← All contributors

nh

Overview

27
Games logged
9 won, 18 lost.
33.3%
Personal win rate
−26.8 pts vs community (60.1%).
597
Days active
16.5 games / year on average.
Jacqueline
Favorite investigator
Most-played character.
3
Longest win streak
Consecutive victories in a row.
10
Longest loss streak
Consecutive defeats in a row.

Career arc

Aug 22, 2015
first game
Apr 10, 2017
last game

Wins vs losses

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

N= 27

Best & worst Ancient One (n ≥ 5)

  • Best Cthulhu — 0.0% (n=5)
  • Personal nemesis Cthulhu — 0.0% (n=5)
  • Unique AOs faced 13

Net record

−9
9 victories minus 18 defeats. Below the 50/50 line by 16.7 pts.

Ancient Ones

Above or below the curve

For each Ancient One this contributor has faced at least 5 times, the gap between their personal win rate and how the community does against the same foe. Green bars = they beat the community average; red bars = they trail it.

N= 5

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= 27

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= 22

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= 12

Where they sit among peers

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

N= 716
14%ile

Still learning — outperforms 14% 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.

9.7eff. AOs

Career is spread across many Ancient Ones. Top foe: Cthulhu (19% of all games). 13 unique AOs ever faced.

All Ancient Ones faced

Click a column header to sort.

N= 27
Ancient One Games Wins Win %
Cthulhu 5 0 0.0%
Azathoth 4 0 0.0%
Yog-Sothoth 3 2 66.7%
Ithaqua 2 0 0.0%
Rise of the Elder Things 2 2 100.0%
Syzygy 2 0 0.0%
Shub-Niggurath 2 0 0.0%
Nephren-Ka 2 2 100.0%
Abhoth 1 1 100.0%
Hastur 1 1 100.0%
Atlach-Nacha 1 0 0.0%
Hypnos 1 1 100.0%
Yig 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= 73

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= 73

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.

22.9eff. chars

Most-played: Jacqueline (8% of all games). 37 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= 20

Expansions mixed per game

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

N= 27

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= 73

Time & Activity

Time at the table

Across 14 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 14
51h
Roughly 51h 5m spent summoning horrors. Averaging 219 min per game (+48 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= 14

Quick wins or long grinds?

2min
Their victories average 218 min and defeats 220 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= 25

Games per year

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

N= 27

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= 27

Records

Trophy case

Career bests and lifetime tallies.

6
Best score
3h
Fastest victory
6h
Longest game
8
Biggest team
44
Monsters defeated
5
Investigators lost

How their games end

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

N= 27

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= 27