How is this calculated?
This page is for the curious — the technical details behind the numbers on the rest of the site. The short version: we smooth win rates toward the community average so investigators with very few games don't show up at the top or bottom by accident, and we show honest error ranges around those rates. Cells with fewer than 5 games are hidden entirely; cells with 5–9 games are dimmed as a reminder that the smoothing is doing most of the work. If you want the formulas, read on.
Ancient One difficulty
We compute the loss rate (defeats divided by total games) for each Ancient One that has at least 5 games logged. The error bars are 95% Wilson score intervals — well-behaved on small samples, unlike the naïve normal approximation. The list is sorted hardest (highest loss rate) first.
Investigator × Ancient One
For each (investigator, Ancient One) pair, the raw win rate is just wins / games. The shrunk win rate uses a Beta(α, β) prior centered on the global win rate, with prior strength of 10 — i.e. each cell is pulled toward the mean as if 10 extra games at the global average had been added. Cells with fewer than 5 games are omitted entirely.
Shrinkage, visually
The leaderboards and tier list use shrunk win rates, computed against a community-mean prior with strength 10. The plot below shows what that actually does to each investigator's number. Investigators with thousands of games barely move; those with only the 30-game floor get yanked toward the middle.
Shrinkage in action
Each dot is an investigator. The diagonal would mean 'no shrinkage'; the horizontal line at the community mean shows the pull. Small-sample investigators (smaller dots) get yanked toward the middle.
Calibration check
A good shrunk estimate should match observed reality in aggregate: if we bucket cells by their predicted (shrunk) win rate, the observed (raw) average inside each bucket should fall close to the diagonal. Systematic deviation would indicate the prior is biased.
Calibration of shrunk win rates
Within each bucket of predicted (shrunk) win rate, we plot the actual observed win rate. Points on the diagonal = well-calibrated. Systematic deviation = the model over- or under-predicts.
Doom-track distribution
For each Ancient One we build a histogram of the final doom-track value across all games where it was reported. The ridgeline view normalizes each AO's histogram so the densities are comparable across rows. Bimodal shapes (mass near 0 and near 15) indicate a "swingy" AO where games end decisively in either direction; smooth unimodal shapes indicate predictable pacing.
Source
The underlying spreadsheet is maintained by the Eldritch Horror community. We fetch the raw submissions tab once a day, normalize, and rebuild.