The mathematics Of Beauty [Dating]

Today, we will be able to investigate female attractiveness, but we won’t be doing the same old photo analysis stuff. Instead, we’ll look past a girl’s picture, into the reaction she creates inside the reptile mind of the human male.

Among the remarkable things we’ll show:

that the more men as group disagree about a girl’s looks, the more they become liking her
guys are inclined to ignore girls who are merely cute
and, actually, having some men think she’s ugly actually works in woman’s favor

Fair warning: we’re about to objectify women, big-time. My job is to research OkCupid’s data, and without a little of objectification that’s impossible. Men will get their turn under the microscope soon enough. As usual, none of this (apart from the celebrity examples) is my opinion. All data is collected from actual user activity.

Let’s start before everything.

All people, but especially guys, spend a disproportionate amount of energy attempting to find, browsing, and messaging our hottest users. As I’ve noted before, a hot woman receives roughly 4× the messages a median-looking woman gets, and 25× as many as an unsightly one. Getting swamped with messages drives users, especially women, away. So we now have to investigate and redirect this tendency, lest OkCupid become sausageparty.com .

Every so often we run diagnostic plots like the only below, showing what number messages a sampling of 5,000 women, sorted by attractiveness, received during the last month.

These graphs are adjusted for race, location, age, profile completeness, login activity, etc-the best meaningful difference between the folk plotted is their looks. After running a number of these, we began to ask ourselves: what else accounts for the wide spread of the x’s, particularly on the ” above-average” half of the graph? Is it just randomness?

What is it about her:

that gets more attention than her:

…though consistent with our users, they’re both good-looking?

Not all 7s are an analogous

It turns out that the 1st step to understanding this phenomenon is to head deeper into the mathematically other ways you’ll be attractive.

For example, using the classic 10-point ‘looks’ scale, let’s say somebody’s a 7. It can be that everyone who sees her thinks exactly that: she’s pretty cute.

But something extreme like this might just as easily be occurring:

If all we know is that she is a 7, there’s no option to tell. Maybe for some guys our hypothetical woman is the cat’s pajamas and for anything else she’s the cat Garfield. Who knows?

As it turns out, this distribution of opinions is vital.

Celebrity photos: to titillate and inform

Let’s check out what the ratings distribution could possibly be for a couple famous people. I imagine that for, say, the actress Kristen Bell it might be roughly like this:

Ms. Bell is universally considered good-looking, however’s unlike she’s a supermodel or anything. She would probably get a number of votes inside the ‘super hot’ range, lots around ‘very attractive’, and almost none at the ‘unattractive’ end of the graph.

Compare her to Megan Fox, who might rate like this:

On the far right, you’ve got the numerous dudes who think she’s the sexiest thing ever. On the far left, you will have the small number of folk who have seen her movies.

Unlike Ms. Bell, Ms. Fox produces a powerful reaction, even though it’s sometimes negative.

Real People

Now let’s look back at the two real users from before, this time with their own graphs. OkCupid uses a 1 to 5 star system for rating people, so anything else of our discussion shall be in those terms. All of the users pictured were generous and confident enough to allow us to dissect their experience on our site, and we appreciate it. Okay, so we’ve got:

As you’ll discover, though the common attractiveness for the two women above is extremely close, their vote patterns differ. On the left you’ve consensus, and on the appropriate you’ve got split opinion.

To put a good point on it:

When we began pairing other folks of similar looks and profiles, but different message outcomes, this pattern presented itself repeatedly. The less-messaged woman was usually considered consistently attractive, while the more-messaged woman often created variation in male opinion. Listed below are a couple more examples:

We felt like were on to something, so, being math nerds, we put on sweatpants. Then we did some work.

Our first result was to match the conventional deviation of a girl’s votes to the messages she gets. We found that the more men disagree about a girl’s looks, the more they like her. I’ve plotted the deviation vs. messages curve below, again including some examples.

The women along the graph are near the 80th percentile in overall attractiveness. You possibly can click the tiny thumbnails to expand them.

