### How It All Began…
The premise is simple enough.
You choose an esoteric topic, make a presentation, and present it to our group of friends at Friday night drinks. People had made all kinds of presentations ranging from “On why Danny Devito is more likely than not my birth father” to “Giraffes; too tall?”
As my turn was coming up, I frantically began to ponder, what would I present on?
What strange topic would both amuse and terrify my friends simultaneously.
As my mind puttered along, as it always does, somewhere between humming the Spongebob intro for the 48th time and deciding whether or not a hot dog is a sandwich ([IT IS, YOU WILL NOT CONVINCE ME OTHERWISE](https://www.theverge.com/tldr/2020/4/9/21214870/hot-dog-sandwich-debate-quiz-alignment-website)) it hit me.
> I should do my presentation on Tinder…
I’d spent 5 years using the application, and the science of dating has always fascinated me. And that summer I’d spent retooling my coding skills in Barcelona; the timing couldn’t have been better.
Off the absolute deep end I went.
Before I knew it, I sat there flipping through 44 slides of my Tinder data in presentation format.
> Did I really just put together 44 slides on my Tinder exploits…WITH SOURCES (my high school teachers would have been proud… sort of)?
At that point, I wasn’t sure if it was the 4 Red Bulls, the night schedule I was working, or the sheer ludicrousness of it all, but I couldn’t stop giggling at what I had created. So I figured, heck, might as well share this presentation with all of you as well.
### The Descent Into Insanity…
**Presentation Links:** [PDF](https://drive.google.com/file/d/14E_E6ur6o8EI85SKS6jsEwAgJ6RsRrPZ/view?usp=sharing) or [PPTX](https://drive.google.com/file/d/1yukMl1TYVgozAvaqajtSMVtmLPWizC_p/view?usp=sharing)
So clearly, there’s no point to this, besides having a bit of fun analyzing over 5 years of data. I just want to give fair warning now, if you’re looking for productive Tinder insights or useful profile hints, my friend you are in the wrong place.
But before I dive too deep into my own data, I want to provide a little background & context into Tinder itself for those living under a rock.
###### My friends have extremely high opinions of me
###### That style of swiping on anything is a tactic called post match filtering
For those without the fortune of getting to use Tinder, you can think of post match filtering as the following; you swipe right on everyone, get a few matches, and then engage with only those you find attractive, leaving the rest un-messaged.
Can you imagine if you tried to apply the same tactic in real life at a crowded bar? You just run in and scream, “I’M INTERESTED IN EVERYONE!” and hope for the best.
###### hahahahahahaha, sorry Cameron
###### Doggos for the win
This title is extremely misleading, I know. I’m not the guy from the article, so don’t go thinking I’m some sort of modern day Hitch, but it’s the actual article I read when I first tried imitating antics like adding “match of the day” filters to my photos.
It’s still hard for me to believe I literally read the article, thought to myself…
> WOW… this a great idea
Stood myself up, walked over to the Georgia Tech campus library, went to the multimedia center, tried to recreate it on photoshop, failed miserably, asked the poor girl working the help desk, received the most pitiful look of my life, and finally got her to essentially make it for me.
If there was ever proof that time travel is not happening in our lifetimes it’s this right here. Because most certainly, I should have traveled back in time, slapped myself in the face, and curb-stomped my iPhone 4. But alas, that grinning 19yo idiot went off and published those photos to his profile.
But I digress, those were many moons ago, and since then I have matured from making poor photoshop projects for Tinder to making elaborate powerpoints for Tinder.
Ahhhh… progress.
### More Recently…
So post college, what exactly did life look like for me? Well it involved a heck load of moving thanks to work. And with all that moving what better to do than Tinder? I mean after all…
And after 4 years, when you finally mixed in all the cool diagrams I saw on reddit for r/tinder, me being a nerd, and my weird competitive edge about this whole Friday night drinks thing,
well… you get all this **\*gestures wildly at article\***.
