Every dot is a game. Color reads from red (games less active over time) to green (games more active over time). Click any bubble to add it to your selection for the sandbox where you will be able to analyze your picks. ⌘ Cmd+click (Mac) or Ctrl+click (Windows/Linux) to add multiple games without opening the card.
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Thirteen findings. One thesis: most games die fast, but the ones that survive share a handful of measurable traits. Every chart below is computed live from the dataset.
Most games are already dead.
We tracked - titles on Steam: their player histories, their community scores, their stories. Most fade within months. A few never do. We wanted to understand why.
The median game holds onto just - of its peak relevance. Three quarters never cross -. The rest stopped showing up.
Distribution of alive ratio across all tracked games. Log scale on y-axis.
Drag a band across the right half of the histogram (alive ratio > 50%).
Only - games, barely -, survive that threshold. The entire "still alive" population fits in a sliver of the distribution.
Old games can be alive. New games can be dead.
If decay were just entropy, every 2010 release would be a ghost. The scatter refuses that story. Survival spreads across every release year: age is a weak excuse for dying.
Release year explains almost none of the variance in alive ratio. Something structural separates survivors from the rest.
Each dot is a game. Y = alive ratio. Size = sqrt(peak players). Color = alive ratio.
Switch to "AAA and Immortals" to restrict to titles that mattered, at launch or never stopped mattering.
The rolling median stays nearly flat across cohorts. Age is not destiny, even for the biggest releases.
The distribution splits cleanly by design intent, and the metric measures different things in each.
For pure-online games, we measure temporal persistence: the fraction of months the game maintained at least 20% of its peak player base, weighted by peak size. Most cluster near zero, they lose their audience within months of launch.
For story games, we measure completion success at scale: the share of engaged users who reached a positive outcome (still playing or completed), weighted by audience size. The distribution spreads further right because finishing a game leaves a different trace than dying servers do.
Hybrid games blend both, leaning toward the multiplayer side.
The metric measures different things per type. That's the point: a multiplayer game survives by holding a live community; a story game survives by being finished, recommended, and remembered. Two definitions of "alive" for two regimes of play.
Distribution of alive ratio by game type. Story measures RAWG engagement; pure online measures Steam player longevity. Different metrics, different shapes, same insight: design intent structures how games age.
Pure-online games have the longest reach upward, but most cluster near the floor with everyone else.
Sort by game type and the shape becomes visible. All three distributions are bottom-heavy: most games die. But the top tail differs. Pure-online produces the rare blockbusters that climb above 70%. Story games top out lower but maintain a denser mid-range. Hybrids inherit a bit of both.
Design intent doesn't predict success, it shapes how a game ages. A multiplayer game that survives can reach extraordinary heights; a story game that survives lives more modestly but more reliably. Two definitions of "alive" produce two shapes.
Beeswarm by game type. Y = alive ratio. Color = archetype. Click any dot to add to selection.
Hover the highest dot in the "story" column.
Even the story outliers carry sandbox or co-op tags. The exceptions prove the rule.
Nominal genre labels barely predict survival.
Group by top-level genre and the median alive ratio barely moves. Action, RPG, Strategy, the labels map loosely to what players actually do. The signal hides one layer deeper.
Genre is what's on the box. Survival is what's in the loop, the behavioral tag DNA underneath the marketing label.
Median alive ratio by genre with p25-p90 whiskers. Dashed line = global median. Genre is what's on the box.
Community love is the strongest predictor of survival we found.
Of the - well-rated games (RAWG rating ≥ 3.5), - are still meaningfully alive today: -. Players who love a game tend to stick around, return, or recommend it. The correlation isn't perfect, but it's stronger than any other signal in this dataset.
Still, the beloved aren't all eternal. - well-rated single-player titles have moved past the moment of active play, finished, archived, and remembered. Their love endures even when their lobbies don't.
Rating (RAWG 0-5) × alive ratio. Click a quadrant to drill into its top games.
Click the bottom-right quadrant, high rating, low alive ratio.
A long list of well-reviewed titles that most players completed and left. Quality earns admiration; it doesn't guarantee staying.
Some games explode at launch. Others take years to find their audience. The difference shows up in who survives.
The left chart tells a retention story: games close to the diagonal kept their players. The right chart asks how they got there: fast or slow. Big launches cluster near zero months to peak. But months to peak alone predicts little: the path to the top matters less than what you build once you get there.
Most games peak the day they launch, but when you peak says nothing about how long you last.
