Quantitative#087

Brier-Score Calibration Harvesting

Across thousands of resolved markets, check whether prices are accurate on average — for example, do 90-cent favorites actually win 90% of the time, or only 87%? Where a whole price band is consistently mispriced, you take the favorable side across every market in that band, not on any single opinion. The edge is a persistent, statistically measurable calibration bias in a price range, harvested in bulk so individual outcomes wash out.

What you need to run it

Large resolved-market dataset with implied price at horizons
Reliability/calibration binning and Brier decomposition
Bucket-level position sizing across many markets
Ongoing recalibration as the bias decays

Where this applies

Markets on Polymarket where brier-score calibration harvesting is the natural play:

Will the heavy favorite (>85c) win each upcoming Senate race?
Will the top seed win each NBA first-round playoff series this postseason?
Will each strong-favorite incumbent (>90c) retain their House seat in 2026?

Capabilities this demands

Model / quantData ingestionRisk managementPatience

At a glance

CategoryQuantitative

Requirements4

CapabilitiesModel / quant, Data ingestion, Risk management, Patience

VenuePolymarket (CLOB, Polygon)

Build it

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This is documentation, not advice. Poly Research & Robotics publishes how these strategies work because the method should be checkable — not as a recommendation to trade them. See the full strategy database (147 strategies) or the data resources directory.