Why Men Get So Few Matches on Dating Apps

Two friends install a dating app and try their luck. The man tries to set up the perfect profile. A nice front picture.

A group picture to make it seem like he has friends A picture in Paris to make it seem like he’s cultured. And a picture climbing mountains to make it seem like he's adventurous. After some hard work, the profile is ready.

The woman doesn’t feel safe sharing a lot of personal information, so she chooses the first picture she can find and she's ready to go. They start swiping and hope for the best At the end of the day, when the woman checks her phone her like counter is full. Practically every profile she likes is an instant match.

Soon she’s overwhelmed by the amount of matches and messages on her inbox. For the man, it's a different story. So far, he has only received a couple of likes and has zero matches.

He becomes frustrated with the app and starts questioning his self image. He put so much effort into setting up a nice profile, afterall. Why can't he get any matches?

To answer to that question, we need to understand the numbers behind dating apps. I made a simulation of a dating app with 1000 dummies to try to understand why men get so few matches. Dating apps can paint a distorted picture of what the real dating world is like.

Some studies indicate that Dating apps can have a negative impact on self esteem, with a stronger effect on men. And women often have to find strategies to deal with intrusive behavior from men in these apps. It's hard to understand exactly what’s going on inside these apps because there is very little data available.

However, we can make educated guesses based on the little information we have. I'll start with an ideal unrealistic scenario and we'll see how quickly things change when I start adding real life variables A side note, we’re talking about dating with opposite genders. Same-gender dating in dating apps has very different dynamics and it's out of scope for this video.

Since we’re trying to make an ideal scenario, let's assume we have the same amount of men and women using the app. Let’s also assume that everyone sees 100 profiles per day and that every profile is treated equally by the algorithm. I’m going to assume that users like 1 out of 4 profiles they see.

This means every user has a probability of 25% of being liked every time their profile is shown. Some of these parameters aren't realistic, but that's ok. We’re going to start like this and make it more realistic as we go.

So, how many likes and matches will everyone get at the end of the day? Let's run the simulation. Ok, not too bad.

On average, both women and men get 25 likes and 6 matches per day. So, why does this look different from real dating apps? Let’s start with reason number 1: There are more male than female users I was able to find user gender data for Tinder and Bumble, two of the most popular dating apps in the world.

In both apps, there were significantly more men using the app then women. In our simulation I'm going to keep it simple and assume that we have 2 men for every woman, a ratio somewhere between Tinder's and Bumble's I ran the simulation again and the differences were big. I'm going give you a chance to pause the video in case you want to try to guess how many likes and matches users get.

We now start to see the first signs of a gender imbalance in the results. Because there are 2 men for every woman, the number of likes received by women doubled and the number of likes received by men halved. And when we look at the number of matches, something interesting happens.

Women received an average of 50 likes. Since they like 1 out of 4 profiles, you would expect them to get an average of 12 or 13 matches. However, they only get 6.

That's because now there so many male users that women don't even get a chance to see half of the users that liked them. They only see 100 profiles per day and there are just too many men in the queue. At this point it makes sense that women start to feel a bit overwhelmed by the amount of likes they receive.

In addition, because they often encounter intrusive behaviour from men that also makes them think carefully about who they give likes too. Men, on the other hand, are starting to get a bit desperate. Because they don’t get a lot of likes, they know they can’t be too picky and they start giving likes more generously to improve their chances of getting matches.

Which leads us to reason number 2: Men give more likes then women. According to this New York Times article from 2014, men are nearly three times as likely to like a profile then women on Tinder. So let's use those numbers.

I'm updating the simulation so that women and men give likes in 14% and 46% of cases, respectively. So how do you think this is going to change the results? Now the gender imbalance increases even further.

Women get an average of 92 likes whereas men only get 7. And because men like 46% of the users they see, these 7 likes result on an average of 3. 2 matches.

Women get twice as many, an average of 6. 4 matches per day. But things can get even more complicated for the average male user.

Attractiveness is subjetive, but the reality is that some profiles will be considered attractive by more users than others. Which brings us to reason number 3: A small share of the users get a big share of the likes In a Q&A post from 2017 on Hinge's official website, one of the engineers behind the dating app shared some data about this imbalance. He mentioned that certain people get exponentially more attention than others: He reported that about half the likes from men were given to about 25% of women and half the likes from women were given to only 15% of men.

This means that, especially with men, there's a small segment of users that get a large slice of the total likes. Let’s try to include that in our simulation. I’m giving every user a score from 0 to 100% that determines how attractive they are perceived by other users.

Until now, attractiveness has had no impact on the probability of getting likes. That means that every time a profile was shown, the probability of that profile being liked was 46 and 14% for women and men, respectively, regardless of their perceived attractiveness. I'm now looking for a new distribution such that the top users get exponentially more likes, but while making sure the average like percentages stay the same.

I went for the simplest formulas I could come up with, and you can find my assumptions on the screen in case you’re interested. Basically I assumed that users with an attractiveness score of zero have 0% chance of getting likes, and users with an attractiveness score of 100% have 100% chance of receiving a like. This is admittedly an oversimplification, but since I couldn’t find any real data I’m trying to keep it as simple as I can.

With these curves, 50% of the likes given by men go to the top 27% women and 50% of the likes given by women go to the top 10% men, which is quite close to the numbers reported by Hinge. So this should be reasonably accurate. Let's run our final simulation.

You can try to guess the results now. This was a bit of a trick question. The numbers stay the same.

That’s because even though we added inequality between users within the same gender, we didn't change the behavior of men and women as a group that much. They still give the same amount of likes. The difference is, now the distribution is skewed by the top users who get most of the likes.

That means that the averages no longer describe the experience of the average user. So let's add a new metric, the median: in other words, how many likes and matches does the average user get. And now we can see the average male user only receives 1 like and zero matches.

If we look at the average number of likes for different attractiveness scores, we see that the top 10% of male users get 37 likes on average whereas the average users get somewhere between 0 and 1 like. And If we look at the average number of matches, something interesting happens. The top 10% of men actually get more matches than the top 10% of women.

They get fewer likes but because they are less selective than women, they actually manage to get more matches. I wonder if this also happens in real life. As I tried to make my simulation more realistic, the number of likes received by the average man went down from 25 to 12 to 7 to 1.

Once again, this was just my attempt to simulate a dating app based on the little information that I could find. It may or may not describe your experience with dating apps. For example, I couldn't find data for the number of profiles users see every day, so I just assumed it was 100.

And I didn't include factors like different cultures and demographics, and the impact of premium subscriptions that give advantages to paying users. Nonetheless, the simulation helped me understand what’s probably happening inside these dating apps So, what are the key takeways here? This video is by no means making an argument against the use of dating apps.

Meeting online has become the most popular way U. S. couples connect, according to this study.

So dating apps do work and many relationships were made possible thanks to them. My personal takeway of this video is that, due to the reasons I mentioned, dating apps can give us a distorted perspective of the actual dating world, and I think we should be aware of the impact this can have on the experience and self esteem of the users. Because, in the end, I think this imbalance can be harmful for both men and women.

Men will struggle to get matches, which only gives them an incentive to like as many profiles as they can to improve their chances of a match. And women, when they get a match with a man, they know that he’s probably giving a like to every second profile he sees, so there's a good chance he's not even genuinely interested. That’s my take, but let me know what you think in the comments.