Matchups

For every investigator with at least 5 games against a particular Ancient One, we show their win rate — nudged toward the community average of 59.8% so very small samples don't dominate. The best matchups (30+ games) lead off; the worst follow.

Win rate by matchup

Each cell's color shows the win rate for that investigator against that Ancient One. Hover a cell for the game count and the unsmoothed rate.

N= 96,507 HOW?

Hover a row below to spotlight that matchup in the heatmap above; click to pin.

Best matchups (n ≥ 30)

N= 317
  • Agatha vs Rise of the Elder Things87.9% (48 games)
  • Bob vs Rise of the Elder Things83.6% (51 games)
  • Jacqueline vs Antediluvium83.3% (86 games)
  • Agatha vs Shub-Niggurath83.3% (44 games)
  • Ursula vs Hypnos82.6% (88 games)

Worst matchups (n ≥ 30)

N= 562
  • Michael vs Cthulhu33.8% (55 games)
  • Finn vs Cthulhu36.2% (106 games)
  • Harvey vs Cthulhu37.5% (62 games)
  • Mark vs Yig37.9% (307 games)
  • Vincent vs Cthulhu38.0% (32 games)

The cost of a matchup

A 60% win rate against Cthulhu feels different from a 60% win rate against Yig if one of them leaves the table littered with corpses. Win rate alone misses the bodies.

Empty chairs by Ancient One

Each bubble is one Ancient One. X = win rate. Y = average investigators lost (defeated or devoured) per team member. Bottom-right is the dream: you win, and everyone goes home. Top-left is the nightmare: you lose, and you lose everyone.

N= 21,135

Modifiers: preludes that flip a matchup

Picking the right prelude can swing a matchup by more than picking the right investigator. Each cell shows how the win rate against a given Ancient One changes when a particular prelude is in play vs. when it isn't.

Prelude × Ancient One swing

How much each prelude changes the win rate against each Ancient One (with the prelude vs. without). Green cells = this prelude makes that matchup easier; red cells = it makes it harder. Showing the 30 preludes with the biggest swing overall; cells without enough games are dimmed.

N= 9,307

Partnerships that change the matchup

Some investigator pairs win more often together than either of them does on their own. Below: the pairs whose joint win rate most beats (or trails) their solo win rates. Anything to the right of the dashed 1.00× line means the pair is greater than the sum of its parts.

Pairs that lift each other

The best teaming-up effects (20+ shared games). The number is how their joint win rate compares to playing apart — 1.20× means they win 20% more often together. Whiskers show the range of uncertainty; green whiskers mean we're confident the boost is real.

N= 409

Pairs that drag each other down

The opposite end (20+ shared games): two investigators who win less together than they do separately — maybe they fight for the same items, or step on each other's roles. Red whiskers mean we're confident the drop is real.

N= 422