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Pavlicic

Overview

58
Games logged
32 won, 26 lost.
55.2%
Personal win rate
−5.0 pts vs community (60.1%).
1,801
Days active
11.8 games / year on average.
Diana
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

May 15, 2014
first game
Apr 20, 2019
last game

Wins vs losses

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

N= 58

Best & worst Ancient One (n ≥ 5)

  • Best Azathoth — 83.3% (n=6)
  • Personal nemesis Cthulhu — 33.3% (n=6)
  • Unique AOs faced 16

Net record

+6
32 victories minus 26 defeats. Ahead of the 50/50 line by 5.2 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= 31

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= 58

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= 57

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= 45

Where they sit among peers

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

N= 716
45%ile

Below the median — outperforms 45% 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.

12.4eff. AOs

Career is spread across many Ancient Ones. Top foe: Yog-Sothoth (14% 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= 6
Ancient One Community loss % Personal wins Personal win %
Cthulhu 56% 2 / 6 33.3%

All Ancient Ones faced

Click a column header to sort.

N= 58
Ancient One Games Wins Win %
Yog-Sothoth 8 4 50.0%
Azathoth 6 5 83.3%
Hypnos 6 3 50.0%
Cthulhu 6 2 33.3%
Nephren-Ka 5 2 40.0%
Yig 4 1 25.0%
Hastur 4 2 50.0%
Abhoth 3 2 66.7%
Syzygy 3 3 100.0%
Shub-Niggurath 2 1 50.0%
Antediluvium 2 2 100.0%
Atlach-Nacha 2 1 50.0%
Nyarlathotep 2 1 50.0%
Rise of the Elder Things 2 2 100.0%
Shudde M'ell 2 1 50.0%
Ithaqua 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= 156

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= 143

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.

28.7eff. chars

Most-played: Charlie (6% of all games). 49 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= 55

Expansions mixed per game

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

N= 58

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= 246

Time & Activity

Time at the table

Across 58 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 58
123h
Roughly 123h 15m spent summoning horrors. Averaging 128 min per game (−44 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= 58

Quick wins or long grinds?

17min
Their victories average 135 min and defeats 118 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= 58

Games per year

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

N= 58

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= 58

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= 58

Records

Trophy case

Career bests and lifetime tallies.

11
Best score
45m
Fastest victory
4h 30m
Longest game
5
Biggest team
68
Monsters defeated
2
Investigators lost

How their games end

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

N= 58

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= 58