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Neither polls nor prediction markets is universally better at forecasting elections. They answer different questions. A poll estimates what a defined group says it prefers or intends to do when questioned. A prediction-market price reflects traders’ beliefs about a specified future result, such as a candidate winning. To judge which is more accurate, compare the same race, outcome, date, forecast method and performance measure.
Historical studies sometimes give markets an edge, particularly early in a campaign or when a contest is genuinely uncertain. Other evidence finds performance varies by forecast horizon and market conditions. Both approaches have missed major outcomes, so neither a poll margin nor a 90% market price should be treated as a guarantee.
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Polls and prediction markets measure different things
The most important distinction is the object being measured. A conventional election poll asks sampled people a question such as “Who would you vote for today?” or “Which candidate do you favor?” The result is an estimate of opinion in the poll’s defined population during the period when interviews were conducted. It is a snapshot tied to a question, sample, field dates and weighting choices.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A prediction market lists contracts whose payoff depends on a future event. In a simple binary contract, a holder receives a fixed amount if a candidate wins and zero if the candidate loses. The trading price aggregates participants’ beliefs about that event. If a contract trades at 65 units on a 0-to-100 scale, traders are signaling a belief around a 65% chance, but the price is not automatically the population’s true probability. Fees, limited liquidity, risk preferences and recurring trading biases can move prices away from a perfectly calibrated probability.
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Professor Leighton Vaughan Williams summarized the distinction in written evidence to the UK House of Lords Select Committee on Political Polling and Digital Media: “Pollsters, except perhaps with their final polls, tend to claim that they are not producing a forecast, but a snapshot of opinion. In contrast, the betting markets are generating odds about the final result.”
That difference makes a direct comparison tricky. A poll reporting 48% for one candidate and 45% for another is not equivalent to a market pricing that candidate at 65% to win. The first is a vote-intention estimate; the second is an outcome probability. A model must translate polling information into a probability before the two forecasts can be evaluated on the same scale.
What a poll can—and cannot—tell you
A time-bounded estimate of stated opinion
Poll results describe answers collected during a field period. They depend on who was sampled, how respondents were contacted, the wording and order of questions, weighting, turnout assumptions and the treatment of undecided respondents. Even a high-quality poll is an estimate with sampling and non-sampling error.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Raw vote-share numbers also do not provide a probability of victory by themselves. A two-point lead could imply a comfortable win in a low-error environment or a toss-up when uncertainty is wide. To produce a forecast, analysts generally combine the poll estimate with an error model, other polls, state or district structure, turnout assumptions and the remaining time before voting.
Why a poll is not automatically an election forecast
“Who would you vote for today?” asks about current intention, not necessarily behavior on election day. Voters can change their minds, candidates can leave the race, events can alter turnout and the composition of the electorate can differ from the poll sample. A final poll taken immediately before voting has a shorter gap to the outcome, but it still carries uncertainty.
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Poll averages reduce the influence of any single survey’s sampling error, yet an average can inherit shared problems when pollsters use similar methods or miss the same kinds of voters. A polling average is still not a market probability unless a forecasting model supplies that conversion.
How a prediction market produces a forecast
Prices aggregate dispersed information
Traders may use polls, campaign reporting, statistical models, fundraising data, local knowledge, historical patterns and their own interpretation of events. When they buy and sell contracts, those views are combined into a changing price. In that sense, a market can incorporate polling rather than serve as an entirely independent alternative.
The 2009 Public Opinion Quarterly analysis of the 2008 US presidential and Senate cycle argued that market prices could absorb public information faster than polling results accumulated. A trader can react immediately to a debate, endorsement or report; a survey must be designed, fielded, weighted and published.
Why a price is not a guaranteed probability
Market signals are affected by transaction costs, liquidity and the incentives of participants. A thinly traded contract may move sharply on a small order or fail to reflect new information. Traders may also prefer a position for reasons unrelated to expected value, and markets can exhibit favorite-longshot bias or other systematic pricing errors. These features mean that a 90% price should be read as a strong but uncertain forecast, not as a 90% promise.
