The Visual Novel medium encompasses an enormous range of diverse content. From stories about philosophy to murder mysteries, romance, comedy, and of course, porn. But to what extent do these categories and the fandoms that love them overlap? Using the vote data on VNDB, we visualised the VN medium by mapping out the fandom overlap, and in doing so, learned a few things about the VN fandom.

The Analysis Process



To get an overview of the Western VN fandom, we downloaded all the user-submitted VN ratings on VNDB (using this as a proxy for which VNs each user had read), and filtered out any VNs with fewer than 250 ratings (to keep the final result a manageable size), leaving us with 410 VNs. We then calculated the proportion of users who had rated both VNs to see how much of an overlap there was in the fandom between those two VNs.

To visualise the results, we modelled each VN like it was a beachball floating in a swimming pool. Each beachball has some elastic strings attached to it, where each string lead to another beachball (representing another VN), with the tension in the string determined by the fandom overlap between those VNs. So VNs with large fandom overlaps would be pulled strongly towards each other. However, to prevent the beachballs all clumping together in some horrifying beachball orgy, each beachball also caused ripples in the water as it bobbed up and down, pushing away anything that gets too close. We ran the simulation until the beachballs stopping moving, having found an equilibrium point between the pull of the strings and the push of the ripples.

You can see the early stages of this process in the test simulation below. Each beachball/VN is coloured to highlight the type of VN it is. Handheld console VNs are pink, EVNs are green, and the rest are blue. The elastic strings between beachballs/VNs are shown by faint red lines, with the strings that pull tighter shown as a brighter shade of red. The font size is proportional to how popular a VN is.

You can see that the VNs are randomly distributed at the start, but quickly form into clusters of similar content. The murder mystery focused VNs (Danganronpa, Ace Attorney, and Zero Escape) form a cluster on the right, while the strategy gameplay heavy Sengoku Rance and Kamidori Alchemy Meister cluster in the top left.

The VN Market Map



A full size version of this image is here.

When we run the algorithm, the results can be a bit intimidating. A huge jumbled mass of VNs. How do we interpret this? The first step in understanding this map is recognising the clusters that the VNs have grouped themselves into. If we use the VNDB tag and release data, we can begin to highlight similar types of VN and the clustering becomes more apparent.

The Plot Focused category is only approximate as it highlights only those VNs with certain tags: “Horror,” “Utsuge,” “Nakige,” “Mystery,” “Murder Mystery,” “Life and Death Drama,” or “Thriller.” You can detect my plot preference biases in that list…



Combining these categories, we can generate a much clearer map of the VN fandom.

A zoomable version of this map can be found here, and a high resolution image is here.



There are two major elements in interpreting what this map says about the VN fandom. The first is how dense each cluster is (how tightly packed it is). The density tells us how much fans tend to stick to VNs of that type, and how important that type of content is to them. The second is each cluster’s position relative to one another, which tells us how much the fandoms overlap and what they’re most similar to.



Analysing the map from the top, we see the porn cluster stands erect. Its somewhat fringe position indicates that the nukige fandom is a minority of the Western VN fandom, although it’s still pushing hard into the main VN mass which shows there’s a lot of overlap, especially with those who like more light hearted stories, comedies, moege etc.

The strategy gameplay VNs barely make it into a cluster at all as they’re quite widely spread out, indicating that people aren’t necessarily drawn to them for their gameplay content, but value other aspects of them (*cough* the porn).

Next, we have the core of the map, the plot focused VNs with emotionally heavy content. Katawa Shoujo takes pride of place in the centre of the fandom as it’s by far the most frequently cited first VN fans read. In the bottom left we find a particularily tight subgroup of those VNs that seem to be linked through their detective gameplay mechanics and release on handheld consoles.

Over on the right, we can see a couple of popular VN series that seem a little disconnected from the rest of the map. The Nekopara and Sakura series have large fanbases, but are off in the fringe, nearer nukige stuff than anything plot related. Their fringe position implies that their fans are coming more from the Steam gaming community than the rest of the VN fandom as people who read either of those series don’t go on to read many other VNs.



Further down, we see the untranslated VN cluster is quite tightly bound, indicating that those who can read untranslated VNs tend to stick to just untranslated content. They highly value a VN being untranslated when choosing a VN to read. Its position at the opposite end to the nukige cluster indicates those learning Japanese for VNs are primarily interested in them for their stories rather than the porn.



The Western VNs are less a cluster than a halo around the the main VN fandom. This shows that EVNs have yet to really integrate themselves with the VN fandom as a whole (outside of otomes), and that while VN fans might be happy to read a particular EVN they come across, they aren’t actively searching for more EVNs. Those most willing to try them seem to be those who enjoy plot focused or otome VNs.



Lastly, we have the otome and yaoi clusters off by themselves in the bottom left, with the two of them sharing some fans. The relatively tight clustering indicates a loyal fandom, but they don’t seem to read much outside of that. They’re almost an entirely separate fandom from the rest of the VN scene, although they seem much more willing to read EVNs than the majority of VN readers.



VN Recommendations



Using this map of the fandom’s tastes, could we generate VN recommendations for someone? If we highlight only those VNs they’d read we could see if it clustered in one area, and if it did then any unread VNs in that area would be a good suggestion for them. I made a web tool (see here) to do exactly this (it requires you to have had a VNDB account before Nov 18th). If you’ve got a VNDB account, give it a try and let me know if the nearby VNs fit your taste~

I hope you found the post interesting. If you liked it, please share it around. I had a lot of fun working on it, but it was only possible thanks to the invaluable help of /u/8cccc9 and Part-Time Storier.



Next week’s post will break down the map into more categories and analyse them in more detail, look at how the VN ratings differ per cluster, and investigate how the EVN fandom is split. I’d love to hear any feedback or suggestions for further analysis. If you want to get in touch, you can comment here, on my Twitter, or PM me via the Ren’py Discord Server.