Pool —Your number —

Step 1 · Your city

New York city · 25 mi estimate

Step 2 · About you

More about you — improves accuracy

Step 3 · Your standards

Your dating pool

—
Calculating how much of the local pool remains…
adult 🚺 match these measurable preferences
City-and-radius estimate before orientation, mutual attraction or app activity.
— retained— local adults
Not currently married · age 25–34

Your narrowing stack

applied sequentially

Step 4 · Your odds — will they swipe right?

New York · 90-day outlook

What’s your number?

—
people in this pool might swipe right on you

Will they swipe right? 3 quick questions

10%Your best guess — this is the biggest lever on your number.
How active are you on the apps?
Roughly what share of eligible people get shown your profile in 90 days. More activity = more eyeballs.
2%
—
possible first dates
Assumes you also like them. Treat this as a range, not a forecast.
Evidence & caveats
Income responds to the cityYour income band is converted to an approximate metro earnings percentile. Above $100k, the calculator fits a lognormal tail to each metro and sex’s observed ACS $50k/$75k/$100k shares; it is an extrapolation, not a published bracket. A conservative 0.7×–1.65× response curve is then applied for men; women’s curve stays nearly flat. Hitsch, Hortaçsu & Ariely
Swipe-right survey, not observed behaviorIn Tyson et al.’s 2016 Tinder survey in London, 59% of women said they liked 10% or fewer profiles, versus 9% of men. The sample was small and Tinder skewed casual, so it is only an illustrative bound for long-term dating. Tyson et al. paper
Match rate ≠ swipe-right rateThe same 2016 London Tinder study’s own male profiles matched with 0.6% of people they liked; its female profiles matched with 10.5%. Those are profile-specific match rates, not rates to apply to you. Study details
No reliable match → date rateA small 2019 survey of Norwegian students found about one in-person meeting per 57 Tinder matches. It measured any meeting—not dates or long-term intent—so the slider above stays your assumption. Study summary

How personalization works: the compatible pool now carries the same measured overlap correction used on the Pool size tab before the profile-response scenario begins. The page then applies transparent scenario adjustments for age-range proximity, metro-relative income rank, height, self-rated photo and prompt quality, and compatible lifestyle or family preferences. Body type, relationship status, children, family goals and alcohol use carry small labeled scenario weights; politics and religion add small local-context adjustments only where grounded. None are causal estimates. Race and ethnicity remain neutral by design. Income, app reach and response evidence are not a validated current-city prediction; use the editable weights and displayed range—not the midpoint alone.

Step 5 · City dating market

How your city stacks up

Your pool, your mutual odds, and your competition — in your city and the 74 others, ranked by expected mutual matches — with the dating-market tilt and each market’s short-vs-long lean (Date-onomics) baked in.

Dating pools in 75 U.S. cities

My Dating Number estimates how many single adults match your standards across 75 U.S. metros. Below are your personal best and toughest markets \u2014 tap any city to see why it ranks there, then load it to run your exact standards in that city. You can also browse popular metros or search all 75.

Where should you move for love? 🗺️Ranked for your profile

Best bets for you 📈

Tough markets for you 📉

Browse popular metrosTap a city to see the breakdown

Don't see your city? 🔍

How this works · data & honesty notes

🔍 How your pool is calculated

Match your standards
Might swipe right

The cliff between rows 2 and 3 is mostly exposure, not rejection — with Regular activity, only about 2.5% of compatible people are ever shown your profile.

🎲 How your odds are calculated 90-day outlook

Serious intent (61%) is seeded from the 2026 Singles in America survey (16% actively seeking a committed relationship + 45% who'd welcome one), and app reach starts at a 28% level that is reshaped by target age and sex from Pew's national table (2023 ever-used × 2020 current-activity) — the FAQ lists the evidence behind each, and you can change the level in step 4.

🎚️ The 5 biggest levers on your number

  1. 1
    📱 App activity

    Casual → power user is a 16× swing in who sees you. Nothing else you control moves the number more.

  2. 2
    💘 Spark

    The share who’d swipe right back — photos, prompts, profile. The slider runs 1–50% for a reason.

  3. 3
    📍 Metro + radius

    10×+ between the biggest and smallest metros. The pond sets the ceiling; the radius sets how much of it you fish.

  4. 4
    🎂 Age range

    The broadest demographic cut. Widening it is usually the fastest way to grow the pool.

