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If you want to estimate who will win an election without wagering, use several transparent signals rather than one odds feed: an average of high-quality polls, a poll projection, a vote-expectation survey, a fundamentals model, and carefully calibrated expert judgment. Combining forecasts and publishing uncertainty is generally more robust than relying on any single poll, model, panel, or prediction-market price.
What to use instead of election betting
Election betting reduces a complicated forecast to a tradable price. That price can be a useful comparison point, but it is still produced by a wagering market and can move with liquidity, trading incentives, and herd behavior. A non-wagering forecast can show its inputs, assumptions, error history, and uncertainty directly.
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The practical substitute is a forecast stack:
- Polling averages smooth noisy individual surveys.
- Poll projections translate current polling into an expected final vote share.
- Vote-expectation surveys ask who respondents think will win, not whom they support.
- Fundamentals models use a small, explicit set of structural predictors.
- Expert or Delphi panels add contextual interpretation, with their track record measured rather than assumed.
- Forecast combinations blend methods and weight them by calibration and comparable historical error.
These approaches answer different questions. A poll estimates current reported intention. A projection estimates an eventual result. An expectation survey captures respondents’ judgment about the likely winner. A fundamentals model asks what economic and political conditions usually imply. A panel supplies interpretation. Keeping those distinctions visible prevents a probability from being mistaken for a vote count or an official result.
Polling averages: the clearest starting point
Why one poll is not a forecast
A single poll is a noisy snapshot. Its result depends on the sample frame, mode (online, telephone, or another method), field dates, questionnaire wording, likely-voter screen, weighting choices, and random sampling error. Nonresponse and coverage problems can create additional error that a margin-of-error number does not capture.
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Use a rolling average of recent polls instead of treating the newest release as a definitive update. A sound aggregator records each poll’s field dates, sample size, population, mode, sponsor, and weighting method. It should avoid double-counting multiple releases from the same fieldwork and should disclose how it handles partisan “house effects.”
What to inspect in an average
- Recency: older surveys should receive less weight when the race is changing, but not so little that the estimate lurches with every release.
- Sample quality: check whether the sample represents registered voters or likely voters and whether the screen is consistent across polls.
- House effects: systematic differences among pollsters should be estimated from their prior performance rather than ignored.
- Uncertainty: look for an interval around the average and for an explanation of whether it includes correlated polling error.
- Geographic fit: national polling may not map cleanly to an electoral-college, parliamentary, ranked-choice, or two-round system.
Averages reduce idiosyncratic noise, but they do not automatically remove a shared bias. If most pollsters use the same flawed frame or miss a late shift, the average can be confidently wrong.
Poll projections: turning current data into an expected result
A projection starts with observed polls and estimates how those numbers may evolve by election day. It can model trend, undecided voters, turnout, historical relationships between polling and final results, and the calendar. The output should be an expected vote share or win probability accompanied by an uncertainty interval.
Questions to ask before trusting a projection
- What is the last date of polling included, and how are polls after that date treated?
- Does the model estimate turnout and undecided-voter allocation, or simply extrapolate the current margin?
- Is uncertainty simulated at the poll level, the state or district level, and across correlated errors?
- Does it account for the electoral system that converts votes into seats or electors?
- Are the model rules and historical backtests published so another reader can reproduce them?
In a study summarized by Gerd Graefe for Oxford University Press in 2014, combined poll projections had lower error than single polls, although performance varied by election. That result supports aggregation, not a guarantee for a particular race. A projection’s value depends on whether its assumptions fit the country, election type, and time horizon.
Vote-expectation surveys: ask who will win
Expectation surveys ask respondents which candidate or party they believe will win, regardless of their personal preference. Supporters may be biased about their own choice, but people often use information from conversations, local conditions, media coverage, and social networks when judging the likely winner. Aggregating those judgments can therefore provide a signal different from a standard vote-intention poll.
Across seven U.S. presidential elections from 1988 through 2012, the most recent expectation survey identified the winner 92% of the time, compared with 79% for the most recent single poll. Its expectation-based vote-share error was 51% lower than the error of single polls. These are historical, U.S.-specific averages, not a universal accuracy rate for current elections.
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The figures are reported by Gerd Graefe in an Oxford University Press study (2014): 92% average winner hit rate and 51% lower vote-share error than single polls. Graefe wrote that “vote expectation surveys provided more accurate forecasts of election winners and vote shares than four established methods.” The evidence does not mean expectations should replace polling. It means they can be an independent input, particularly when combined with other signals.
How to run an expectation survey responsibly
- Ask a neutral question such as “Which candidate do you think will win?” rather than implying that one answer is socially preferred.