The market’s definition matters as well. A contract may specify a national popular-vote winner, an electoral-vote winner, a parliamentary majority, a referendum result or another settlement rule. Two prices that appear to concern the same candidate can refer to different outcomes.
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The evidence: what comparative studies found
| Evidence | What it examined | Main result | Important qualification |
|---|---|---|---|
| Public Opinion Quarterly analysis (2009) | US presidential and Senate races during the 2008 election cycle; polls compared with Intrade prices | Raw prices were not automatically superior. After adjustment, market-based forecasts were more accurate and informative in that study, especially early in the cycle and in uncertain races. | The result applies to that sample, period and adjustment method, not to every election or platform. |
| Large-sample US elections study | A broad set of US elections, comparing forecast performance over different horizons | Prediction markets appeared to provide the most precise forecasts and had bias similar to polls. Market forecasts tended to improve as election day approached, while polls performed worse in that study. | Forecast horizon and the study’s data and methods affect the comparison. |
| House of Lords written evidence (2018), reporting the 2015 Irish referendum | Spread-betting market midpoint, final-poll average and the actual Yes result | The market midpoint was 60% Yes, the final-poll average was 71% Yes, and the result was 62% Yes. The market was closer in this contest. | One referendum cannot establish a general market advantage. |
| House of Lords written evidence (2018), historical cases | UK and US political outcomes including the 2015 UK general election, 2016 UK EU referendum, 2016 US presidential election and 2017 UK general election | Both polling and betting-market forecasts were surprised by consequential outcomes. | The examples show shared vulnerability to shocks rather than a universal ranking. |
The 2008 Public Opinion Quarterly data included observations in which candidates priced above a 90% probability still lost in 0.27% of the observations described as certain. That figure is specific to the study’s data and definition; it is not a calibration statistic for current markets.
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Vaughan Williams’s committee evidence also described a historical exchange dataset containing 411,858 contract observations from 2008. The size of that dataset shows the scale of one analysis, not that market prices were accurate in every observation.
A documented disagreement: the 2015 Irish referendum
The Irish same-sex-marriage referendum illustrates why the underlying numbers must be identified before declaring a winner. According to the parliamentary evidence, the spread-betting market’s midpoint was 60% for Yes. The average of final opinion polls was 71% Yes. The actual result was 62% Yes.
On a simple percentage-point comparison, the market was two points from the result and the poll average was nine points away. The example is useful because the methods diverged materially and the market happened to be closer. It does not show that markets are always better: a different contest, sample or settlement rule could produce a different ordering.
Why the forecasts diverge
Timing and information flow
A poll freezes answers from a defined field period. A market can reprice continuously as traders respond to new information. This can give a market an earlier reaction to events, while a poll average may take days or weeks to reflect them. The advantage can disappear if trading is sparse or participants fail to update their positions.
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Different targets
Polls commonly estimate vote intention or vote share. Markets commonly estimate the chance of a named outcome. Comparing a vote-share lead with a win probability mixes targets. A model must account for uncertainty in the vote-share estimate, the electoral system and the distribution of outcomes before a poll can be expressed as a win probability.
Information aggregation
Market participants can process public polls and add other information. A market price may therefore be a synthesis of polls, models, reporting and local observations. Calling it an alternative to polling can overstate the independence of the signal.
Different biases and frictions
Polls face sampling error, nonresponse, weighting and turnout-model risk. Markets face transaction costs, liquidity limits, risk preferences and pricing biases such as favorite-longshot effects. Neither method is a frictionless measurement instrument.
Forecast horizon and uncertainty
The relative performance of the methods can change as voting approaches. The 2008 comparison found an adjusted market advantage particularly early in the cycle and in uncertain contests. The broader US study likewise found that the forecast horizon mattered, with market forecasts improving nearer election day. These findings do not imply that every market improves monotonically or that every poll deteriorates; they show why the date attached to a number is essential.
