How to Evaluate a Market Forecast After an Event Ends

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Once an event is over, it is tempting to look at the final result and decide whether a prediction market got it right. But a resolved outcome alone cannot tell you whether the market’s original probability was well calibrated, whether the forecast was informative, or whether its performance was better than a reasonable alternative.

Evaluating a market forecast requires a more structured approach. Analysts need to preserve the probability available before the outcome, confirm exactly what happened, and compare the forecast against the result using a consistent method.

This matters because prediction markets express uncertainty through prices. A market that assigns an event a 70% probability is making a different claim from one that assigns it a 95% probability, even if both ultimately point toward the same outcome.

Here is how to evaluate a market forecast after an event ends.

1. Preserve the Original Forecast

The first step is to establish what the market actually predicted and when.

Record the market price or implied probability at the time you want to evaluate. Depending on the question, this might be the opening price, a price recorded at a fixed time before resolution, or the final price before the outcome became known.

These are different forecasting snapshots. A price recorded three weeks before an event should not be judged as though it were the closing price immediately before resolution.

For a useful record, capture:

  • The event and the market’s exact question.
  • The date and time of the forecast.
  • The market price or implied probability.
  • The contract’s resolution rules.
  • The final resolved outcome.
  • Any relevant fees, spreads, or market conditions if you are also analyzing trading performance.

Preserving the original forecast helps prevent hindsight bias. Once an outcome is known, people tend to interpret earlier events through the lens of what actually happened. A timestamped record provides a more reliable basis for evaluation.

2. Confirm What the Market Was Actually Forecasting

Before scoring a forecast, verify the event definition and its resolution criteria.

A contract may concern whether an event occurs before a particular date, whether a named threshold is reached, or whether a specific official source confirms an outcome. Those details determine what counts as a successful resolution.

For example, a market asking whether an economic indicator will exceed a threshold is not necessarily equivalent to one asking whether the indicator will meet or exceed it. Similarly, preliminary figures and revised figures can differ.

The evaluation should use the outcome defined by the contract, not a broader interpretation of what the market appeared to be discussing.

If the market is voided, remains unresolved, or is settled under a special rule, document that status rather than treating it as an ordinary correct or incorrect forecast.

3. Compare the Probability With the Outcome

For a binary event, the outcome can be represented as 1 if the event occurred and 0 if it did not. The forecast probability can then be compared with that outcome.

Consider a market that assigned an event a 70% probability.

  • If the event occurs, the forecast was consistent with the more likely outcome.
  • If the event does not occur, the forecast was wrong in terms of its favored outcome, but the result was still within the 30% probability assigned to failure.

Neither outcome alone establishes whether the forecast was accurate. The original probability matters.

One useful measure is the Brier score, which calculates the squared difference between the forecast probability and the observed outcome. For a binary event, the formula is:

Brier score = (forecast probability − outcome)²

A lower score is better. For example, a 70% forecast receives a score of 0.09 if the event occurs and 0.49 if it does not.

The score captures an important distinction: a forecast that assigns very high confidence to an outcome that fails is penalized more heavily than a less confident forecast.

However, one Brier score cannot establish the long-run accuracy of a market. Its real value comes from comparing scores across a defined set of forecasts.

4. Evaluate Forecasts Across Multiple Events

A market can correctly forecast an unlikely outcome, and it can miss an outcome that it reasonably assigned a low probability. To assess whether its probabilities are reliable, analysts need to examine many resolved events.

Calibration is especially useful here. A forecasting system is calibrated when events assigned similar probabilities occur at approximately those frequencies over a sufficiently large collection of comparable forecasts.

Imagine a collection of 100 events, each assigned a probability near 70%. If approximately 70 events occur, the forecasts are consistent with calibration at that level. If only 50 occur, the market may have overstated the likelihood of those outcomes in that sample.

The observed frequency will not always match the stated probability exactly. Sample size, variation, event dependence, and the way the forecasts were selected all affect the interpretation.

A sound evaluation should therefore use a sufficiently large and clearly defined sample, rather than relying on a handful of memorable wins or losses.

For more background, see How Accurate Are Prediction Markets?.

5. Compare Like With Like

A forecast score becomes more informative when compared with a reasonable baseline or another forecasting method.

