How Accurate Are Prediction Markets?

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Prediction markets are often described as forecasting tools that turn the collective judgment of participants into probabilities. When a contract trades at a price implying a 70% chance of an event occurring, that price provides a measurable estimate of how likely the market considers the event to be.

But how accurate are prediction markets in practice?

The answer depends on what is being predicted, how far away the event is, how much information is available, and how accuracy is measured. Research has found that prediction markets can produce useful forecasts, but their performance varies across settings. A market price is an estimate, not a guarantee, and a single correct prediction does not establish that a market is consistently reliable.

What Does Prediction Market Accuracy Mean?

Prediction market accuracy is the degree to which market-implied probabilities correspond to actual outcomes over time.

That definition matters because a prediction market does not always make a simple claim that an event will or will not happen. Many contracts express a probability through their trading price. Evaluating that probability requires more than checking whether the most likely outcome eventually occurred.

Consider a market that assigns an event a 70% probability. If the event happens, the market’s favored outcome occurred. If it does not happen, the event was still assigned a 30% chance of failure. Either result is compatible with the original forecast.

The meaningful question is whether forecasts at similar probability levels perform as expected across a sufficiently large collection of resolved events.

Calibration: Do Market Probabilities Match Reality?

Calibration is one of the most useful ways to evaluate probabilistic forecasts.

A forecasting system is well calibrated when events assigned a particular probability occur at approximately that frequency over many comparable forecasts.

For example, imagine a collection of 100 independent events, each assigned a 70% probability by a forecasting system. If approximately 70 of those events occur, the forecasts are well calibrated at that probability level. If only 50 occur, the system has overstated the likelihood of those events in that sample. If 90 occur, it has understated it.

These examples illustrate calibration in a group of forecasts, not a requirement that every batch produce an exact match. Random variation is normal, especially in smaller samples.

Calibration also does not tell the whole story. A system that assigns nearly every event a probability close to the overall base rate might be calibrated while doing little to distinguish likely outcomes from unlikely ones. Analysts should therefore consider both calibration and how effectively forecasts distinguish between events with different chances of occurring.

What Research Says About Prediction Market Accuracy

Research generally supports the idea that prediction markets can aggregate information effectively, but the evidence does not establish that every market is accurate in every situation.

A 2008 study published in the International Journal of Forecasting compared prediction market forecasts with hundreds of election polls across five U.S. presidential elections. The authors found that market forecasts were closer to the eventual outcome more often than the polls examined, including when forecasts were made well ahead of election day. That finding applies to the particular elections and methods studied; it is not proof that markets outperform polls in every context.

A 2016 study in Management Science compared prediction markets with prediction polls in a geopolitical forecasting tournament involving more than 2,400 participants and 261 events. Market prices outperformed the simple average of individual poll forecasts in the study. However, teams using statistical aggregation, adjustments for past performance, and recalibration performed better than the markets in that comparison. The results show that performance depends partly on how forecasts are collected and combined.

More recent research has examined market calibration directly. An August 2026 study by Kalshi researchers analyzed more than 2.2 million resolved markets on the platform across 11 categories. The authors reported that probabilities were more closely calibrated as markets approached resolution, with aggregate Brier scores improving from approximately 0.08–0.09 at a three-month horizon to around 0.02 at market close, excluding a category the study classified as exotic.

That result is notable, but its scope matters. The study was authored by researchers affiliated with the platform whose markets were analyzed. Its results describe that dataset and methodology, not every prediction market or every future event. Independent replication and comparisons across platforms and event categories remain important when drawing broader conclusions.

Taken together, the research supports a measured conclusion: prediction markets can provide informative probabilistic forecasts, but their accuracy depends on the market, the forecast horizon, the participants, and the evaluation method.

Why Accuracy Changes as an Event Approaches

A probability estimate can change as new information becomes available. That does not automatically mean the earlier forecast was inaccurate.

Suppose a market assigns a 40% probability to an event three months before it is scheduled to occur. Later, new evidence emerges and the market probability rises to 75%. The two prices represent estimates made at different times using different information.

To evaluate accuracy fairly, analysts must specify when the forecast was recorded. A market’s closing probability cannot be used as though it were the probability available months earlier.

Research on prediction markets has found that calibration can vary with time to expiration. The large 2026 Kalshi study described above reported substantially better aggregate calibration closer to resolution than at its three-month horizon. Earlier academic research has also documented differences in pricing biases across forecast horizons.

Several factors can explain why forecasts change:

  • New information: Reports, data releases, injuries, announcements, and other developments can change the evidence available to participants.
  • Uncertainty: Events further in the future may depend on more unknown variables.
  • Participation: More informed or active trading can help incorporate information, although participation alone does not guarantee accuracy.
  • Market structure: Fees, liquidity, contract design, and trading restrictions can affect the relationship between a quoted price and an underlying probability.

