Doing the Math on Online Dating

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I’ve recently found myself single for the first time in almost a decade (I’ll skip the back story if it’s all the same to you), and it’s taken some getting used to.  There’s good and bad both but, on reflection, I don’t think I want to stay single long term.   The bad news is that the percent of the population that is unmarried and has no children starts shrinking rapidly at age 26 and I’m already 7 years past that.  I’d like to believe that somewhere on the edge of the bell curve is the woman for me, but that still leaves the question of where and how to meet her. My best bet? Online dating. Here’s why.

Let’s look at the numbers

A quick look at google provides the baseline number – 7.15 million people in the Bay Area.   I don’t want to drive all the way to San Jose though and I’m not interested in people who prefer suburbs so if I look at just the urban inner Bay Area (San Francisco, Oakland, Berkeley, El Cerrito, Emmeryville, Albany, Alameda) I’m left with 825,863 + 400,740 + 115,403 + 24,048 +10,335+18,969 + 75,641 or 1,470,999 people. Sounds like a huge dating pool, right?

It’s easy, given such a large pool, to believe that if a given relationship doesn’t work out someone else will come along, and a lot of people seem to fall into that sort of thinking. The trouble is, 1.5 million people living in an area doesn’t actually translate to 1.5 million options. Let me explain.

If the San Francisco Bay Area (where I live) conforms to national averages, 39.8% of people here are in their 20’s or 30’s and 53.4% are either never married or divorced.  Out of those, approximately half will be female and 4 percent will have an IQ that’s at least in the same general ballpark as mine (IQ is a deeply flawed metric for a long list of reasons, but it’ll have to suffice in the absence of a better one).  NPR says 50% of americans are overweight and 15% are obese (obese being a subset of overweight). I don’t mind a few extra pounds – I’m not as skinny as I used to be though I’m working hard on that right now – but there’s a fine line there.  I’ll take a shot in the dark and say 40% of people are too heavy for my taste.  A further  20% are too short according to the census data – I’m 6’2″ so anyone under 5’2″ is out.  So that’s the first round of eliminations.

I wasn’t able to find good numbers on the number of unmarried people who are involved in a committed relationship (ie not actually single) but from my own social circles I’d say it’s probably north of 70%.  Based on my preliminary investigations on dating sites, many more in the Bay Area are involved in or prefer polyamorous relationships, removing them from consideration.  I’ll ballpark the total between those groups at around 80%, a massive reduction.   The data also shows that highly intelligent women are far less likely to want children,  so I’ll take a whopping 70% cut since having kids is on my bucket list.   I also need to narrow the pool based on political inclination.  Smart people statistically lean liberal, but I’m well to the left of liberal so I’ll take another 50% reduction.

Wikipedia, which is clearly super credible, says that 15.4% of people in the SF Bay area are Lesbian, Gay, or Bisexual; but that number will be higher in the cities I’ve chosen because San Francisco and Oakland’s large LGBTQ communities skew the numbers. Unfortunately I can’t find good numbers, so I’ll ballpark it and guess maybe 10% of women in the sf bay area are probably not interested in dudes.

I don’t have any statistics on physical attractiveness and honestly I don’t really care about looks that much so I’m not including a number for that. And of course most women won’t be interested in a gigantic nerd who writes articles like this, though within my tiny subset of people who knows how big a cut that would be. I’ll be wildly optimistic and leave it out of my equation.

So my final formula is 1,470,999  * 0.398 * 0.534 * 0.5 * 0.04 * 0.6 * 0.8 * 0.2 * 0.3 * 0.5 * 0.9 =  81.0348

So where does that leave me?

All in, that leaves somewhere in the neighborhood of 81 people who made my criteria for things I can easily find or guestimate numbers for.  Other calculations are harder. For instance highly intelligent people skew towards Atheism, which works in my favor since I’m not interested in religious people.  Unfortunately, I don’t have good figures on how heavy that skew is.  I also don’t have any way to accurately estimate the percentage of my remaining population who are doing something with their lives.  I can’t just use income since for me a person who works at a nonprofit or does advocacy work gets a thumbs up even though they’re likely underpaid, while an investment banker or corporate lawyers gets a thumbs down.  Add to that the massive uncertainty of having had to ballpark so many numbers and there’s a lot of room for error.  This is doubly true because the Bay Area leans left, has a lower obesity rate than many other urban areas, and has an unusual number of highly intelligent people who came here to work in tech so it’s probable that I’ve eliminated too many people by using national statistics that aren’t locally accurate. So my actual pool may be significantly larger than I’ve calculated.  Even doubling though only gives me a pool of 162.

Without better data I simply don’t have a good way to make a more accurate estimate. This estimate is really just a series of wild guesses strung together mostly for laughs. But if it’s even directionally correct, my total pool is probably somewhere between 60 and 160 out of a population of almost 1.5 million.

In other words, the odds of meeting a woman who fits even the most basic criteria at a bar are dismal. So online dating is my only option – and I can’t afford to filter out people who otherwise match my criteria. On the flip side, if I’m looking for a partner and not just to get laid, I need to restrict my dating to people with a similar mind set. I don’t have infinite time or money (and let’s be real, dating is expensive as a guy) to waste and I’m not getting any younger.

Next steps

Given all of that, I’m going to skip irrelevant matching questions on dating sites about trivial topics since they just muddy the water.  Tastes in music and favorite movies are poor indicators, for example. 

And then there’s the task of optimizing my own profile.  After I shared the first draft of this, a friend pointed me to a Ted Talk by Amy Web about hacking online dating. Her approach was a bit more …. aggressive than mine. But! She makes some good points. As Webb points out in her video, smart people tend to write more in their profiles, but the best profiles are concise and easy to parse through.  That means I need to edit relentlessly.  Initial research into what women in my target group are looking for indicates that being tall, white, college-educated, and intelligent works in my favor, but being a little older than I’d like to be does not.  No surprises there.  Other factors like my motorcycle and tattoos may actually hurt me, contrary to what the popular wisdom would suggest.  And being divorced definitely hurts me, not because of an objection to divorce on principle, but because people assume it means I have kids and have to pay alimony.  Since neither of those is the case, I’m better off leaving the divorce off my profile and only mentioning it when past relationships come up in conversation.

The next step if I followed Webb’s model would be to build a script that can identify popular men in my age cohort and scrape their profiles to look for keywords and keyword density. Honestly, that just sounds creepy and I’m not going to do it.  There’s a lot of additional room for optimization as well, but I don’t know how far down that road I’ll go…  Romance is hard to quantify.

Aside from the lols, doing this exercise was interesting on a number of levels because it meant thinking through what I actually want in a partner and how valuable each of my requirements is since every item on the list shrinks the pool of options. That’s the real point of all this for me. After all, if you don’t know what you’re looking for, it’s hard to know when you’ve found it.

What would your equation look like?

Update: I shared the TED talk and post with Kelly Clay, a friend in Seattle who’s been frustrated with dating there. She was inspired to do her own post using the same basic equation. I thought it was amusing and it proves that the work is replicable, so that’s cool.

Update 2: I found my 1 in 1.5 million! Here’s what I found along the way: The do’s and Don’ts of Online dating, from a Newlywed.






One response to “Doing the Math on Online Dating”

  1. […] and then met the love of her life. (This blog was also inspired by my coworker, who took to task writing up the same algorithm based on data in San […]