Online anime discussions seem obsessed with individual anime: there can only be one best anime (and it’s Rakugo, fite me irl). But rather than focusing on specific anime, could we look at what binds them together? I’m talking about anime fans. No anime fan watches only one anime, they watch lots, so if we chart the fandom overlap between anime we could how different anime series group together. Do they group together based on source material? The type of genre? The release date? Let’s find out!

The Analysis Process (skip this section if uninterested)

First, we need some data on anime fans. Fortunately, we don’t need to conduct an extensive survey, anime fans are happy to publicly catalog their tastes on sites like MyAnimeList.net. To get a random sampling of the active anime fandom, I downloaded the completed anime and profile data of anyone logging into MAL on a couple of days in January. I got data for 7883 users who had watched 1,417,329 anime series in total (enough for 4 lifetimes of constant anime watching).

I filtered out anything that wasn’t a TV series or movie, and any anime with fewer than 1,000 fans in our dataset, leaving us with 380 anime. I then merged anime entries of the same series together, leaving 268 anime to chart.

To map out the fandom’s tastes, I modeled each anime like it was a beachball floating in a swimming pool. Each beachball had several elastic strings tied to it of varying strength. The strongest string was connected to the anime/beachball that it shared the most fans with. The next strongest string went to the anime/beachball it had the second most fans with, and so on. This way anime with lots of overlapping fans would be drawn together. But to prevent the confused mess of a squeaky beachball bondage orgy, each beachball also bobbed up and down in the pool of water, causing waves that pushed away anything that got too close.

I let this simulation run until it reached an equilibrium point where the pull of the strings balanced against the push of the waves. The result would hopefully be an anime/beachball web where groups of anime with large fandom overlaps formed clusters.

The Fandom Map

After warming myself on my overheating computer processors, the simulation finally ended with the result below. The font size of each anime name is proportional to how popular it is, and the red lines represent the fandom overlap/elastic strings between beachballs.

A full-size version can be seen here.

Eek, that’s a lot to take in! At first glance there are a few obvious clusters like the Ghibli movies in the bottom right, but it can be hard to identify what common themes are linking these anime. So what do we do? MAL tags to the rescue! (Surely this is the only time those tags have ever been useful). We can use the MAL genre tags the highlight different genres and see if they group together.



The green dots show which anime have the genre tag in the title.



What had once seemed a single unified blob is shown to be split into multiple clusters, but what does it tell us? Each cluster should be judged on two metrics: self-cohesion and its proximity/overlap to other clusters.

Self-cohesion is how tightly grouped a cluster is. The more tightly grouped it is, the more fans tend to stay within that genre and pick anime based on it. So for example, the ecchi cluster is quite tightly grouped in the top left area, telling us that the inclusion (or lack of) ecchi content is important to those fans in choosing what to watch. Alternatively the lack of cohesion can tell us something too, drama anime are spread out all over the place, suggesting there isn’t a unified group of fans specifically seeking out drama content like there is for romance, slice of life, or action anime.



The proximity to other clusters tells us where fandoms overlap. For example, the thriller and ecchi clusters are at opposite ends, suggesting that generally, the fandoms don’t overlap much. However psychological, mystery, and thriller anime all seem to overlap quite well.

It seems the most prominent split in the fandom is between romance and action anime, with the two of them taking up significant portions of the medium, but overlapping only in ecchi anime.



Recency Bias

As well as plotting genres, we can also see if the release date of an anime influences its position. If fans tend to pick anime based on what’s currently airing, that’d show up with anime of the same year grouping together.

While the middle area is quite mixed, the latest anime series from 2017 cluster significantly on the left side, suggesting anime aired in the same season do tend to clump together with a lot of fan overlap, but only while the anime is less than a year old. After that, the rest of the anime fandom who are pickier about the anime they try outnumber those who watch all the latest series.

Fandom age

Using the publicly listed birthdays of our MAL users, we can see which anime tends to attract the oldest users.

The age map is almost a mirror image of the recency bias map, with the unsurprising result that older anime tends to have older fans, and the most recent anime have the youngest fandom.

Gender differences

The number of women on MAL is likely undercounted as only 17% of those listing their gender state they’re female, but we can still analyse how the gender ratio changes between anime clusters.

Note that green nodes don’t mean a female majority (only 3 anime had a female majority fandom: Yuri on Ice, Free, and Ouran Koukou Host Club), it just means the ratio is higher than average.

There is clearly a gender divide in the anime each gender is watching. The female fans seem to gravitate more towards psychological stories and the Ghibli movies. Unsurprisingly, ecchi stuff is watched almost entirely by males. What’s more surprising is how few female fans there are among the recent releases cluster, suggesting female fans are less likely to follow the latest anime season.

MAL also offers a non-binary gender option. Only 1% of those displaying their gender picked non-binary, totaling just 61 users in my dataset, so I wouldn’t put too much stock in this result, but I was curious about the result…

It’s generally more mixed than the stark male-female divide, although the non-binary fan hotspots align much more with females than males, peaking on Yuri on Ice, Free, and Ouran Koukou Host Club.

Age ratings

Just for fun, we also tried plotting the age ratings of anime.

As would be expected, the R+ nudity ratings are clustered in the harem area, the younger ratings are mostly from the Ghibli films, and the R-17 violence ratings align with the action and psychological/thriller cluster.

Unrated anime

I have an obsession with rating anime, I must rate them all! But some users mark anime as “completed” without ever entering a score (what’s wrong with you?). I tried plotting the ratio of such unrated anime to see if they formed a pattern.

Turns out there’s a bit of a pattern here… ( ͡º ͜ʖ ͡º) The more ecchi it is, the less likely fans are to rate it. I guess they must scratch their rating compulsion itch through other means.

I hope you liked our little analysis. It wouldn’t be possible without the help of Part-time Storier, 8cccc9, and critttler. Thank you all~



I always love discussing this stuff, so feel free to contact me here on tumblr, twitter, or Discord ( Sunleaf_Willow ／(^ n ^=)＼#1616).



My next post (in a week or two) is going to focus on female fans and see how the female fandom’s tastes cluster.

