Your body is normal
Researchers measured thousands of women and sorted every body into nine shapes. What they found says something reassuring — and useful — about yours.
When researchers set out to study women's body shapes, they didn't start with opinions. They measured thousands of women and sorted every single body into one of nine shapes — a system called FFIT, built entirely from measurement data. It's the same system this app uses.
Here's what they found in Western samples: the single most common shape is Rectangle. [B] Nearly half of women. The straight-through shape that so much styling advice treats as a problem to solve turns out to be the ordinary one — the shape more women share than any other.
And the mix shifts by population. In one study, hourglass figures made up about 11% of US women but under 1% of Korean women. [B] There's no universal "normal" shape, because shape distributions are simply different across the world. Whatever you measure into, you're in good company — often far more than you'd guess.
What your shape is actually for
Your shape is the starting point for everything useful this app does. Knowing you're a Spoon rather than a Triangle changes which necklines balance you, where a waistband flatters you, which colors near your face work in your favour. [A]/[B] These are real, mechanical relationships — a horizontal line here does this, a darker value there does that — and the research behind them is the whole reason this app exists.
That's the point of sorting bodies into nine shapes: it makes the advice specific to you. Nine shapes means nine tuned sets of recommendations — nine different geometries, each with its own toolkit. Not one ideal with eight consolation prizes.
Why we measure instead of asking
One of the most consistent findings in this field: people's sense of their own shape often differs from what the tape measure says. [B] Our own founder measured as an Hourglass after years of being sure she was a Triangle. That's not a failure of self-awareness — it's just how perception works, for all of us.
It's also why this app asks for measurements instead of asking you to pick your shape from a lineup. The tape is kinder than the mirror, and a good deal more accurate.
How to read what we tell you
Throughout this app, our claims carry small evidence tags, so you always know how much weight to put on them:
- [A] — replicated across strong studies. Solid ground.
- [B] — real, but from a single study or a smaller sample.
- [C] — a well-reasoned framework, not yet directly tested.
- [D] — popular styling advice that the evidence doesn't back — which is why we don't build on it.
When a claim is [A], we lean on it with confidence. When the evidence is thinner, we'd rather hand you one recommendation we can stand behind than five that merely sound good. Fewer claims, better ones — each one tuned to your shape, and each one honest about how sure we are.
Sources
- Simmons, Istook & Devarajan (2004) — FFIT nine-shape classification system.
- Lee, Istook, Nam & Park (2007), International Journal of Clothing Science and Technology, and a Manchester (JFMM) cross-study prevalence compilation — shape distributions across US, Korean, and other populations.