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Jax

Overview

140
Games logged
133 won, 7 lost.
95.0%
Personal win rate
+34.9 pts vs community (60.1%).
1,238
Days active
41.3 games / year on average.
Jacqueline
Favorite investigator
Most-played character.
58
Longest win streak
Consecutive victories in a row.
2
Longest loss streak
Consecutive defeats in a row.

Career arc

May 8, 2020
first game
Sep 28, 2023
last game

Wins vs losses

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

N= 140

Best & worst Ancient One (n ≥ 5)

  • Best Nephren-Ka — 100.0% (n=13)
  • Personal nemesis Cthulhu — 75.0% (n=8)
  • Unique AOs faced 16

Net record

+126
133 victories minus 7 defeats. Ahead of the 50/50 line by 45.0 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= 136

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= 140

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= 140

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= 140

Where they sit among peers

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

N= 718
94%ile

Top-quartile player — outperforms 94% 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.

14.8eff. AOs

Career is spread across many Ancient Ones. Top foe: Nephren-Ka (9% of all games). 16 unique AOs ever faced.

Toughest AOs they've beaten

Ancient Ones where the community loses at least 55% of the time — and this contributor has carved out at least one win.

N= 8
Ancient One Community loss % Personal wins Personal win %
Cthulhu 55% 6 / 8 75.0%

All Ancient Ones faced

Click a column header to sort.

N= 140
Ancient One Games Wins Win %
Nephren-Ka 13 13 100.0%
Syzygy 12 10 83.3%
Atlach-Nacha 11 11 100.0%
Hastur 11 11 100.0%
Rise of the Elder Things 11 10 90.9%
Azathoth 10 10 100.0%
Ithaqua 10 10 100.0%
Shudde M'ell 9 9 100.0%
Cthulhu 8 6 75.0%
Yig 8 8 100.0%
Yog-Sothoth 8 7 87.5%
Abhoth 7 7 100.0%
Hypnos 7 7 100.0%
Shub-Niggurath 6 5 83.3%
Antediluvium 5 5 100.0%
Nyarlathotep 4 4 100.0%

Investigators

Most-played investigators

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

N= 492

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= 329

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.

44.3eff. chars

Most-played: Jacqueline (4% of all games). 55 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= 139

Expansions mixed per game

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

N= 140

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= 759

Time & Activity

Time at the table

Across 135 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 135
600h
Roughly 599h 50m spent summoning horrors. Averaging 267 min per game (+95 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= 135

Quick wins or long grinds?

27min
Their victories average 267 min and defeats 240 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= 140

Games per year

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

N= 140

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= 140

When they log games

Every logged game on a day × hour grid, by the time it was submitted (in the form's own timezone, not this contributor's). Darker cells are busier slots. The strip on top totals games by hour, the strip on the right by weekday. Hover any cell for win rate and average length.

N= 140

Records

Trophy case

Career bests and lifetime tallies.

-28
Best score (lowest win)
1h 30m
Fastest victory
8h
Longest game
8
Biggest team
208
Monsters defeated
20
Investigators lost

How their games end

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

N= 140

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= 140