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MJK

Overview

281
Games logged
165 won, 116 lost.
58.7%
Personal win rate
−1.4 pts vs community (60.1%).
2,973
Days active
34.5 games / year on average.
Tommy
Favorite investigator
Most-played character.
7
Longest win streak
Consecutive victories in a row.
4
Longest loss streak
Consecutive defeats in a row.

Career arc

Mar 27, 2018
first game
May 17, 2026
last game

Wins vs losses

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

N= 281

Best & worst Ancient One (n ≥ 5)

  • Best Abhoth — 77.8% (n=18)
  • Personal nemesis Cthulhu — 27.8% (n=18)
  • Unique AOs faced 16

Net record

+49
165 victories minus 116 defeats. Ahead of the 50/50 line by 8.7 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= 281

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= 281

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= 281

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= 281

Where they sit among peers

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

N= 714
49%ile

Below the median — outperforms 49% 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.

16.0eff. AOs

Career is spread across many Ancient Ones. Top foe: Atlach-Nacha (7% 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= 18
Ancient One Community loss % Personal wins Personal win %
Cthulhu 56% 5 / 18 27.8%

All Ancient Ones faced

Click a column header to sort.

N= 281
Ancient One Games Wins Win %
Atlach-Nacha 19 12 63.2%
Yig 19 9 47.4%
Hastur 18 10 55.6%
Abhoth 18 14 77.8%
Ithaqua 18 10 55.6%
Shudde M'ell 18 10 55.6%
Shub-Niggurath 18 11 61.1%
Cthulhu 18 5 27.8%
Syzygy 18 12 66.7%
Hypnos 17 10 58.8%
Antediluvium 17 11 64.7%
Azathoth 17 10 58.8%
Nyarlathotep 17 9 52.9%
Rise of the Elder Things 17 13 76.5%
Yog-Sothoth 17 12 70.6%
Nephren-Ka 15 7 46.7%

Investigators

Most-played investigators

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

N= 355

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= 447

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.

54.9eff. chars

Most-played: Hank (2% 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= 280

Expansions mixed per game

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

N= 281

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= 2,240

Time & Activity

Time at the table

Across 3 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 3
2h
Roughly 1h 50m spent summoning horrors. Averaging 37 min per game (−134 min vs the community's 171 min).

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= 281

Games per year

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

N= 281

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= 281

When they play

Every logged game on a day × hour grid (UTC). Darker cells are busier slots. The strip on top totals games by hour of day; the strip on the right totals them by weekday. Hover any cell for win rate and average length.

N= 281

Records

Trophy case

Career bests and lifetime tallies.

30m
Fastest victory
50m
Longest game
4
Biggest team
477
Monsters defeated
68
Investigators lost

How their games end

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

N= 281

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= 281