vagos
Overview
Career arc
Wins vs losses
A quick visual tally of this contributor's career so far.
Best & worst Ancient One (n ≥ 5)
Net record
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.
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.
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.
Where they sit among peers
Rank against every other contributor with at least 5 games logged. Higher is better.
Still learning — outperforms 11% 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.
Career is spread across many Ancient Ones. Top foe: Cthulhu (35% of all games). 7 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.
| Ancient One | Community loss % | Personal wins | Personal win % |
|---|---|---|---|
| Cthulhu | 56% | 1 / 7 | 14.3% |
All Ancient Ones faced
Click a column header to sort.
| Ancient One | Games | Wins | Win % |
|---|---|---|---|
| Cthulhu | 7 | 1 | 14.3% |
| Shub-Niggurath | 4 | 1 | 25.0% |
| Azathoth | 2 | 0 | 0.0% |
| Yig | 2 | 2 | 100.0% |
| Rise of the Elder Things | 2 | 0 | 0.0% |
| Yog-Sothoth | 2 | 1 | 50.0% |
| Ithaqua | 1 | 1 | 100.0% |
Investigators
Most-played investigators
Where this contributor spends their time on the roster. Sorted by total games with each character.
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.
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.
Most-played: Jacqueline (13% of all games). 23 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.
Expansions mixed per game
How many expansion boxes this contributor typically combines in a single game.
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.
Time & Activity
Time at the table
Across 18 timed games. Game length is self-reported; the rare sub-30-minute entries (data slips) are excluded.
Game-length distribution
How long this contributor's games run, in minutes. The visible range clips a few long outliers.
Quick wins or long 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.
Games per year
This contributor's logged games by calendar year — the seasons when they were most active.
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.
Records
Trophy case
Career bests and lifetime tallies.
How their games end
The split of victory types (green) and defeat causes (red) across this contributor's games.
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.