Triple F Loading System

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Loading matrix

Tier

Level

Prescription

Program Cell

Set plan Your version — starts from the suggestion, edit and save.

Program notes

Framework

Model tuning

Play with the numbers here while you and Rich figure them out — the matrix, suggestions, and reference charts update instantly. Changes stay on this computer until you reset.

Today's Prescription

13 year old female, 123 lb

Foundation 13 to 14 Standard barbell bench Level 1

Set	Load	Reps	INOL	Intent

Total reps45

Total tonnage1,231 lb

Main work37 lb

INOL dose1.48

MAV

Draft difficulty band from the strength dose calculator thresholds.

Exercise Library

Authoring queue

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Review

Saved Program Cells

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Exercise Tier Overridden only

Athlete reference chart

Bench press, Foundation

Print this for the weight room or hand it to athletes — read down to the weight class, across to the tier, to find the working load. These are the same numbers that will push into the athlete app once that connection is built.

Tier Level Chart Pick a tier and level — each chart (Female / Male) reads weight class down, age class across. Exercise and variant come from the bar up top.

Parameters

What we're programming and why

A deterministic loading library for Triple F youth athletes. Coaches author the loading rules once — per exercise, tier, level, and athlete context — and those rules feed the athlete app. Same inputs always produce the same prescription; the engine runs without AI.

The five tiers

Foundation — Quality. Own the pattern with clean, repeatable reps.
Fit — Volume. Build work capacity with higher reps.
Fortified — Load. Heavier, lower-rep strength.
Fast — Power. Intentionally hard — speed under real load, not a light exposure.
Freak — Mixed. Combined volume, load, and speed for advanced athletes.

Five levels (1–5) per tier. An athlete advances a level by demonstrating the load and skill, then earns the next tier.

How load is set

Suggested load = body weight × %BW coefficient × movement factor × gender factor, rounded to available equipment. Each pattern scales differently — a deadlift loads far heavier than a split squat. Female 13–14 is the baseline; other cohorts adjust from there. Gender factors are draft values Rich owns.

Dose — INOL

Difficulty = reps ÷ (100 − relative intensity %), summed across sets, placed in a band:

MV — Maintenance. Low stress; learning or deloading.
MEV — Minimum effective. Enough to adapt with clean execution.
MAV — Maximum adaptive. Productive hard work; watch recovery.
MRV — Maximum recoverable. High demand; needs coach review.

Athlete context

Every prescription is scoped by age class (10–12, 13–14, 15–16, 17–19), weight class (seven buckets, 70–350 lb), and gender cohort. Outside training load isn't controlled here — coaches adjust on the floor and log what actually happened.

Principles we don't undo

The engine stays deterministic — it works without AI.
The system suggestion stays visible even when a coach edits the load.
A coach edit changes that session's prescription, not the rule.
Rich owns the production rule values; changes go through review.
No real athlete names, birthdays, or records live in this tool or in GitHub.

Developer · rule object (JSON)