Sticky prices and common surprises
The House of Lords evidence raised the possibility that market prices can become “sticky,” failing to respond fully to new information. The same evidence records major outcomes that surprised both pollsters and betting markets. A market can react faster in principle and still miss an event if participants discount it, lack liquidity or share the same mistaken assumptions as pollsters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare a poll with a market forecast
Use the following sequence whenever two numbers are presented as competing election forecasts:
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- Match the contest and settlement rule. Confirm that both figures concern the same race, jurisdiction and outcome. A national vote-share poll cannot be compared directly with a market for an electoral-vote winner or a parliamentary majority.
- Record the dates. Note the poll’s field dates, publication date and the market price’s timestamp. A market quote from after a debate is not comparable with a poll conducted before it.
- Identify the target. Label each number as vote intention, vote share, a candidate’s chance of winning or another event probability. Do not call a raw poll lead a probability.
- Check aggregation and adjustment. Determine whether the poll is a single survey, an average or part of a model. For a market, check the contract wording, price range, trading volume and whether the quoted figure is a midpoint or last trade.
- Put uncertainty on both numbers. Look for the poll’s margin of error and design effects. For a market, consider liquidity, spread and the possibility that price biases or participant incentives distort calibration.
- Choose the performance measure. Decide whether you are evaluating closeness to the eventual vote share, winner calls, probability calibration, average error or precision. A forecast can be precise but biased, or unbiased on average but wide and uninformative.
- Account for the horizon and race type. Compare early-cycle forecasts with other early-cycle forecasts and final-week forecasts with final-week forecasts. Note whether the contest was highly uncertain, because historical results indicate that uncertainty changes the relative performance.
- Separate one example from a pattern. A market beating a poll average in one referendum, or a poll missing one election, cannot establish a general rule. Use a large, appropriately matched sample before making a broad claim.
Accuracy, bias and precision are different tests
Accuracy
Accuracy asks how close a forecast came to the eventual outcome or whether it identified the winner. For a probability forecast, a 60% prediction that wins is not automatically better than a 90% prediction that wins; the evaluation should consider the full set of forecasts and outcomes.
Bias
Bias is a systematic tendency to lean high or low. A polling method might consistently overstate one side’s support. A market might systematically overprice favorites or long shots. A method can have low average bias while still making large individual errors.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Precision and calibration
Precision describes how concentrated or decisive forecasts are. A market that moves from 50% to 90% is more decisive than one that remains near 55%, but decisiveness is useful only if the probabilities are calibrated. In a calibrated set, events assigned roughly 70% should occur about 70% of the time over many comparable forecasts. The historical 0.27% statistic from the 2008 study is not a universal calibration estimate and should not be repurposed as one.
For polls, a narrow sampling margin does not capture every source of error, especially shared nonresponse or turnout-model problems. For markets, a narrow bid-ask spread does not prove that the probability is correct; it may only show active trading around a consensus.
What “more accurate” should mean for a reader
If you want to know current public preference, use a poll designed for the population and question you care about. If you want a continuously updated estimate of the chance of a defined result, a market provides that type of signal. If you want the strongest forecast, inspect a model that explains how it combines polls, markets and other evidence instead of assuming that either raw input is sufficient.
For an early campaign, market prices may incorporate news before a polling average catches up, and the 2008 comparison found an adjusted market advantage in that setting. Near election day, both approaches can improve or degrade depending on sampling, liquidity and new events; the broader US study found markets improving with proximity while polls performed worse in its sample. Treat those as historical findings, not promises about a current election.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA high probability remains a statement about uncertainty. The record of shocks in the 2015 UK general election, the 2016 UK EU referendum, the 2016 US presidential election and the 2017 UK general election demonstrates that polling and markets can be wrong together. The sensible conclusion is not to pick a permanent winner, but to compare like with like and preserve the uncertainty each method actually carries.
A practical checklist for reading election numbers
- What exact event does the number predict?
- When were the underlying answers collected or the market price recorded?
- Is the figure raw, averaged or model-adjusted?
- Does it represent a share, a margin or a probability?
- How much sampling error, nonresponse risk or market illiquidity could remain?
- Is the comparison based on a matched historical sample rather than a single anecdote?
- Which metric—error, winner calls, bias, calibration or precision—supports the claim of superiority?
Those checks prevent the most common category error: treating a snapshot of stated preference and a price on a future event as if they were interchangeable measurements.
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