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kukish

Overview

16
Games logged
10 won, 6 lost.
62.5%
Personal win rate
+2.4 pts vs community (60.1%).
301
Days active
16.0 games / year on average.
Leo
Favorite investigator
Most-played character.
4
Longest win streak
Consecutive victories in a row.
4
Longest loss streak
Consecutive defeats in a row.

Career arc

Oct 13, 2020
first game
Aug 10, 2021
last game

Wins vs losses

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

N= 16

Best & worst Ancient One (n ≥ 5)

  • Best
  • Personal nemesis
  • Unique AOs faced 9

Net record

+4
10 victories minus 6 defeats. Ahead of the 50/50 line by 12.5 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= 16

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= 12

Where they sit among peers

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

N= 716
57%ile

Above the median — outperforms 57% of the cohort.

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.

7.5eff. AOs

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

All Ancient Ones faced

Click a column header to sort.

N= 16
Ancient One Games Wins Win %
Atlach-Nacha 3 1 33.3%
Hastur 3 2 66.7%
Hypnos 2 2 100.0%
Yig 2 1 50.0%
Nephren-Ka 2 2 100.0%
Azathoth 1 0 0.0%
Cthulhu 1 0 0.0%
Shub-Niggurath 1 1 100.0%
Shudde M'ell 1 1 100.0%

Investigators

Most-played investigators

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

N= 59

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= 45

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.

14.6eff. chars

Most-played: Leo (13% of all games). 26 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= 16

Expansions mixed per game

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

N= 16

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= 48

Time & Activity

Time at the table

Across 16 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 16
45h
Roughly 44h 50m spent summoning horrors. Averaging 168 min per game (−3 min vs the community's 171 min).

Game-length distribution

How long this contributor's games run, in minutes. The visible range clips a few long outliers.

N= 16

Quick wins or long grinds?

6min
Their victories average 166 min and defeats 172 min — they tend to close out the faster games.

Win rate by team size

Does this contributor do better solo or in a full party? Win rate against the number of investigators at the table, with a 95% confidence band. Only team sizes with 3+ games shown.

N= 14

Games per year

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

N= 16

Records

Trophy case

Career bests and lifetime tallies.

13
Best score
1h 30m
Fastest victory
4h 30m
Longest game
4
Biggest team
20
Monsters defeated
4
Investigators lost

How their games end

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

N= 16

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= 16