- Record field dates and respondent geography; expectations in one state or district should not be generalized nationally.
- Report the share choosing each candidate and the share saying they do not know.
- Do not convert the response directly into a vote forecast without testing its historical relationship to outcomes.
Fundamentals-based quantitative models
Fundamentals models use a short list of predictors that are available before the campaign’s final phase. Common inputs include economic conditions, incumbent approval or popularity, and the number of terms or years the incumbent party has held the White House. A model can be a regression, a Bayesian specification, or another auditable statistical formulation; what matters is that the variables, training period, and update rules are explicit.
In the review summarized by Graefe, 34 of 39 forecasts from seven established models correctly predicted the winner across six U.S. presidential elections from 1992 through 2012. Vote-share error varied among the models and elections. The record demonstrates that a small structural model can be informative, not that fundamentals always beat late polling.
Where fundamentals help
- They provide a forecast before extensive campaign polling exists.
- They are less sensitive to a single news cycle or one unusual survey.
- Their assumptions can be inspected and backtested.
Where fundamentals fail
Models may miss election-specific shocks such as scandals, wars, financial crises, candidate withdrawals, major disasters, or unusual turnout mobilization. A model trained on a narrow set of U.S. presidential elections should not be applied unchanged to a parliamentary coalition contest or a country with a different economic and electoral structure. Treat the model as one component and widen the uncertainty when the current race differs materially from its training data.
Expert judgment and Delphi-style panels
Experts can interpret information that is difficult to encode: coalition negotiations, candidate quality, regional organization, institutional rules, or the meaning of an emerging scandal. A Delphi-style process asks several experts to make independent forecasts, exposes an anonymized summary, and allows revision. Independence matters; a panel that simply repeats the first confident voice is a discussion, not a useful aggregation.
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Expertise is not a substitute for measurement. In a peer-reviewed comparison covering forecasts from 2004 through 2016, 277 of 450 expert forecasts (62%) moved in the direction of the eventual result. However, 56% were farther from the outcome than the polling average. Weighted mean absolute error was 1.6 percentage points for experts versus 1.5 points for polling averages. The comparison shows why a panel’s historical calibration should be published alongside its narrative.
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Panel design that limits overconfidence
- Define the target: winner, national vote share, seat share, or probability above a stated threshold.
- Require each expert to submit a numeric probability or interval before discussion.
- Collect the reasoning and the information cutoff date.
- Aggregate forecasts using a transparent rule, such as a median or performance-weighted mean.
- Score every forecast after the result using a proper probability score and absolute error.
A panel can add value when it contributes genuinely different information. It can reduce accuracy when experts anchor on a fashionable narrative or converge on the same unsupported assumption.
Combining forecasts instead of choosing a single winner
Forecast combination is the most practical way to use heterogeneous evidence. Start with a polling average or projection, then add a fundamentals estimate, an expectation-survey result, and a calibrated panel forecast. The combination can be a simple equal-weight average or a weighted method based on out-of-sample performance in comparable elections.
Simple combination workflow
- Define one target. For example, probability that Candidate A wins the national popular vote, or expected seat share under a specified electoral system.
- Align the information date. Do not combine a model updated six months ago with a poll average ending yesterday without recording that difference.
- Put outputs on the same scale. Convert vote shares, seat estimates, and winner probabilities only through a documented model; do not average unlike quantities.
- Set baseline weights. Equal weights are a defensible starting point when the historical sample is small.
- Adjust for proven calibration. Increase weight for methods that performed well on similar elections and reduce weight for systematic bias or poor probability calibration.
- Preserve dependence. Polls, expert commentary, and market prices may all react to the same news, so averaging them does not make their errors independent.
- Publish a range. Show the combined estimate, confidence or credible interval, data cutoff, and the main assumptions.
The PollyVote research project periodically collected expert forecasts and compared them with polling and other methods, illustrating how a combination can be evaluated rather than asserted. The broader forecasting literature repeatedly finds combinations more robust than a single model or poll, especially when component errors are not perfectly correlated.
