Q beats Polymarket on 56.7% of markets.

Q beats a market when its forecasts have a lower average Brier score than Polymarket’s prices at the same times.

Resolved Polymarket markets · Last 60 days · Updated September 11, 2026

All Polymarket markets

Every covered, resolved market. Each market counts once.

Markets Q beats
56.7%417 of 736 markets
Q average Brier
0.1699Lower is better
Polymarket average Brier
0.1726Prices at forecast time

3,685 forecasts · 1 tied market

Geopolitics and global elections

Markets tagged geopolitics or global elections.

Markets Q beats
50.3%79 of 157 markets
Q average Brier
0.1374Lower is better
Polymarket average Brier
0.1370Prices at forecast time

1,190 forecasts

Everything else

All covered, resolved Polymarket markets outside geopolitics and global elections.

Markets Q beats
58.4%338 of 579 markets
Q average Brier
0.1787Lower is better
Polymarket average Brier
0.1823Prices at forecast time

2,495 forecasts · 1 tied market

Largest topics by market count

  • Equities 241
  • AI & tech 104
  • Politics 102

A market can appear in more than one topic.

How we measure accuracy

Brier scores measure how close a forecast probability is to the outcome. Lower is better. We compare Q with Polymarket’s price recorded at the time of each forecast.

We average the forecast scores within each market, then weight every market equally. Q beats a market when its average score is lower. Ties stay in the total but do not count as wins.

The percentage shows how often Q has the better score. Average Brier shows the size of the errors, so a small win and a large miss affect the two measures differently.

Scoring details

Each Brier score is the squared difference between the forecast probability and the outcome, with YES scored as 1 and NO as 0. We include forecasts made in the last 60 days on markets marked closed or resolved, with a final YES price above 99% or below 1%. Open markets and other venues are excluded.

Download forecast data

One row per forecast, with Q’s probability, Polymarket’s price, and the outcome.

Q turns any question into a calibrated probability.

  1. 01Classify the questionResolution rule, horizon, and question type.
  2. 02Map the landscapeActors, assets, linked markets, and related questions.
  3. 03Research the evidenceCheck the sources behind each claim.
  4. 04Build scenariosBase rates, pathways, dependencies, and disconfirming evidence.
  5. 05Produce the forecastA calibrated probability with uncertainty and cited analysis.

Signal selection

Q generates many forecasts. The best ones become Signals. Q curates markets where it sees a large spread and short-term catalysts that it believes will move the market in its direction.

  1. 500forecasts / day
  2. 100spread > 10pp
  3. 50meaningful liquidity
  4. 10resolve within a week

Q gets better over time.

Learning loop

Score each outcome

Compare Q and the forecast-time market price with the resolved result.

Diagnose the error

Trace the evidence, expert weights, and missing inputs behind the miss.

Feed the next model

Turn repeatable findings into fine-tuning and data changes.

Resolve · score · fine-tune · forecast again