The Duck Day Model — Explorer

47,028 blind predictions, 41 refuges, 34 seasons. Every curve here was predicted before the model saw that season.

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The verdict first — 34 blind seasons, graded

Each bar is one full season the model predicted before seeing any of it, for whatever view is selected on the left (a refuge, a region, or all 41 refuges pooled, hunter-weighted). Above zero: the model beat the naive baseline that season. Click a bar to load that season below and see the day-by-day curves behind it.

Walk-forward: predicted vs actual

The skill grid — where the model wins and loses

every refuge × season with ≥200 hunter-days · click a cell to load it above

The effect dials — build a day, ask the posterior

posterior-mean effects from the fitted Bayesian model · applied to the selected view's own average day

Multipliers combine the fitted posterior means: exp(Σ β·z) against a reference day (average conditions, clear sky, north wind, regular shoot day). The effects themselves come from the one model fit on everything — what follows your selection is the starting rate: the readout applies the multiplier to the hunter-weighted average of whatever refuge/region and seasons you've picked above. Amber dials are the water block — a real in-sample association that added no out-of-sample skill; they're shown so you can feel the size of the paradox, not so you can forecast with them. All effects associational, per the paper.