How rare are your standards?
Pick your minimum — looks, age, height, income — and find out how many single people actually clear it.
Watch the background while you drag.
At 1, nobody misses. There is no first no.
AI composites, not real people. Your taste is allowed to disagree.
You bring the standards. They bring the haystack.
Where the numbers come from
Everything here runs on a fixed simulation of 60,000 people of the gender you picked, inside your age range. Each simulated person gets an age, a relationship status, a consensus attractiveness score, a height, an income, and an education level, drawn from published distributions and correlated the way those traits correlate in real life (better-looking people earn somewhat more, income tracks education, and so on). Your filters then eliminate people one stage at a time. Same seed every time, so the same settings always give the same answer. The faces in the background sample that simulation: portraits dim as your filters cut people. The thinning is deliberately non-linear — at harsh settings survivors are over-sampled so the last few stay visible — but every face still lit is one that clears your filters. The number is exact; the wall is a mood.
Scores are holistic — face, grooming, and style together, which is also how the example portraits are drawn. Pick "either" and the pool blends men and women 50/50, each rated on their own curve.
The looks curves are the important part, and they are not symmetric. When men rate women's attractiveness, the ratings form a roughly normal bell curve centered near the middle. When women rate men, the curve shifts hard to the left: in OKCupid's dataset, women rated about 80% of men as below-average looking. We model both curves accordingly, which is why "a minimum of 7" costs dramatically more when you're shopping for men than for women.
| Minimum score | Top % of women (as men rate them) | Top % of men (as women rate them) |
|---|
| Source | Used for |
|---|---|
| OKCupid data blog, 2009 | The two rating curves; the 80%-below-average finding |
| Pew Research Center, 2022 | Share of people single by age and gender (63% of men under 30; 34% of women) |
| US Census / ACS, 2023 | Population by age; individual income; education levels |
| CDC NHANES | Male height distribution (mean 5'9", SD about 3") |
The portraits, including the background crowd, are AI-generated composites drawn to stand roughly where consensus ratings put each score. No real person appears on this page, and reasonable people will disagree with any given face's placement — that disagreement is itself the point of the caveat below.
Honest caveats: these are consensus scores, and raters disagree with each other almost as much as they agree — the pool of people you'd personally find attractive is bigger than any consensus cutoff. Income and education filters use national shares adjusted for age, not your city. Singleness is treated as independent of looks, which is generous to high bars. None of this measures charm, humor, or whether they text back.