Apply the Drake Equation to estimate how many compatible partners exist in your area. A fun, mathy approach to dating probability.
In 1961, astronomer Frank Drake created an equation to estimate the number of detectable extraterrestrial civilizations. Economist Peter Backus famously applied the same framework to dating, asking "Why I Don't Have a Girlfriend" — and found only 26 women in London met all his criteria.
This calculator adapts Drake's approach to romance: starting with the total population in your area, then sequentially filtering by age range, gender preference, single status, educational compatibility, physical attraction, mutual attraction, and general compatibility ("getting along"). Each filter dramatically shrinks the pool.
The results are deliberately eye-opening. What seems like a city of millions often yields surprisingly few compatible matches — sometimes dozens or even single digits. But the real value is seeing which filters matter most. Relaxing just one criterion (age range, for example) can multiply your dating pool dramatically. It's a mathematical nudge toward keeping an open mind. It also helps separate realistic constraints from preferences that may be doing most of the filtering.
It is equal parts fun and insightful. The Drake Equation for Love shows how selective criteria compound to shrink your dating pool and which compromises would increase it most.
It is useful because it makes the tradeoffs visible instead of abstract. You can see which assumption has the biggest impact, which turns a vague dating question into a concrete sensitivity analysis.
N = P × fGender × fAge × fSingle × fAttraction × fMutual × fCompatibility. Where P = city population, and each f is a fraction (0-1) representing the proportion passing that filter.
Result: 50 compatible people
1,000,000 × 50% × 20% × 50% × 10% × 5% × 10% = 50 potential matches. That's 0.005% of the city — you'd need to meet 20,000 people to find one!
Frank Drake's original equation estimates the number of civilizations in the Milky Way: N = R* × fp × ne × fl × fi × fc × L. Each factor filters a smaller subset, from star formation rates to the fraction that develop technology. The love version mirrors this cascade of increasingly specific filters.
If you live in a city of 500,000 and apply six filters each at 50%, your pool drops to 500,000 × 0.5^6 = 7,812. But if just one filter is 20% instead of 50%, the pool shrinks to 3,125 — a 60% reduction from one stricter criterion. This exponential sensitivity is why flexibility matters so much in dating.
Peter Backus (2010) applied this to London and found 26 potential girlfriends. Randall Munroe (xkcd) applied similar logic to finding ideal romantic partners. Economist Tim Harford also explored this framework, concluding that the mathematical optimal dating strategy involves meeting about 37% of your options before committing (the "secretary problem").
It's a Fermi estimation — a structured way to make educated guesses about unknowable quantities. The individual percentages are subjective, but the multiplicative framework is sound. It's meant to be insightful and fun, not precise.
That's the key insight! Each filter compounds. Five filters at 50% each leave just 3.1% of the population. The lesson: being slightly more flexible on any one criterion dramatically increases your pool.
Studies suggest people find 10-20% of the relevant population physically attractive at first glance. But attraction grows with familiarity, so this number may be conservative for longer interactions.
Not directly. Apps expand your reach beyond your natural social circle, effectively increasing the "population" you're exposed to. You could use your app's user base as the starting number instead.
Using London's population of 4 million, after filtering by gender, age (24-34), degree holders, attraction (5%), and mutual attraction (5%), he calculated about 26 women. His paper was called "Why I Don't Have a Girlfriend." He later married.
Mathematically: widen any filter (age range, type preferences), move to a bigger city, or increase the number of people you meet. The model shows that broadening your criteria even slightly has a multiplicative effect.