But I didn’t want to just do an analysis like I had seen from other people on the internet, and so from arguably my favorite video clip of all time it was time to…
###### This seriously has to be one of the greatest videos on YouTube
If you haven’t seen this video, do yourself a favor and [watch it](https://www.youtube.com/watch?v=oxxBXpnn2Jw&ab_channel=Movieclips) now… Go on, I’ll wait, it’s that good.
> You back? Good, let’s get on with it
So what was the first step in my decent into insanity. Well easy enough, I needed to go get my information from Tinder. It’s super easy to get, simply go [here](https://account.gotinder.com/) and request your data. You can even try it for yourself!
In a few days you’ll receive a massive JSON file to start exploring. When you first crack it open, it should look something like this.
> Now I know what you’re about to do, and it’s ok. Put your laptop or phone down and back away slowly, don’t yeet that device out the window.
It’s ok, this won’t be a crazy in depth presentation on coding or data structures.
With the data in hand, all there was left to do was a bit of coding.
###### The code is on my [github repo](https://github.com/syrashid/Tinder-Project-Code)
### So What Did We Get?
Nothing too fancy but enough for us to start having some fun. As a homage to all my Redditor brethren before me, I started off with the classic Sankey diagram. A type of diagram breaking down data into flows from a starting point to an ending point. For me I started with my total number of swipes, and ended with successful dates from the application.
To provide some context, not all of this was done with ruby, some was also manually done as there was really no good way to get some of the numbers near the right side such as number of dates I went on.
But overall, the data speaks for itself, the discrepancy between men and women when it comes to swiping is absurd. I wouldn’t consider myself a cave troll, but that match to right swipe ratio (2.5%) is abysmal.. oof.
As a point of comparison, when looking at the same ratio for females on r/tinder the percentage falls between 25–30%.
All right, what about what I was saying to the few lucky ladies I did manage to match with?
###### Yes, I did spend the time making the word cloud into Tinder’s logo
This was actually based off a class exercise during my time with Le Wagon, a coding bootcamp I attended in Barcelona. Time well spent as you can see.
Well if building word clouds wasn’t a good use of time, maybe doing sentiment analysis would be, so that’s what I did.
This was inspired when I saw an article analyzing Trump’s twitter feed to evaluate his overall sentiment. Sadly, nothing mind blowing here either…
All right then, word processing was meh, let’s talk behavior instead.
And as all high school math nerds will protest… WE WANT GRAPHS, WE WANT GRAPHS, WE WANT GRAPHS.
### Well then graphs you shall get…
###### My gift for slide titles is the skill I’m most proud of
We’ll start off by analyzing the number of times I opened up the app in a single day.
Looking at Sep 25, it’s probably good that I am no longer working at that company…
But this data is kind of hard to look at, let’s aggregate it by month to see if we can unearth some other insights.
I laughed out loud when I first saw Feb 2019. When I first decided to quit, I had put in my resignation months earlier, meaning that by February, I was living my best lame duck life. I had helped find my replacement, and I had transitioned all of my responsibilities. When my friends would ask what I was doing with my days at work I would shrug my shoulders and say,
> I don’t know, emails and admin stuff mostly
Clearly not…
But let’s take a step back, is thirst really measured in app opens? Or would it be better to look at my total swiping behavior per month.
Yes, let’s do that.
My favorite part of this graph was realizing why the absence occurred between Aug 2017 and Apr 2018. No, it wasn’t because I started dating someone, and no it wasn’t because I had lost my phone. It was because I got so tired of the endless swiping to no avail that I said to hell with it.
And what about when we break these down by ratio instead, taking right swipes over total number of swipes on a 100% scale, how do we fare?
I think the uh oh period speaks for itself. As time passed, my standards began to drop as well as my desire to put in effort. The act of sifting through potential matches was too exhausting, even if it were just flipping through a few photos. Why not put in effort after a match was made, when you were guaranteed the other party was interested in you?
As for the two spikes in 2016 and 2018, well those actually coincided with when I shaved my head for St. Baldricks, it’s funny how our perception of ourselves affects our behavior. Even though you’re exactly the same person, even with no bald photos online, it still affected my behavior subconsciously.
And of course the last spike, it occurred when I moved to Barcelona. I was there for only three months attending a coding bootcamp and I have no regrets. It truly was the summer of love.
