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Subscriber active since. Fair warning: we're about to objectify women, big-time. The whole purpose of this blog is to analyze OkCupid's data, and without a little bit of objectification that's impossible. Men will get their turn under the microscope soon enough. As usual, none of this with the exception of the celebrity examples is my opinion. All data is collected from actual user activity. Note: this study was originally posted on OkCupid's OkTrends and has been republished here with permission. All people, but especially guys, spend a disproportionate amount of energy searching for, browsing, and messaging our hottest users.

Getting swamped with messages drives users, especially women, away. So we have to analyze and redirect this tendency, lest OkCupid become sausageparty. Every so often we run diagnostic plots like the one here, showing how many messages a sampling of 5, women, sorted by attractiveness, received over the last month. These graphs are adjusted for race, location, age, profile completeness, activity, and so on—the only meaningful difference between the people plotted is their looks. After running a bunch of these, we began to ask ourselves: what else s for the wide spread of the x 's, particularly on the "above-average" half of the graph?

Is it just randomness? It turns out that the first step to understanding this phenomenon is to go deeper into the mathematically different ways you can be attractive. For example, using the classic point 'looks' scale, let's say a person's a 7. It could be that everyone who sees her thinks exactly that: she's pretty cute. If all we know is that she is a 7there's no way to tell.

Maybe for some guys our hypothetical woman is the cat's pajamas and for the rest she's the cat Garfield. Who knows? Let's look at what the ratings distribution might be for a couple famous people. I imagine that for, say, the actress Kristen Bell it would be roughly like this on the left. Bell is universally considered good-looking, but it's not like she's a supermodel or anything.

She would probably get a few votes in the 'super hot' range, lots around 'very attractive', and almost none at the 'unattractive' end of the graph. On the far right, you have the many dudes who think she's the sexiest thing ever. On the far left, you have the small of people who have seen her movies.

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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 the rest of our discussion will be in those terms. All the users pictured were generous and confident enough to allow us to dissect their experience on our site, and we appreciate it.

They're pictured here on the left. As you can see, though the average attractiveness for the two women above is very close, their vote patterns differ. On the left you have consensus, and on the right you have split opinion.

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When we began pairing other people of similar looks and profiles, but different message outcomes, this pattern presented itself again and again. The less-messaged woman was usually considered consistently attractivewhile the more-messaged woman often created variation in male opinion. Here are a couple more examples on the left. 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 compare the standard deviation of a woman's votes to the messages she gets. The more men disagree about a woman's looks, the more they like her.

We found that the more men disagree about a woman's looks, the more they like her. I've plotted the deviation vs.

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The women along the graph are near the 80 th percentile in overall attractiveness. As you can see, a woman gets a better response from men as men become less consistent in their opinions of her. Our next step was to analyze a woman's actual vote pattern of 1 s, 2 s, 3 s, 4 s, and 5 s:. This required a bit more math and is harder to explain with a simple line-chart. Basically, we derived a formula to predict the amount of attention a woman gets, based on the curve of her votes. With this we can translate what guys think of a woman's looks into how much attention she actually gets.

The equation we arrived at might look opaque, but when 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 important thing to understand is that the m s are the men voting on her looks, making up her graph, like so:. And those m s with positive s in front contribute to messaging; the ones with negative s subtract from it. Here's what this formula is telling us How we know this— because the.

This tells us that guys giving you a '4'who are actually rating you above average-looking, are taking away from the messages you get. Very surprising. In fact, when you combine this with the positive in front of the m 1 term, our formula says that, statistically speaking:. How we know this— the. This is certainly an expected result and gives us some indication our formula is making sense. This is a pretty crazy result, but every time we ran the s—changing the constraints, trying different data samples, and so on—it came back to stare us in the face.

In plain scientific terms, it was like a baby we were trying to drown had somehow grown gills. This happens all the time in China. So this is our paradox: when some men think you're ugly, other men are more likely to message you. And when some men think you're cute, other men become less interested.

Why would this happen? Perhaps a little game theory can explain:. Suppose you're a man who's really into someone. If you suspect other men are uninterestedit means less competition. You therefore have an added incentive to send a message.

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You might start thinking: maybe she's lonely. You send her the perfectly crafted opening message. On the other hand, a woman with a preponderance of '4' votes, someone conventionally cute, but not totally hot, might appear to be more in-demand than she actually is. To the typical man considering her, she's obviously attractive enough to create the impression that other guys are into her, too. But maybe she's hot enough for him to throw caution and grammar to the wind and send her a message.

It's the curse of being cute.

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I don't assume every woman cares if guys notice her or not, but if you do, what does all the above analysis mean in practical terms? Well, fundamentally, it's hard to change your overall attractiveness the big single we were talking about at the beginning. 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 show off what makes them different, and who cares if some people don't like it.

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And they get lots of attention from men. But our advice can apply to anyone. Browsing OkCupid, I see so many photos that are clearly deed to minimize some supposedly unattractive trait—the close-cropped picture of a person who's probably overweight is the classic example. We now have mathematical evidence that minimizing your "flaws" is the opposite of what you should do. If you're a little chubby, play it up. If you have a big nose, play it up. If you have a weird snaggletooth, play it up: statistically, the guys who don't like it can only help youand the ones who do like it will be all the more excited.

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Christian RudderOkTrends. It's better to be ugly than cute, statistics show. So, what is it about this girl That gets more attention than this girl? Not all 7s are the same. But something extreme like this could just as easily be going on:. Let's look at Kristen Bell's attractiveness. Let's examine Megan Fox. Now, let's look at everyday people again:.

Less-messaged women on dating sites are usually considered consistently attractive. Attractiveness can be broken down into a simple equation. The more men who say you're hot, the more messages you get. Men who think you're cute actually subtract from your message count. If someone doesn't think you're hot, the next best thing for them to think is that you're ugly. Here's why this is happening:.

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