About this project
Every game on Steam, Metacritic, and Wikipedia, placed in 3D space based on the tags real players apply to them. Games with similar tag profiles cluster together. Games that resist easy classification end up somewhere in the space between.
by Cooper Colglazier · spacebetweengames@gmail.com
How to use
This is an interactive 3D map of 90,000+ games from Steam, Metacritic, and Wikipedia. Games that share similar player-applied tags cluster together. Fly into a cluster, click on games to learn about them, and discover things you've never heard of sitting right next to your favorites.
Use the search bar to find a specific game and fly to it, or hit the random button to fly somewhere unexpected. Filters let you narrow the cloud by genre, review score, Metascore, and tags. Click a game to see its info card, pin cards to compare games side by side, and use the share button to send a direct link to any game. Press C for cinematic mode to hide the UI and just fly around.
Filters
Filters narrow down which games are highlighted on the map. Games that don't match your filters are dimmed but remain visible, so you can always see the shape of the full cloud. The game count in the filter bar updates in real time to show how many games match.
Genre
Filter by genre category. Each game belongs to one genre based on its dominant tags. Select one or more genres to highlight only those games. Click a genre again to deselect it.
Metacritic
Set a minimum Metascore threshold. For example, selecting 80+ shows all games with a Metacritic score of 80 or higher (including 90+ games). The dropdown shows how many games match each threshold. Only games reviewed on Metacritic have a score — games without one are hidden when this filter is active.
Steam reviews
Filter by minimum review count (e.g. games with at least 1,000 reviews) and/or by Steam review rating tier (Overwhelmingly Positive, Very Positive, etc.). These two sub-filters combine: a game must meet both the review count and the rating criteria to pass.
Tags
Search for any tag and choose to include or exclude it.
- + Include means the game must have that tag. Multiple included tags use AND logic — the game must have all of them.
- − Exclude means the game must not have that tag. Useful for hiding content you're not interested in.
You can also click tags directly on a game's info card to quickly add them as include filters.
Methodology
Data sources
Game metadata and review scores come from three sources: Steam (community tags, user reviews, and store metadata via the Steam Store API — the primary signal for where each game lands), Metacritic (critic and user scores across its full game catalog), and Wikipedia (via Wikidata — pre-Steam, retro, mobile, and console titles that never had a Steam release, plus encyclopedic descriptions used for tagging). The combined dataset covers 90,000+ games.
Tag vectors
Each game is described by a vector of Steam user tags with proportional weights (reflecting how often the community applied each tag).
For example, Sleeping Dogs and Sifu are both martial arts action games set in crime-filled cities — and they end up near each other on the map. What they share:
But what separates them is what pulls them toward different neighborhoods. Sleeping Dogs has:
While Sifu has:
Numbers show how many Steam users applied each tag to that game (Sleeping Dogs / Sifu for shared tags).
Tags like Base-Building or Detective carry more signal than ubiquitous ones like Singleplayer. The vast majority of games have real Steam user tags. For Metacritic and Wikipedia titles without a Steam presence, tags were predicted using an AI model from their descriptions and genres. Games with fewer than 4 tags lack enough information to be placed meaningfully and were excluded from the map. Utilities and audio/video production tools from Steam were also excluded.
Dimensionality reduction
The high-dimensional tag vectors are projected into 3D space using UMAP (Uniform Manifold Approximation and Projection). This preserves local neighborhood structure: games that share tag DNA end up near each other. Developer and release year features provide a secondary clustering signal, so games by the same studio or from the same era tend to group loosely together.
Tuning UMAP parameters is part science, part art — balancing readable spread between games against tight, accurate clusters. Understanding UMAP is a useful resource for learning how parameter choices shape the final layout.
Don't @ me...
- / This project is not affiliated with Metacritic, Steam, or any game publisher.
- / Review scores and metadata may not be perfectly up to date or entirely accurate. Data was collected at a point in time and may drift from current listings.
- / AI-predicted tags (for Metacritic and Wikipedia games without Steam presence) may definitely contain inaccuracies.
Spatial placement is purely algorithmic (and stochastic — running the data pipeline again would produce a slightly different mapping). In other words, the specific clustering and placement of games shouldn't be considered perfect. Games that are near each other share similar tag profiles, not necessarily similar quality or feel.
Genre-blending games naturally end up between clusters. For example, Grand Theft Auto IV sits almost exactly equidistant between the RPG and Shooter neighborhoods — it shares DNA with both, so the algorithm places it in the space between.
Distance on the map is not a quality judgment. Exploring the edges of clusters is worthwhile — games there often blend genres in interesting ways. However, the specific distance between clusters is mostly meaningless and an artifact of the algorithm. The intention with this project is to help people visualize the gaming space and find new games via natural exploration.
Inspiration
This project is inspired by Every Noise at Once by Glenn McDonald — a genre map of the music world.