The Brutal Dating Data Behind One of Hinge's "Most Eligible"
How to get your profile viewed by 10x as many people, whether comments improve match rate for women, and match ratio across 9 years.
In 2015, someone from Hinge’s marketing team reached out to me and asked if I’d mind being featured on their “most eligible” list. They organized it by industry, and I landed at #4 in the Marketing category. Naturally, I posted it to Instagram, got a few messages from deeply random Facebook friends, and moved on.
The Business Insider feature is now long gone, evidenced only by some scattered press and my Instagram screenshot.
Nearly 10 years later, despite what I’d like to call “heroic efforts,” I’ve managed to remain single. Most of my “eligible” peers seem to have gracefully exited the dating pool, but here I am—still swiping. So, what does being an “eligible” woman on Hinge actually look like? And how do the stats reflect my experience?
Dating Data Dump
A few weeks ago, I discovered that Hinge—like Tinder—offers the ability to download your data. My curiosity immediately kicked in. I plugged my matches.json file into ChatGPT and generated a stunning (and slightly demoralizing) array of graphs. Let’s dive into what the data reveals about my dating life.
The Problem with Hinge Data
When I first explored tools to visualize Hinge data, I noticed they were reporting absurdly high match rates that didn’t align with my reality. These tools typically calculate match rates by dividing the number of matches by the number of likes, which is far too simplistic.
The real insight comes from distinguishing between “outbound matches” (when I send a like first) and “inbound matches” (when someone else likes me first). If there’s no “like” data attached to a match, it’s inbound. This distinction is crucial for understanding actual popularity.
Also, Hinge doesn’t report left swipes, leaving you in the dark about your own pickiness. To address this, you’d need a Hinge+ subscription to see all inbound likes, avoid rejecting any profiles, and only swipe right on people you actually want to match with. That’s the only way to calculate your pickiness on inbound likes. There’s no way to calculate outbound like pickiness.
What does my profile look like?
Profile as of December 2024:
As a data point, the profile above has achieved the LOWEST EVER outbound match rate I’ve ever had on Hinge, at 6% (117 outbound likes in November 2024, 7 outbound matches).
I’m honestly kind of proud. The profile is designed to be a bit weird and quirky and is a pretty accurate reflection of who I am. I’m even started seeing someone I met through it recently, perhaps the best success indicator.
How Popular Am I Really on Hinge?
As measured by outbound match rate
I’ve always found that I have the lowest success rate on Hinge (as defined by how many of my likes result in matches). While my Tinder match rate is around 60% on average, about double Tinder’s female benchmark according to Tinder Insights, the men of Hinge swipe right on me closer to 23% of the time, even though (or more likely, because) Hinge tells you everyone that has liked you, even with a free account, unlike Tinder.
I don’t find this surprising given that Tinder focuses on more casual connections and has a very high free daily right swipe limit (100 vs 8 for Hinge). Even as a woman, I’m liable to be more generous on likes on Tinder and decide later if I’m actually interested if there’s actually a match.
I graphed all outbound likes by quarter, overlaid with matches received from outbound likes and the outbound match ratio:
It’s interesting that there isn’t a very clear trend over time with match ratio, with the only big note I have is 2024’s drop in match rate. The outliers (2016 and 2020 Q2 and Q3) are all due to small sample size.
The pandemic decimated my dating life, followed by a nearly manic return a year or two later.
How to Get Your Profile Seen More on Hinge
This graph of inbound matches doesn’t say anything about how picky I am.
But it is interesting because assuming I’m consistently picky across time, it means that I’m getting a TON more people liking my profile during periods where I send out more likes.
Here is inbound matches graphed against outbound likes:
The correlation is .93, which is close to perfect.
More outbound likes sent = more people seeing my profile = more inbound likes = more inbound matches.
Takeaway: want more people to see your profile and get more inbound matches?
SEND MORE LIKES.
Either max out your free likes every day or pay for Hinge and send out a ton (but don’t send them out unless you actually want to match with them, because that would also confuse the algorithm).
Hinge understandably prioritizes showing the profiles of its most active users, throttling the likes if you’re not actively participating.
It’s extremely direct: if you send out 10x as many outbound likes, you will get about 10x as many inbound likes.
Phone Number Exchanges
While I can’t see messages people sent me, I can see what messages I sent. I asked ChatGPT to count instances where I shared my phone number by year across time:
As a percentage of total conversations, here’s what it looked like across time - because 2022 was such an insanely active year, the percentage dropped:
My FaceTime Screening Strategy, as Told by Data
So women get a bunch of matches. But how many (at least for this woman) lead to actual conversations?
It’s nice to see that I’ve actually become more engaged with matches over time. I sent at least 2 messages to only about 30-35% of message between 2018 and 2021, then far more in 2022 through 2024.
I can actually point to a reason for why that is. See the below graph:
Prompt: Graph average number of messages per match that has a conversation by year/month/etc.
You can see that while I engaged with more matches, the actual length of conversations went down dramatically 2022 onwards.
That was by design. 2022 was such a busy year, dating-wise, that I developed a new screening methodology for matches especially in the wake of the pandemic.
I switched from trying to suss out whether to go on a date through text conversation over to doing FaceTime calls.