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| Method | Primary input | Typical output | Main advantage | Typical failure mode |
|---|---|---|---|---|
| Single poll | One sampled survey at a stated field date | Current vote intention | Fast, direct measurement | Sampling noise, nonresponse, house effects |
| Polling average | Several recent polls | Smoothed current margin | Less sensitive to one poll | Shared bias across pollsters |
| Poll projection | Polls plus trend, turnout, and historical error assumptions | Expected final vote share or win probability | Connects present data to election day | Misspecified trend, turnout, or correlated error |
| Expectation survey | Respondents’ belief about the winner | Winner likelihood or expected vote share | Captures information beyond stated preference | Small samples, social or partisan bias |
| Fundamentals model | Economic and political structural variables | Early vote-share or winner forecast | Auditable and available before late polling | Election-specific shocks and regime changes |
| Expert/Delphi panel | Specialist judgments and contextual information | Probability, margin, or scenario forecast | Interprets institutions and unusual events | Herding, narrative bias, overconfidence |
| Prediction-market price | Wagering trades | Market-implied probability | Continuous updating and incentive to find information | Liquidity, participant incentives, and herd effects |
| Combined forecast | Several methods with stated weights | Calibrated probability and interval | Diversifies model-specific errors | False precision or double-counting correlated inputs |
There is no universal ranking. Oxford’s review reports that forecast horizon matters: market precision tends to improve near Election Day, expert opinion is comparatively consistent, and statistical models may struggle to remain competitive. Those patterns describe historical comparisons, not a promise about a particular market or current race.
A decision framework for choosing a non-wagering forecast
If the election is months away
Start with fundamentals and long-run polling, then show a wide interval. At this horizon, a small change in one survey should not dominate the estimate. Use scenarios for economic deterioration, leadership changes, or a major candidate event rather than pretending the central estimate is precise.
If voting is several weeks away
Use a transparent polling average or projection as the anchor. Add an expectation survey and a fundamentals estimate as independent checks. Examine whether polls agree across modes, pollsters, and regions; disagreement is information about uncertainty, not a reason to select the most favorable poll.
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If voting is days away
Give greater weight to current, high-quality polls and turnout-specific evidence, while retaining a meaningful allowance for correlated polling error and late movement. A market-implied probability can be displayed as a comparison baseline, but it should not replace the documented forecast.
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Model the system directly. A national vote share may not determine the winner in an electoral-college system, a proportional system with thresholds, a ranked-choice count, or a two-round election. Use district, state, constituency, or round-specific data where the rules require it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge accuracy and uncertainty
Winner accuracy is only one measure. A forecast that says “60%” should be rewarded when the event occurs more often than a “55%” event, even if both predictions name the same winner. Use proper scoring rules for probabilities, mean absolute error for vote or seat shares, and calibration plots that compare predicted probabilities with observed frequencies.
- Calibration: among events forecast at 70%, does the event occur about 70% of the time over a suitable sample?
- Sharpness: does the forecast distinguish genuinely uncertain races from highly one-sided ones without becoming overconfident?
- Comparable backtest: does the evaluation use the same country, electoral system, forecast horizon, and information available at the time?
- Data vintage: are revisions and later information excluded from an earlier forecast?
- Interval coverage: do stated 80% intervals contain the outcome about 80% of the time?
Historical scores cannot eliminate uncertainty in a new race. They tell you how much trust to place in a method relative to alternatives under similar conditions.
Common mistakes and how to correct them
Treating a probability as a guarantee
A 70% forecast still implies a 30% chance of the other outcome. Report the probability and interval, not only the favored candidate.
Confusing national and constituency results
In systems where seats or electors are allocated geographically, a national poll can be an incomplete input. Use local polling and a seat-conversion model, and explain the conversion uncertainty.
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Mixing incompatible dates
Label every component with its latest data date. A stale model may be useful as a baseline but should not be presented as if it incorporated today’s information.
Double-counting the same signal
An expert panel may rely on the same polls that feed the aggregator, while a market reacts to the same headlines. Correlated inputs need lower combined weight or an explicit dependence adjustment.
Overfitting a short history
With only a handful of comparable elections, performance-weighting can magnify random success. Prefer simple weights, shrink extreme estimates toward a baseline, and publish sensitivity to alternative weighting rules.
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A forecast without inputs, dates, code or calculation rules cannot be audited. At minimum, publish the data cutoff, variables, weights, uncertainty method, and a change log.
A practical non-wagering forecast checklist
- Define the outcome and electoral system.
- Set the information cutoff and forecast horizon.
- Collect multiple polls and document sample, field dates, mode, likely-voter screen, and weighting.
- Calculate or obtain a polling average with an uncertainty interval.
- Add a poll projection if its turnout and trend assumptions are visible.
- Collect a vote-expectation measure and keep its historical, geographic limits attached.
- Run a fundamentals model with variables that were specified before seeing the current outcome.
- Invite independent experts to submit numeric probabilities before discussion.
- Combine the outputs with transparent weights and test sensitivity.
- Publish calibration, historical error on comparable elections, data dates, and the reasons the forecast could fail.
This process produces a forecast a reader can inspect and update without placing a wager. It also makes disagreement useful: when polls, fundamentals, expectations, and experts diverge, the spread identifies where uncertainty actually lies.
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