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PelonRb

Overview

17
Games logged
7 won, 10 lost.
41.2%
Personal win rate
−19.0 pts vs community (60.1%).
2,102
Days active
3.0 games / year on average.
Jacqueline
Favorite investigator
Most-played character.
1
Longest win streak
Consecutive victories in a row.
4
Longest loss streak
Consecutive defeats in a row.

Career arc

May 30, 2019
first game
Mar 1, 2025
last game

Wins vs losses

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

N= 17

Best & worst Ancient One (n ≥ 5)

  • Best Azathoth — 54.5% (n=11)
  • Personal nemesis Yog-Sothoth — 16.7% (n=6)
  • Unique AOs faced 2

Net record

−3
7 victories minus 10 defeats. Below the 50/50 line by 8.8 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= 17

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= 17

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= 17

Where they sit among peers

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

N= 716
25%ile

Below the median — outperforms 25% 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.

1.8eff. AOs

Career is spread across many Ancient Ones. Top foe: Azathoth (65% of all games). 2 unique AOs ever faced.

All Ancient Ones faced

Click a column header to sort.

N= 17
Ancient One Games Wins Win %
Azathoth 11 6 54.5%
Yog-Sothoth 6 1 16.7%

Investigators

Most-played investigators

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

N= 49

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= 44

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.

8.7eff. chars

Most-played: Jacqueline (18% of all games). 11 unique investigators ever fielded.

Expansions

Expansions mixed per game

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

N= 17

Time & Activity

Time at the table

Across 17 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 17
34h
Roughly 33h 40m spent summoning horrors. Averaging 119 min per game (−53 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= 17

Quick wins or long grinds?

9min
Their victories average 124 min and defeats 115 min — their wins tend to be the longer grinds.

Games per year

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

N= 17

Records

Trophy case

Career bests and lifetime tallies.

6
Best score
1h 27m
Fastest victory
3h 10m
Longest game
2
Biggest team
14
Monsters defeated
1
Investigators lost

How their games end

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

N= 17

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= 17