On this page (6 sections)
Content reviewed: 2026-10-13. Examples are hypothetical.
Poll results and market prices measure different things. A poll estimates support in a surveyed population; a prediction contract pays on a specified event such as winning an election. A candidate polling at 52% does not therefore have a 52% chance of winning.[1]
Start with matching definitions
Record the poll's population, field dates, question wording and geographic scope. Check whether it covers all adults, registered voters or likely voters. Then read the market's exact condition: nomination, popular vote, a particular jurisdiction or the eventual winner.
A national vote-share estimate is not interchangeable with the probability of winning an electoral system that aggregates local contests. A forecast must translate the poll evidence into the event named by the contract.
Account for more than sampling error
Pew explains that the reported margin of sampling error does not capture every source of polling error. Weighting, nonresponse and turnout assumptions also matter.[1] Do not read a narrow lead as certainty or treat a small change between polls as necessarily meaningful.
For a hypothetical illustration, a model might estimate a candidate's final margin as a normal distribution with mean two percentage points and standard deviation four points. That model gives a probability of a positive margin of about 69.15%. The result depends on the assumed distribution and uncertainty; it is not obtained by calling 52% support a 52% win chance.
Avoid counting the same information twice
Market participants can see public polls. Averaging a poll-based forecast with the market price does not create two independent sources of evidence. Compare timestamps and ask what information entered each estimate. Repeated polls from one organization or overlapping field periods can also share errors.
Keep your own model output separate from the market-implied value. If you blend them, record the rule before evaluating outcomes and test it on data not used to choose the weights. A weight selected because it looks good in retrospect can overstate performance.
Evaluate probability quality
Use proper scoring rules and calibration checks across many comparable forecasts.[2] A single surprising election does not establish that a model was wrong to assign the winner a modest probability. Conversely, a correct favorite does not prove a 99% forecast was justified.
Translate the comparison into a tradable question
Even a well-supported probability must be compared with the executable offer, fees and available size. Polymarket's displayed price can be a midpoint or last trade.[3] If the plausible forecast range overlaps the all-in cost, recording the disagreement and waiting for better evidence may be more useful than forcing a trade.
References
- Andrew Mercer, Pew Research Center. Understanding the Margin of Error in Election Polls. September 8, 2016.
- Tilmann Gneiting and Adrian E. Raftery. Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association 102(477), 359-378, March 2007. DOI: 10.1198/016214506000001437.
- Polymarket Documentation. Prices & Orderbook. Accessed October 13, 2026.