But for all the increases in swiping and shenanigans, my matches simply weren’t keeping pace. It would make sense that as I swiped more I should receive more matches, but the data never really supported it. There could be a million reasons as to why this happened, potentially the rise of other dating apps, people getting pickier, or heck maybe I fell down another branch on the ugly tree, who knows? 🤷♂️
So at this point, you like me are a little bar charted out. Let’s instead, take a look again at the old Sankey diagram, specifically that one portion we glossed over last time.
### OK… No More Graphs…
### Please…
I want to dive deeper into the what happens after we match.
So let’s say it’s Friday night, you’re about to go out for drinks, but you’ve got 20 min before you meet your friends at the bar; so you decide to hop on Tinder to kill some time. You’re swiping, you’re swiping, left swipe there, swiping some more, right swipe there, and boom, suddenly you stumble across this profile.
And you think to yourself, “huh, he doesn’t look like he fell off the ugly tree and hit _every_ branch on the way down, seems adventurous, good with birds (probably knows bird law), likes snacks. I’m gonna swipe right.”
But after the message, how exactly did my odds look?
Honestly, I was pretty happy about this.
And it was around this point I learned a valuable lesson about myself…
###### Some back of the envelope calculations, but they get the point across
I want to emphasize here, this is just time spent swiping, this doesn’t include anything related to setting up a profile, getting those pics where I looked like a damn snack, or coming up with the wittiest content for my profile. These numbers are purely based off time just swiping, and swiping alone.
With all this time spent, clearly there must have been some “highlights”. What exactly were these?
In my defense the cutecumber line did work for me, and I did end up striking a wonderful conversation with someone.
But the real question that kept popping up for me was, for all the effort and time I was putting in, was I becoming more successful ?
Mind you this is a completely made up metric, but I reckon it does a pretty good job of estimating how successful you are over time.
It is calculated by considering success as the ratio of your monthly matches over your monthly right swipes. With 100% being you match every right swipe, and 0% being extremely close to what my actual number was. 😭
As I charted these by month, it’s pretty clear to see my prime in Tinder fell somewhere in 2016 and 20017, where I hit upwards of 10% twice. However, as 2018 and 2019 dragged on, I at one point hit less than 1%…
Now as I mentioned earlier there could be a lot of reasons for this, but I had to be sure no matter what it was, that it wasn’t my looks.
### My Opus Magnum…
So I did one of the most complex breakdowns for the presentation and analyzed photos of myself over time to see if I could figure out what’s going on. I even did a complex visual aging extrapolation to see what I’d look like in 2030.
###### Please see all the science I did
Clearly, something was wrong, given these photos I assume it was something to do with Tinder or just modern culture as a whole, we will probably never know.
### A Few Final Thoughts…
As I wrap up this tinder-novella, you’re probably asking “But Sy, what did you learn? Are you telling me I just spent 30 minutes reading this, and my only take away is that you’ll make an incredible Eddie Murphy impersonator circa 2030?”
To this, I would say of course not.
Below I’ve summed up my hot takes for Tinder use for all you folk out there:
The slides do it better justice than I ever could. Don’t try and take anything from this, don’t try and pull out some hidden meaning, just don’t.
If anything I would leave it at this.
Everyone’s experience on Tinder is going to be different, and what works for some people will crash and burn for others. What makes some people cringe, will make other’s swoon.
My advice; experiment, have fun, and make crazy charts to scare your friends; life is too short to listen to same internet randos take on what’s right and what’s wrong for Tinder.
All I really took away from all of this is; it was really fun to use my coding skills for something actually fun, as opposed to just getting another exercise to spit out “Hello World!”
With that being said, as I was building all of this out, I realized some of you may also want to see some of this info for your own data.
And again, because I love making things that have no real productive value in society, I started building an app with a few friends to do just that.
Once we finish we’ll be posting the app [here](https://github.com/mangotreedev) on our github repo. Send us an email at hello@mangotree.dev with the tagline “TINDER PROJECT” and we’ll let you know when it’s ready!