  5. 5
    🎚️ Standards, stacked

    Every must-have multiplies. The model uses measured overlaps (religion→drinking, kids→childlessness) where they exist.

💆 Read it without spiraling

  • 📵 It’s the apps, not you. A small number means small reach — roughly 97 of 100 compatible people never see your profile. That’s distribution, not desirability.
  • 🎯 You need one person. The pool measures search efficiency, not your worth or your odds of something great.
  • 📐 It’s a model, not a headcount. Built from Census surveys — directional by design. Real humans are weirder than averages, in your favor as often as not.
  • 🔧 Small tweaks multiply. A few more years on the age range or +25 miles of radius often doubles the pool. Play with the sliders.
Data sources & caveats
  • People: ACS 2024 1-year PUMS microdata — a measured joint table of age × marital status × education × race × earnings × kids (minor child in the household) per metro and sex; headcounts use the published B01001 18+ base. Independent per-table multiplication is the fallback if a metro’s bundle can’t load.
  • Behavior: BRFSS SMART 2023 (drinking, smoking — per-metro, by sex), NSDUH (drugs), Pew (religion), metro 2024 vote shares (politics), Singles in America (intent).
  • Mechanics: Hitsch et al. (income effects), Tyson et al. (swipe selectivity), OkCupid/OkTrends 2009–2014 (reply-rate priors and age-desirability curves seeding the swipe-right default — blended 50% with a flat baseline, hetero-derived so the age curve is off for same-sex pairings); exposure tiers assumed, scaled by profile desirability.
  • Honesty: where joint data doesn’t exist, filters multiply independently — labeled in the app. Marijuana × other drugs combines SAMHSA’s past-year and past-month rates with an independence approximation over the mixed periods (labeled approximations). Date-onomics ratios are theory-informed proxies, not predictions.
Data notes & sources

Provenance: [MEASURED] directly observed in the cited source · [INFERRED] modeled, estimated, or a proxy (including coarser-geography fallbacks) · [ASSUMED] judgment call with no direct measurement.