For example, an analyst might compare a market’s probabilities with a simple historical base rate, an expert forecast, or a statistical model. The comparison should use the same events, forecast timestamps, outcome definitions, and evaluation method wherever possible.

Without those controls, apparent differences in performance may reflect differences in the events being predicted rather than the quality of the forecasting systems.

It is also important to distinguish calibration from discrimination. Calibration asks whether probabilities correspond to observed frequencies. Discrimination asks whether a forecasting system assigns higher probabilities to events that occur than to those that do not.

A system can be reasonably calibrated without being particularly informative if it rarely distinguishes between events. Looking at both characteristics gives a fuller picture of performance.

6. Account for Time to Resolution

Market probabilities can change substantially between the opening of a market and the event’s resolution.

New information, changing conditions, and trading activity may cause prices to move as uncertainty declines. A closing forecast may therefore perform differently from a forecast recorded much earlier.

When evaluating a market, separate these time horizons instead of combining them into one score without explanation.

For instance, an analysis might evaluate forecasts recorded seven days before resolution separately from forecasts recorded one hour before resolution. That allows analysts to investigate whether forecast performance differs by horizon.

It also avoids giving the market credit for information that became available only after the original forecast was recorded.

A later forecast can be more accurate because more information is available. That is a useful property of the forecasting process, but it is not evidence that the earlier forecast contained the same information.

7. Separate Forecast Accuracy From Trading Profit

A forecast can be well calibrated without every trade based on it being profitable. Likewise, a profitable trade does not necessarily demonstrate that the underlying probability estimate was accurate.

Forecast evaluation asks how well probabilities correspond to outcomes. Trading performance asks whether a position generated a financial return after accounting for the price paid, transaction costs, fees, execution, and contract terms.

These questions overlap, but they are not interchangeable.

A market that assigns a 60% probability to an event may provide a useful forecast. Whether a particular trade at that price was financially attractive is a separate question that depends on the available alternatives and the costs and risks involved.

Keeping the two evaluations separate prevents financial outcomes from being used as a substitute for evidence about forecast quality.

8. Document the Limitations

Even a carefully calculated score can be misleading if the sample or methodology is poorly chosen.

When publishing an evaluation, explain the number and type of markets included, how probabilities were recorded, which outcomes were excluded, and whether the forecasts came from independent events. A large number of contracts does not necessarily mean a large number of independent forecasting situations.

Analysts should also be cautious when comparing markets from different categories. Forecast performance may vary between sports, elections, economic events, and other subjects, so an overall average can conceal important differences.

Finally, a historical evaluation describes the sample that was studied. It does not guarantee that a market will perform the same way in the future.

Transparent methodology makes the conclusions easier to interpret and easier for other readers to reproduce.

A Practical Market Forecast Review

When an event resolves, use this checklist to evaluate the forecast:

  • Record the original probability and its timestamp.
  • Confirm the market question and resolution criteria.
  • Verify the final outcome using an appropriate source.
  • Calculate a suitable forecast score, such as the Brier score for a binary event.
  • Add the result to the full evaluation dataset.
  • Compare performance across a sufficiently large set of forecasts.
  • Separate results by forecast horizon or event category when relevant.
  • Compare against a reasonable baseline where possible.
  • Document exclusions, limitations, and methodological choices.
  • Keep forecast accuracy separate from trading returns.

The purpose is not simply to label each market right or wrong. It is to build a consistent record that allows forecasting performance to be assessed over time.

The Bottom Line

Evaluating a market forecast after an event ends requires more than checking whether the outcome matched the market’s favored result.

Preserve the probability recorded before the event, verify the exact resolution criteria, and compare the forecast with the outcome using a consistent scoring method. Then evaluate the results across multiple events, taking calibration, forecast timing, sample size, and relevant baselines into account.

A single result is an observation. A properly documented collection of forecasts is evidence that can be analyzed.

The objective is to understand how well market probabilities performed under defined conditions, not to turn every resolved contract into a verdict on the market as a whole.

Sources & Further Reading

Research and methodology

Related Wageranalytica analysis

Research findings depend on the events, datasets, and methods used. The sources above provide further context for evaluating probabilistic forecasts rather than a guarantee of any particular market’s performance.

No picks. Just analysis.

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