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nobody

Overview

21
Games logged
16 won, 5 lost.
76.2%
Personal win rate
+16.1 pts vs community (60.1%).
4,072
Days active
1.9 games / year on average.
Jacqueline
Favorite investigator
Most-played character.
6
Longest win streak
Consecutive victories in a row.
2
Longest loss streak
Consecutive defeats in a row.

Career arc

May 20, 2014
first game
Jul 13, 2025
last game

Wins vs losses

A quick visual tally of this contributor's career so far.

N= 21

Best & worst Ancient One (n ≥ 5)

  • Best
  • Personal nemesis
  • Unique AOs faced 9

Net record

+11
16 victories minus 5 defeats. Ahead of the 50/50 line by 26.2 pts.

Ancient Ones

Most-faced Ancient Ones

Where this contributor spends their time, split into wins and losses. Bar length is total games against each foe; the green share is how often they won. Sorted by games played.

N= 21

Win rate with confidence range

Same per-AO win rate, but with a 95% confidence band based on sample size. Wide bars = few games (treat with caution). Narrow bars = many games. Only AOs with 2+ games shown.

N= 18

Personal vs community win rate

Each Ancient One the contributor has fought multiple times. Across the bottom: the community's win rate against that foe; up the side: this contributor's own. Color marks the gap — green where they beat the community against that foe, red where they trail it. Dot size shows how many games they've logged against it.

N= 14

Where they sit among peers

Rank against every other contributor with at least 5 games logged. Higher is better.

N= 716
77%ile

Top-quartile player — outperforms 77% of contributors with 5+ games.

Repertoire breadth

An effective count of how many Ancient Ones make up this contributor's career — accounts for how lopsided their distribution is. Close to the unique-AO count = broad variety. Close to 1 = one or two favourites dominate.

7.2eff. AOs

Career is spread across many Ancient Ones. Top foe: Yig (19% of all games). 9 unique AOs ever faced.

All Ancient Ones faced

Click a column header to sort.

N= 21
Ancient One Games Wins Win %
Yig 4 4 100.0%
Shub-Niggurath 4 2 50.0%
Nephren-Ka 3 3 100.0%
Yog-Sothoth 3 3 100.0%
Abhoth 2 1 50.0%
Shudde M'ell 2 1 50.0%
Rise of the Elder Things 1 1 100.0%
Antediluvium 1 1 100.0%
Azathoth 1 0 0.0%

Investigators

Most-played investigators

Where this contributor spends their time on the roster. Sorted by total games with each character.

N= 80

Win rate by investigator

Per-character win rate with a 95% confidence band. Only investigators played 3+ times appear; wide bars mean a small sample.

N= 86

Roster breadth

An effective count of how many investigators make up this contributor's career — it accounts for how lopsided their picks are. Close to the unique count means wide variety; close to 1 means a couple of mains dominate.

23.3eff. chars

Most-played: Jacqueline (8% of all games). 35 unique investigators ever fielded.

Expansions

Boxes in play

How often each expansion is on the table in this contributor's games. A single game usually mixes several boxes, so the totals overlap.

N= 19

Expansions mixed per game

How many expansion boxes this contributor typically combines in a single game.

N= 21

Win rate by expansion

Win rate in games featuring each box (95% band). Boxes played 5+ times only — this reflects which boxes they tend to win with, not the box's own difficulty.

N= 53

Time & Activity

Time at the table

Across 8 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 8
28h
Roughly 28h 20m spent summoning horrors. Averaging 213 min per game (+41 min vs the community's 171 min).

Games per year

This contributor's logged games by calendar year — the seasons when they were most active.

N= 21

Win-rate trajectory

A rolling win rate across their career in game order — is their form trending up or down? The dashed line marks the community average.

N= 21

Records

Trophy case

Career bests and lifetime tallies.

3
Best score
3h
Fastest victory
5h
Longest game
8
Biggest team
25
Monsters defeated
1
Investigators lost

How their games end

The split of victory types (green) and defeat causes (red) across this contributor's games.

N= 21

Outcome mix vs community

For each way a game can end, the gap between this contributor's share and the community's. Bars to the right = it happens to them more often than the average player.

N= 21