I found this super helpful for many reasons:
A lot of men are bad at taking photos. A handful are way too good. Some men whose photos I liked were not attractive to me on facetime, and some men whose photos I was iffy on turned out to be quite good-looking on a video call.
Having a blanket policy of doing video calls allowed me to be more relaxed on judging photos since I could schedule in way more 30 minute calls than in person dates.
It gets rid of catfish extremely quickly. Some catfish will make up an excuse for why they can’t, but if I basically refused to continue talking without a video call, they’d just evaporate/unmatch.
Sometimes they weren’t straight catfish, but their photos would be 10 years outdated and they’d be lying about their age. One of the most egregious was a dude who had himself listed as 39 but was actually 54.
Sometimes it was super clear that our personalities did not mesh. Or they would talk to me for 15 seconds, decide they weren’t into it, and hang up/unmatch. Honestly, people can be amazingly rude, but I would rather know that without needing to make a trip in person.
Merely agreeing and showing up to a FaceTime call was a way to cull less dedicated suitors. I figured if they couldn’t set aside 30 minutes in their day to qualify for a date with me, they probably weren’t that serious anyway. Even if they didn’t like FaceTimes, I took it as a positive signal if they showed up since it demonstrated willingness to make compromises for my comfort.
It still wasn’t perfectly accurate but it did mean fewer surprises in person and super obvious bad fits got eliminated much, much earlier in the process. It also allowed more men to self-select out of the process early on, which is a win in my book - it would be pretty unlikely that a truly good match would forget to followup and schedule an in-person date after a video call with their soulmate.
Does adding a comment improve outbound match rate?
This was really interesting! The match rate of outbound likes with a comment was actually slightly lower than outbound likes without one.
However, this wasn’t a well-controlled, randomized trial. I am more likely to send a comment if a person is extra eligible so it’s possibly just an adverse selection effect.
Past the Match: How The Dates Went (in 2022)
Well. I only have exhaustive data about this from 2022. That was the only year that I was psychopathic/autistic enough to make a comprehensive spreadsheet that included how I met the person and how many followup dates there were and why things ended. Luckily for you, that was my most active year ever!
Here is the breakdown:
719 total matches
408 matches where I sent at least 1 message
27 first dates
5 second dates
2 third dates
1 mini relationship (more than 6-7 dates)
Here it is as a depressing Sankey graph:
Who broke it off with who? And did I like any of these guys?
8 I did not like them and they didn’t followup
6 I liked them but they didn’t followup
5 I ended things
3 they ended things
4 I was pretty lukewarm on and they didn’t followup, but I would have tried a second date if they had
1 we mutually ended things
The bolded categories I would label as “People I actually liked.”
That’s 10 out of 27, or 37%.
I also have this habit of going out with people whose pics I am ehhh on (where I’m not actively repulsed but also not terribly drawn).
People whose pics I was ehhh on: 12
Of those, people I felt attracted to in person: 4
So personality, charisma, and physicality triumphed over photos about 33% of the time.
How to Analyze Your Own Hinge Data
Open the Hinge app and go to Settings.
Scroll down to “Download my data.”
After receiving the email notification, download the .json file.
Upload the file to ChatGPT or another data analysis tool and start exploring.
Cross-check the outputs for accuracy if using ChatGPT and manually inspect the data if needed. Often issues can be fixed by starting a new chat.
Notes and Insights
I find it unlikely that I am actually one of the most-matched women on Hinge. (Not a comment on my self-esteem - I think I’m great! - just cold, hard math.) Unless other women want to chime in that they’re seeing similarly low match rates, it seems implausible that a female profile could be in the top 10% of match rates at 23%. I am missing data from 2014-2015 that was used for the actual “award” I received, though, and I was located in Boston rather than NYC which is less tough of a dating market for women so that may explain away some of my skepticism.
Anecdotally on a Reddit thread, some women reported their outbound like match ratio on Hinge was 5-10% and some men report being pretty selective with their inbound likes.
If you’re a woman, it would be way less ego-crushing to only tend to your inbox of inbound likes rather than sending out likes. However, the fewer likes you send out, the fewer inbound likes you’ll receive, as I’ve shown in the data.
Assuming that I’m above average in outbound match rate and of average pickiness (which my Tinder data benchmarked against Tinder Insights data confirms), it’s a little depressing even for a supposedly eligible woman sending likes.
In Conclusion: The Limits of Data
Dating is hard for everyone, even someone with an above average match rate who makes a good faith attempt to engage with a large portion of matches.
I think the older I get, the more I see a dating profile as less about getting the best match rate possible and more about repelling the wrong matches and attracting those that will be uniquely drawn to what makes you, well, you.
I do think my experience has been that men (and maybe women, I have no idea) are looking for a *feeling* more so than anything else. They are looking for things to feel “easy”, “comfortable”, for a “spark”.
I generally am on good terms or friends with most people I’ve dated, and they pretty uniformly cite “just not feeling it” or something about life circumstances (which I translate as “just not feeling it”) if they broke things off with me.
At the end of the day, we are fallible flesh-and-blood meat sacks swimming in a bunch of hormones, as much as I’d like for our feelings to be as easy to parse as a spreadsheet.
I really adore online dating data, well done.
Genuinely so excited to be at the objective analysis phase of online dating — grabs popcorn.