people who actually know.

Leaving is rarely a decision. It's a rhythm slipping.

Almost nobody sends a resignation. What happens instead is that the gap between sessions stretches a little past what is normal for that person. Then it stretches again. A week goes by with nothing. A fortnight. By the time it is obvious, the athlete has been gone for a while and the check-in feels late to both of you.

The important word in that paragraph is normal, and it has to mean normal for them. A generic rule like "flag anyone who hasn't trained in seven days" is wrong in both directions. For a runner who trains most days, seven days of silence is already a long time and the rule is late. For a swimmer who does one long session a week, seven days is Tuesday, and the rule cries wolf until the coach stops listening to it.

What actually works is measuring each athlete's own rhythm, the typical gap between their sessions over the last few months, and reading every quiet spell against that. Done that way, a weekly swimmer reads as quiet from about day nine. A daily runner reads as quiet from day three. Same rule, different clocks, because the clocks belong to the athletes.

That is the first thing the data shows: the earliest visible sign is not a bad week. It is a person falling behind their own pattern.

The two kinds of gap

A gap on its own tells you very little. What tells you something is what was happening just before it.

The gap after a healthy rhythm. Sessions were landing at their usual spacing, form was cycling between stress and recovery the way it does when training is going well, and then it stopped. Nothing in the training itself explains the silence. This is the gap worth a message, because whatever caused it is not in the data.

The gap after a decline. Form had been falling across several consecutive sessions with no recovery between them, and then the athlete stopped. This may be a person who needed the rest and took it. It may be a person who dug themselves into a hole and is now avoiding the pool. The data cannot tell those apart, but it can tell you that the silence has a run-up, and that the conversation should probably start with how the last few weeks felt rather than with where they have been.

Same number of days. Two different conversations. A system that only counts days will hand you both with the same label.

The gap that isn't one

Two more things the squad-wide view shows that a single athlete's file does not.

Still training, just not this sport. An athlete who has dropped the swim but is still running three times a week has not left training. They may have left you. That distinction is invisible if you only look at the sport you coach, and it is one of the clearest signals in the data that the problem is about the coaching relationship rather than about motivation.

Load dropped, not stopped. Sessions are still landing but the training load has fallen by a third or more on the previous week, or the week has gone from four sessions to one. This is not silence, but it is often what precedes it. It is also often nothing: a taper, a work trip, a cold. The data can raise it. Only a person can read it.

What we don't know

Here is where the picture stops, and we would rather say so than fill the space.

We cannot see why. Injury, illness, a new job, a new baby, money, a falling-out, a better offer from another coach, or simply the season ending in their head before it ended on the calendar. Every one of those produces the same slipping rhythm, and the data has no way to separate them.

We also cannot see the relationship. Whether the last three messages went unanswered, whether the athlete felt heard after their bad race, whether the programme stopped fitting their life. That is the part of coaching that never fits in a database, and we are not going to pretend it does.

We have read the usual claims about why athletes leave their coaches. We are not repeating them here, because we have not verified any of them and neither, as far as we can tell, has anyone else. What we have is the shape above. We think it is worth knowing. We do not think it is the whole story.

A question for coaches

So, genuinely: what do you see in the fortnight before someone leaves that the data would never catch?

A change in tone in their messages. Fewer questions. A race they stopped mentioning. Something in how they describe a session that has nothing to do with the numbers in it. If you have coached for any length of time you have a list, and it is a better list than ours.

If you are willing to share it, write to us at coaching@getsimma.com. We read every reply, and if enough coaches answer we will write up what we hear, with names left out and nothing added. We would rather publish what coaches know than what we guessed.

Simma reads every session your squad records and watches each athlete's rhythm against their own history, so the quiet ones surface before the check-in is late. It cannot tell you why. It can make sure you're the one who asks. Create your coaching account. If you're in the middle of the capacity squeeze this piece grew out of, how many athletes one coach can realistically manage is the other half of the argument.