  1. Census ACS 2024 1-year table-based summary files — B01001 age × sex, B03002 race/ethnicity, B15002 education by sex, and B20005 earnings by sex for all 75 metros. [MEASURED]
  2. Market ratios (City market): college-educated men per women 25+ for bachelor’s+ profiles, otherwise men per women 18–44 — the ratio that sets a market’s terms, per the Date-onomics argument (Jon Birger, building on Guttentag & Secord). Normalized to the 75-metro average and capped; a competition proxy, not a prediction. The short-vs-long lean: male-scarce markets skew casual, female-scarce markets skew toward commitment. [MEASURED ratios; INFERRED as competition proxy]
  3. 2020 Census PL 94-171 tract populations with TIGER/Line 2020 tract centroids (core-city centroids from TIGER/Line Places). Each radius population is the 2020 tract population within that radius of the core city, scaled to the Census 2024 metro total. [MEASURED tract geography; 2020 shape scaled to 2024 totals]
  4. CDC/NCHS 2021–2023 anthropometric reference — measured height percentiles, adults 20+. [MEASURED]
  5. CDC/NCHS 2017–2018 BMI data — age-adjusted adults 20+; BMI <25 is remainder-derived. [MEASURED; <25 band INFERRED as remainder]
  6. Census ACS 2024 S2001 example for San Francisco metro — published sex-specific earnings brackets stop at $100k+. The calculator’s $150k/$200k/$250k shares are metro-tail estimates fitted to each metro and sex’s observed ACS $50k/$75k/$100k survival shares. [MEASURED brackets; INFERRED tail]
  7. Census 2024 educational attainment — bachelor’s+ among adults 25+. [MEASURED]
  8. Census CPS ASEC 2024 Table MS-1 — marital status by sex among people 15+; “married” includes spouse present, spouse absent and separated. [MEASURED]
  9. CDC NSFG 2017–19 — ever had a biological child by sex among ages 15–49; this is not children currently at home. Used only as the fallback if a metro’s PUMS bundle can’t be loaded. [MEASURED]
  10. Pew 2023 survey of adults without children — 47% of childless adults 18–49 said they were unlikely ever to have children; no significant sex difference. “Likely” uses the 53% remainder, which includes nonresponse. [MEASURED survey; INFERRED intent mapping]
  11. Pew 2023–24 Religious Landscape Study — national religious profile used for all metros. [MEASURED nationally; INFERRED as metro]
  12. MIT Election Data + Science Lab county returns with verified metro aggregation — 2024 Democratic/Republican two-party presidential vote shares used as a geography-aware political-compatibility proxy in 36 metros [MEASURED]; the other 39 metros use their primary state’s certified 2024 two-party share (FEC official results) [INFERRED as metro proxy]. It is not a survey of party affiliation — vote share is the proxy construct [INFERRED]. Where neither exists, Pew 2026 NPORS national affiliation is the final fallback.
  13. CDC BRFSS SMART 2023 — per-metro cigarette smoking and past-30-day any-drinking rates, split by sex: 50 metros measured directly, 4 more pooled from their metropolitan divisions, Seattle from its Seattle-Bellevue-Kent division, and 20 metros from state estimates (Pennsylvania and Kentucky use 2022, which lacked 2023 data). Smoking is cigarettes only. Body type stays on measured NCHS BMI — BRFSS height/weight is self-reported and understates obesity. [MEASURED SMART metros; state fallbacks INFERRED]
  14. Pew Research Center religion and alcohol analysis — in its 2015 survey, 51% of Protestants and 60% of Catholics reported drinking in the past 30 days. Those observed conditional rates are rescaled from the national baseline to the metro’s sex-specific drinking rate when either supported religion is selected; unsupported faiths keep the metro rate. [MEASURED conditionals; rescaled to metro rate (see BRFSS note)]
  15. SAMHSA 2024 NSDUH — past-year marijuana and any-illicit-drug use for adults 18+, both sexes. [MEASURED]
  16. Race/ethnicity uses ACS 2024 MSA total-population shares as an adult approximation [INFERRED]. ACS metro earnings use earnings in the past 12 months as a share of adults 18+; 16–17-year-old earners in the numerator create a negligible universe mismatch [ASSUMED negligible]. Above $100k, a least-squares lognormal tail is fitted separately for every metro and sex; for men at $250k+ this yields about 13.5% in San Francisco, 1.9% in Cleveland and 1.9% in Memphis. Body-size labels map to CDC BMI bands in the methodology: below average = BMI <25, average = 25–29.9, above average = 30+. Religion, marijuana and drug figures are not sex-specific. The model does not estimate sexuality, app activity, mutual preferences or compatibility.
  17. Tyson et al., 2016 — Tinder study in London: the 0.6% male-profile and 10.5% female-profile figures are match rates among likes sent, not swipe-right rates; the survey portion supplies the self-reported like-rate context. [MEASURED]
  18. Hinge company-data interview, around 2017 — like distribution was more concentrated for men; it does not provide a swipe-right rate. [MEASURED company data]
  19. Bruch & Newman, 2018 — 2014 messaging data from four U.S. cities; attention-based desirability, not looks or swipe probability. [MEASURED]
  20. Grøntvedt et al., 2019 — small self-reported Norwegian student sample; meetings, not dates, and not a general conversion rate. [MEASURED]
  21. Hitsch, Hortaçsu & Ariely, 2006 working paper — observed first-contact outcomes among roughly 22,000 users in two U.S. cities. Men above $50k received 34%–151% more contacts as income rose; women’s outcomes were mostly flat. Used only as a clearly labeled sensitivity, not a current-city prediction. [MEASURED study; INFERRED as sensitivity]
  22. Match Group, 15th Annual Singles in America Study, September 2026 — with researchers from the Kinsey Institute at Indiana University; ~5,000 U.S. singles surveyed March–April 2026. 16% were actively seeking a committed relationship and 45% were not actively looking but would welcome a relationship if the right person came along (61% combined seed the serious-intent default). [MEASURED survey; INFERRED as long-term-intent seed]
  23. Pew Research Center, February 2023 — 30% of U.S. adults have ever used a dating site/app (53% ages 18–29, 37% 30–49, 20% 50–64, 13% 65+; 34% of men, 27% of women); Pew 2020 found 18% of ever-users were currently using. The funnel uses the current-activity tier (ever-used × 0.18), reshaped by age and sex. [MEASURED survey rates; the age×sex joint is modeled and the uniform recency discount INFERRED]

Build 2026.09.18 · 16:56 EDT

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