Left: peak × current players (log-log); diagonal = perfect retention. Right: peak × months from release to peak, colored by alive ratio. Big launches cluster near zero.
November and May launches peak higher. The surviving share is the same.
Q2 and Q4 launches outperform in absolute player numbers at launch, the holiday window and the spring-sale spike are both real. Six years later, the retention percentage is indistinguishable. The calendar matters to marketing, not to memory.
Timing the launch is a launch-week optimization. It does not change the structural question of whether the game has a reason to persist.
Radial bars: outer ring = median peak players (log), inner ring = median alive ratio. One ring swings, the other stays flat.
Hover November, then hover March.
The outer ring (peak) varies across months. The inner ring (alive ratio) stays flat. The calendar is a marketing concern.
Not all tags are equal. Immortal games cluster around one idea: systems that never resolve.
Moddable, MOBA, MMORPG, Open World aren't genres, they're architectures for infinite play. Story Rich and Singleplayer, by contrast, skew toward the graveyard, not because they're worse games, but because they end.
Immortal games are endless worlds, mortal games are just stories.
Log-ratio P(tag|immortal) / P(tag|mortal). Green = immortal-leaning, red = graveyard-leaning.
Sort by Immortal frequency, which tags appear most often in games that lasted.
The architecture of survival emerges: Open World, Sandbox, Survival. This are structural commitments to endless play.
Living games keep generating Reddit threads. Dying ones go quieter, but the numbers tell a messier story.
Reddit post count should be a clean proxy for organic community discussion: no algorithm, no marketing spend, just players who care enough to post. And the correlation with alive ratio is positive, but weak. Reddit communities outlive the games themselves: r/Skyrim keeps posting long after the Steam numbers have faded. A dead game can carry a loud subreddit. A thriving one can have almost none.
Social noise and player retention don't always speak the same language.
Reddit post count × alive ratio (log-log). Color = archetype. The cloud is wide: many dead games still carry a large Reddit footprint from their past.
Five trajectories. One plateau, four descents, and one revealing overlap.
Normalized to their players at sixth month after release, Immortal games hold the line: a plateau that oscillates between 90 and 120% for years, never breaking. Every other category slips below the cliff. AAA and slow-burn titles erode steadily to 55-70% after six years. But the most telling comparison is between Fading AAA and Mid: their curves trace nearly the same path. A failed AAA does not fail gently, it decays like a mid-tier game. Budget and marketing buy a louder launch, not a longer life.
The curve is the verdict: a plateau means the game found its audience, a cliff means it never did, no matter the launch.
Median player count / month-6 baseline (months since release). Decay curves from 2013-onward games with ≥6 months data.
Enable "AAA" and "Fading AAA" alongside Immortal.
Three curves drifting downward and one that refuses to fall. Immortal games hold their baseline for over five years while AAA slides and Fading AAA collapses. Early scale does not prevent the drop, it just makes it more visible.
Six signals. Five archetypes. Each leaves a different shape.
Across eleven chapters we tracked what makes a game survive: whether players still show up, whether they rate it highly, how much of its peak audience it still retains, the breadth of its social footprint, how many players actually finish it, and how many bother to leave a review. No single signal is enough. But plot all six together, by archetype, and a signature emerges. Immortal games are the ones that fill the hexagon.
The archetypes are not just labels. They are distinct profiles, each shaped by a different survival logic.
Median value per archetype, normalised to the cross-archetype maximum (outer ring = best in class). Axes: Alive ratio, Completion, Playtime, Rating, Community, Decay plateau at month 24.
Enable "Immortal" and "Fading AAA" alongside AAA.
AAA and Fading AAA collapse on Alive and Retention; Immortal stays wide on every axis. The three archetypes have distinct survival fingerprints — same scale, different fates.
Across all the data, these are the games that stood out.
From thousands of titles, a handful earned their place: the immortals that never stopped holding an audience, the AAA giants whose scale couldn't save them, and the slow burns that grew quietly into something lasting. These are not a random sample. They are the clearest examples of each archetype we found.
Load them into the sandbox and explore them on your own terms: any axis, any combination, at whatever depth you want.
Click individual games to add them, or use "Open all in Sandbox" to load all 15.
Fifteen games chosen to embody, and challenge, the argument you just read. Compare them on any axis in the sandbox.
The findings above are our reading of the data, not the final word. Select the games that surprised you most and explore them side by side in the sandbox. Challenge the thesis. Find the exceptions. Build your own argument.