We Tested Polymarket Prices Against 6,776 Resolved Markets. Here's Where They Bend.

Prediction-market prices are good probability estimates on average — that is the standard result, and it held in our data. But 'good on average' leaves room for systematic, repeatable distortion, and the best-documented one is the favorite-longshot effect: low-probability outcomes trade above their true frequency and near-certain outcomes trade slightly below it.

A fair-value model, honestly defined, answers one question: given where this market is priced, where did markets priced like it historically resolve? That is a calibration question, and calibration questions have a standard scoring rule.

We assembled resolved Yes/No Polymarket markets with known outcomes — 6,776 after filtering for resolvability — and, as an experimental test, fit a calibration mapping from market price to resolved frequency and scored it on held-out markets. The scoring rule is the Brier score: mean squared error between stated probability and outcome, where lower is better. Contracts that ask whether something happens by a date are scored separately, by distance to the deadline, in the deadline-markets study.

Raw market prices scored approximately 0.0352. The calibrated mapping scored approximately 0.0319 — an improvement of roughly 9.6%. The improvement concentrates where theory says it should: at the probability extremes, where longshot enthusiasm and near-certainty discounting are strongest.

• Sample: 6,776 resolved Yes/No markets.
• Score: held-out Brier, 0.0352 → 0.0319.
• Improvement: ≈ 9.6% versus raw prices.
• Effect concentrated near the 0 and 1 extremes.
• Status: experimental.

The distance between a calibration result and a tradeable edge is wide, and it is worth walking through it explicitly. First, the improvement is an aggregate: it says the population of prices bends in a known way, not that any specific market is wrong today. Second, the largest dislocations sit at the extremes, where bid-ask spreads are widest relative to the gap — a two-cent theoretical dislocation inside a three-cent spread is not an opportunity, it is noise with good posture.

Third, capture requires timing and settlement: the market can stay 'dislocated' until resolution, and the honest benchmark for any live-edge claim is close-line value — did flagged prices systematically beat the closing line? That measurement is future work on our roadmap, and until it exists, the product treats every fair-value flag as watch context. Vultax presents every fair-value flag as context to investigate, never as a recommendation, and this article follows the same rule.

Used as designed, the fair-value watch is an attention allocator. A market flagged outside its calibrated band is a market worth understanding: check the liquidity, check the event room, check who is trading it and whether venues agree. Sometimes the price is stale; sometimes the flag is thin-category noise; sometimes the market simply knows something the base rates do not.

That triage — flag, then investigate structure — is the entire honest use of the model. Anyone promising more from a calibration curve is selling past their evidence.

For binary outcomes, Brier score = the average of (forecast probability − outcome)². Enter probability between 0 and 1, and outcome as 1 for Yes or 0 for No. Lower is better. A constant 50% forecast scores 0.25 on every resolved Yes/No question; that does not make 0.25 the right benchmark for every sample.

A worked example, not a market observation: a 70% forecast scores (0.70 − 1)² = 0.09 if Yes occurs, and (0.70 − 0)² = 0.49 if No occurs. One of each produces an average of 0.29. Use the Brier score calculator to score your own resolved forecasts and compare them with a constant reference.

A Polymarket Brier score is meaningful only with its sampling rules. Record the time of each price, the time remaining until resolution, the markets included, how ambiguous outcomes were treated and whether markets have equal weight. Correlated outcomes from one event should not be mistaken for independent evidence.

Polymarket's accuracy page describes snapshots at several horizons before resolution. Its headline score and this study's historical result are not a like-for-like contest without the same questions, horizons, outcome rules and weighting. Neither a low Brier score nor better calibration establishes profit after costs.

The published evidence on calibration has grown quickly. Kalshi's own August 2026 study over more than 2.2 million data points found no systematic bias in aggregate, forecasts improving with time, participation and volume, and markets with about $10,000 of volume or twenty traders already informative; its offered yardstick is the 0.10–0.15 Brier scores of skilled superforecasters. A 2026 academic study of 594 Kalshi macro markets reported an overall Brier of 0.0987, with federal-funds-rate contracts near-perfect (0.0001) and unemployment-rate contracts weakest (0.1302), and the one-hour horizon far better than the thirty-day one (0.0812 against 0.1587). Horizon, more than venue, decides the number.

Volume-weighted calibration inherits a problem this site has written about elsewhere. Columbia researchers flagged about a quarter of Polymarket's volume over three years as wash trading, peaking near 60% in December 2024 and highest in sports markets; a calibration curve weighted by volume gives those prints a vote. The weekly test on this page weights by volume for comparability and states the floor it uses; the bend it finds at the extremes should be read with that in mind.