As you’ll find, a lady gets a more robust response from men as men become less consistent in their opinions of her.

Our next step was to investigate a girl’s actual vote pattern of 1s, 2s, 3s, 4s, and 5s:

This required slightly more math and is harder to elucidate with an easy line-chart. Basically, we derived a formula to predict the volume of attention a lady gets, based on the curve of her votes. With this we will translate what guys reflect on a lady’s looks into how much attention she actually gets.

The equation we arrived at might look opaque, but after we get into it, we’ll see it says some funny things about guys and how they decide which women to hit on.

The most crucial thing to realize is that the ms are the boys who make up her graph, like so:

And the terms with positive numbers in front contribute to messaging, the ones with negative numbers subtract from it. Here’s what this formula is telling us:

The more men who say you’re hot, the more messages you get.

How we know this-the .9 in front of m5 is the largest positive number, meaning that the blokes who think you’re amazing are the strongest contributor for your messaging income. Even besides our equation, we know this because it’s obvious. The indisputable fact that excited guys equals male attention is as simple and as natural as a boner.

Similarly, the m2 term, the fellows who think you’re ” meh” , hurts your messaging total. That’s also to be expected. The opposite two terms, however, have something rather more surprising to tell us.

Men who think you’re cute actually subtract from your message count.

How we know this-because the .1 coefficient in front of m4 is negative. This tells us that guys supplying you with a ’4′, who are actually rating you above average-looking, are putting off from the messages you get. If truth be told, after you combine this with the positive number in front of the m1 term, our formula dictates that, statistically speaking:

This is an attractive crazy result, but anytime we ran the numbers-changing the restrictions, trying different data samples, and so forth-it came back to stare us inside the face. In plain scientific terms, it was like a child we were seeking to drown had somehow grown gills. (This happens forever in China.)

What We expect Is happening

So here’s our paradox: when some men think you’re ugly, other men are likely to message you. And when some men think you’re cute, other men become less interested. Why would this happen? Perhaps a bit game theory can explain:

Suppose you’re a man who’s really into someone. Once you suspect other men are uninterested, it means less competition. You therefore have an added incentive to send a message. You may start thinking: maybe she’s lonely. . . maybe she’s just waiting to search out a guy who appreciates her. . . at the least I won’t wander off inside the crowd. . . maybe these small thoughts, plus the undeniable fact that you actually think she’s hot, prod you to action. You send her the peerlessly crafted opening message.

On the opposite hand, a girl with a preponderance of ’4′ votes, someone conventionally cute, but not totally hot, might seem like more in-demand than she actually is. To the standard man considering her, she’s obviously attractive enough to create the impression that other guys are into her, too. But maybe she’s not hot enough for him to throw caution (and grammar) to the wind and send her a message. It’s the curse of being cute.

The overall picture looks something like this:

Finally: What This would Mean To You

I don’t assume every woman cares if guys notice her or not, but while you do, what does all of the above analysis mean in practical terms?

Well, fundamentally, it’s hard to modify your overall attractiveness (the large single number we were talking about at the start). However, the variance you create is under your control, and it’s simple to maximize:

As you’ve probably already noticed, women with tattoos and piercings seem to have an intuitive grasp of this principle. They sing their own praises what makes them different, and who cares if some people don’t find it irresistible. And they get a number of attention from men.

But our advice can apply to anyone. Browsing OkCupid, I see so many photos which can be clearly designed to attenuate some supposedly unattractive trait-the close-cropped picture of an individual who’s probably overweight is the classic example. We have now mathematical evidence that minimizing your ” flaws” is the other of what that you must do. When you’re a touch chubby, play it up. When you’ve got on oversize nose, play it up. When you’ve got a unusual snaggletooth, play it up: statistically, the fellows who don’t love it can only can help you, and the ones who do find it irresistible might be the entire more excited.

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This post is tagged: attractiveness, diagnostic plots, login activity, meaningful difference, number messages, objectification, photo analysis