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RadSquad

Overview

36
Games logged
23 won, 13 lost.
63.9%
Personal win rate
+3.8 pts vs community (60.1%).
1,034
Days active
12.7 games / year on average.
Diana
Favorite investigator
Most-played character.
4
Longest win streak
Consecutive victories in a row.
2
Longest loss streak
Consecutive defeats in a row.

Career arc

Jul 3, 2017
first game
May 2, 2020
last game

Wins vs losses

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

N= 36

Best & worst Ancient One (n ≥ 5)

  • Best Hastur — 83.3% (n=6)
  • Personal nemesis Azathoth — 66.7% (n=9)
  • Unique AOs faced 9

Net record

+10
23 victories minus 13 defeats. Ahead of the 50/50 line by 13.9 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= 15

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= 36

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= 36

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= 32

Where they sit among peers

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

N= 716
61%ile

Above the median — outperforms 61% 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.

7.0eff. AOs

Career is spread across many Ancient Ones. Top foe: Azathoth (25% of all games). 9 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= 2
Ancient One Community loss % Personal wins Personal win %
Cthulhu 56% 1 / 2 50.0%

All Ancient Ones faced

Click a column header to sort.

N= 36
Ancient One Games Wins Win %
Azathoth 9 6 66.7%
Hastur 6 5 83.3%
Ithaqua 4 4 100.0%
Yog-Sothoth 4 4 100.0%
Shub-Niggurath 3 1 33.3%
Syzygy 3 1 33.3%
Yig 3 0 0.0%
Rise of the Elder Things 2 1 50.0%
Cthulhu 2 1 50.0%

Investigators

Most-played investigators

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

N= 94

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= 112

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.

21.3eff. chars

Most-played: Diana (10% of all games). 29 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= 36

Expansions mixed per game

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

N= 36

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= 123

Time & Activity

Time at the table

Across 1 timed game. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.

N= 1
3h
Roughly 3h spent summoning horrors. Averaging 180 min per game (+9 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= 35

Games per year

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

N= 36

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= 36

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= 36

Records

Trophy case

Career bests and lifetime tallies.

3h
Fastest victory
3h
Longest game
6
Biggest team
23
Monsters defeated
3
Investigators lost

How their games end

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

N= 36

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= 36