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nasoov

Overview

23
Games logged
9 won, 14 lost.
39.1%
Personal win rate
−21.0 pts vs community (60.1%).
68
Days active
23.0 games / year on average.
Charlie
Favorite investigator
Most-played character.
3
Longest win streak
Consecutive victories in a row.
4
Longest loss streak
Consecutive defeats in a row.

Career arc

Dec 13, 2016
first game
Feb 19, 2017
last game

Wins vs losses

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

N= 23

Best & worst Ancient One (n ≥ 5)

Net record

−5
9 victories minus 14 defeats. Below the 50/50 line by 10.9 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= 14

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= 23

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= 19

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= 17

Where they sit among peers

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

N= 716
22%ile

Still learning — outperforms 22% 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.

4.5eff. AOs

Career is a mix of favourites and occasional faces. Top foe: Shub-Niggurath (35% of all games). 8 unique AOs ever faced.

All Ancient Ones faced

Click a column header to sort.

N= 23
Ancient One Games Wins Win %
Shub-Niggurath 8 2 25.0%
Azathoth 6 3 50.0%
Cthulhu 3 0 0.0%
Yog-Sothoth 2 2 100.0%
Ithaqua 1 1 100.0%
Hastur 1 0 0.0%
Syzygy 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= 92

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= 101

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.

18.5eff. chars

Most-played: Charlie (10% 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= 23

Expansions mixed per game

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

N= 23

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

N= 21
43h
Roughly 42h 30m spent summoning horrors. Averaging 121 min per game (−50 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= 21

Quick wins or long grinds?

40min
Their victories average 144 min and defeats 104 min — their wins tend to be the longer grinds.

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= 22

Games per year

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

N= 23

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= 23

Records

Trophy case

Career bests and lifetime tallies.

6
Best score
1h 50m
Fastest victory
3h 20m
Longest game
4
Biggest team
39
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= 23

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= 23