How fitness projection works

get_fitness_projection answers questions like “where will my form be on race day if I keep ramping load?” It is tempting to read its output as a forecast. It is not one — it is a deterministic scenario model, and understanding that distinction is what keeps an AI assistant honest about it.

A scenario, not a forecast

A forecast claims what will happen. A scenario answers if this, then that: given a starting point and a set of assumptions, here is how the numbers move. icuvisor projects fitness with a closed deterministic_ctl_atl_tsb model — the same exponentially-weighted CTL/ATL/TSB maths intervals.icu itself uses — run forward from today.

It is deterministic on purpose. The same inputs always produce the same curve, so the assistant can explain why a projection looks the way it does. Free-form “physiology” models, where the model invents its own progression, are rejected: they would be unreproducible and impossible to audit.

Where the curve starts

The projection seeds CTL, ATL, and TSB from the athlete-local start_date returned by get_fitness. The starting point is your real, current fitness — not an estimate — so a projection is only ever as current as your logged data.

The assumptions are part of the answer

Because the result depends entirely on its assumptions, the tool reports them back to you. _meta.assumptions records the scenario it ran: horizon length, weekly ramp percentage, recovery-week cadence and load, the number of explicit planned loads supplied, and the CTL/ATL time constants. _meta.boundaries records the limits: the horizon is capped at 180 days, no hidden upstream periodization fields are read, and explicit planned_daily_loads replace the modelled ramp only on the dates they cover.

Treat those fields as the fine print of the projection. If a scenario assumed a 5%-per-week ramp and you would never train that way, the curve is answering a different question than the one you asked — change the assumption and run it again.

Reading the output

By default the tool returns only the summary. Set include_full: true to get the daily projected CTL/ATL/TSB curve — see Terse by default for when that opt-in is worth the extra tokens.