The important distinction is between a forecast changing in response to new information and a forecast being poorly calibrated. They are related questions, but they are not the same one.

How to Measure Forecasting Performance

A useful evaluation starts with a defined collection of forecasts, recorded at consistent points in time, and a clear record of their eventual outcomes.

1. Record the probability before the outcome

Use a timestamped market price or probability estimate. Decide whether the evaluation concerns opening prices, forecasts at a fixed time before resolution, or closing prices.

Without a consistent time horizon, comparisons can be misleading.

2. Include resolved events, not just memorable examples

A few prominent successes or failures cannot establish overall accuracy. An evaluation should include the full relevant set of resolved markets, explain any exclusions, and avoid selecting only examples that support a preferred conclusion.

Unresolved markets should not be treated as resolved outcomes.

3. Compare probabilities with outcomes

For binary events, the Brier score is one common measure of probabilistic forecasting performance. It calculates the squared difference between a forecast probability and the actual outcome, represented as 1 when the event occurs and 0 when it does not. Lower scores indicate better performance under this measure.

For example, a forecast of 70% receives a score of 0.09 if the event occurs: (0.70 − 1)². If the event does not occur, the score is 0.49: (0.70 − 0)². The evaluation becomes meaningful across a collection of forecasts, rather than from one result alone.

Other measures can be useful depending on the question. Reliability diagrams help assess calibration across probability ranges, while discrimination measures examine whether a forecasting system assigns higher probabilities to events that occur than to those that do not.

No single metric answers every question about forecast quality.

4. Compare like with like

A fair comparison between prediction markets, polls, expert forecasts, or statistical models should use comparable events, forecast dates, outcome definitions, and evaluation methods.

It should also consider whether forecasts are genuinely independent. A large number of contracts may not represent an equally large number of distinct events or independent pieces of information.

5. Examine performance across categories

An aggregate score can conceal important differences. Accuracy may vary across political events, sports, economic releases, and other categories. It can also vary by probability range, trading activity, and time to resolution.

A useful analysis reports these differences instead of assuming that an overall average describes every market.

Does a More Active Market Mean a More Accurate Forecast?

Not necessarily.

Trading activity can help prices respond to new information, and a deeper market may make it easier for participants to trade without moving the price substantially. But volume alone does not demonstrate that a probability estimate is well calibrated.

A heavily traded market can still be affected by shared assumptions, incomplete information, concentrated participation, or incentives unrelated to forecasting accuracy. A quieter market may contain useful information, but a thin market can also produce prices that are more sensitive to individual trades.

To assess whether activity is associated with better forecasting, analysts should compare resolved outcomes and measured forecast performance across markets with different levels of volume and participation. The relationship should be tested rather than assumed.

What Prediction Market Accuracy Cannot Tell You

Even a well-calibrated forecasting system has limitations.

A correct outcome does not prove the probability was accurate. An event assigned a 20% chance can occur, just as an event assigned an 80% chance can fail to occur. The individual outcome does not establish whether the probability estimate was well calibrated.

A good historical record does not guarantee future performance. Information conditions, market participation, contract structures, and the events being forecast can change.

Accuracy does not necessarily mean profitability. A market’s forecast quality and the financial return available at a particular price are different questions. Transaction costs, execution, contract terms, and the price paid all matter to financial outcomes.

A market price is not a direct measurement of objective truth. It is a price formed through a particular market structure. Interpreting it as a probability is useful, but that interpretation should be tested against actual outcomes.

Different markets may use different definitions of resolution. Before comparing forecasts, analysts need to understand the contract rules and exactly what counts as the event occurring.

These limitations do not make prediction markets useless. They explain why careful measurement is necessary before treating market prices as reliable forecasts.

How to Think About Prediction Market Accuracy

Prediction markets are best evaluated as probabilistic forecasting systems rather than as collections of correct and incorrect calls.

The most informative analysis asks whether probabilities are calibrated across many resolved events, how forecast performance changes with time to resolution, and whether results hold across different event categories and market conditions. It also distinguishes the quality of a forecast from the outcome of a single event or the financial return associated with a trade.

Research suggests that prediction markets can aggregate information effectively, and some large datasets show strong calibration as events approach resolution. But those findings need to be interpreted within their specific samples and methods. No platform, category, or probability range should be assumed accurate without evidence.

The central principle is straightforward: evaluate probabilities across outcomes, not reputations across anecdotes.

Further Reading

  • Why One Correct Prediction Doesn’t Prove Accuracy — why individual outcomes are not enough to evaluate a probabilistic forecast.
  • How to Evaluate a Market Forecast After an Event Ends — a practical framework for reviewing a forecast against its eventual outcome.

Explore more analysis in Market Accuracy & Research, part of Wageranalytica’s broader coverage of prediction markets, odds, and probability.

Sources & Further Reading

Research and studies

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Sources are provided to help readers examine the evidence behind this article. Research findings are specific to the datasets, methods, and conditions studied.

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