Binary outcome prediction: keep the probability, not just the pick

Binary outcome prediction means forecasting an event with two possible outcomes. In a prediction market, those are usually YES and NO. The useful forecast is a probability—such as a 70% chance of YES—not merely a choice of which side will win.

Code the resolved outcome as 1 for YES and 0 for NO. Before resolution, let p represent the forecast probability of YES; the probability of NO is 1 − p. Two possible outcomes do not imply equal chances.

A 70% forecast and a 99% forecast both favor YES, but they make very different claims about uncertainty. Reducing them to the same “YES” pick discards information needed to evaluate the forecast’s confidence.

Define the event before predicting it

A binary forecast needs a question whose answer can be determined under stated rules. “Will inflation improve?” is too vague. A hypothetical contract could instead ask: “Will the specified official year-over-year inflation release for December be below 3.0%?”

That wording still needs the exact measure, publication source and treatment of revisions. “Below 3.0%” means a reading of precisely 3.0% would not qualify. Those details define what the forecast is about.

Polymarket’s resolution documentation says markets have predefined rules specifying the resolution source, end date and handling of edge cases. Those rules—not just the headline—determine the winning outcome.

For a usable forecast record, save:

• The event definition: including the threshold, deadline and resolution source.
• The forecast probability and timestamp: what was estimated, and when.
• The forecast source: a market quote, statistical model or judgment.
• The final outcome: assigned under the recorded rules, with exceptional settlements identified separately.

See what YES and NO mean for the distinction between the event and its outcome coding.

Read a market price as an implied probability

For an ordinary binary contract paying $1 if YES wins and $0 otherwise, a 70-cent YES price is commonly read as a 70% implied probability. That is a market-derived estimate, not proof that the event’s true chance is exactly 70%.

Polymarket explains that prices emerge from users trading with one another. As of October 30, 2026, its price documentation also makes a consequential distinction: the displayed price is normally the bid–ask midpoint, but when the spread exceeds $0.10, it shows the last traded price instead.

So record what your number measures. A midpoint is not necessarily an executable purchase price, and a last trade is not necessarily a current quote. Spreads, depth and execution costs matter if you move from evaluating forecasts to evaluating returns. The displayed spread is not, by itself, a statistical confidence interval for the event’s probability.

For the underlying mechanics, see how prediction-market prices become probabilities.

A statistical model or human forecaster can supply a probability instead. Whichever source you use, preserve the probability rather than converting it immediately into a binary label.

Evaluate probabilities, not just winning picks

Consider five hypothetical events, four resolving YES and one resolving NO. Each forecaster assigns the same probability to every event and selects YES whenever that probability exceeds 50%.

Forecaster	YES probability for each event	Winning picks	Mean Brier score
A	60%	4 of 5	0.2000
B	80%	4 of 5	0.1600
C	95%	4 of 5	0.1825

All three have an 80% hit rate. Their probability forecasts are not equally good on this sample.

The binary Brier score is the average of (p − y)², where p is the YES probability and y is the 0/1 outcome. Lower is better; this convention ranges from 0 to 1. The formula and interpretation are documented in scikit-learn’s Brier score reference.

For B, each YES costs (0.8 − 1)² = 0.04, while the NO costs (0.8 − 0)² = 0.64. The average is (4 × 0.04 + 0.64) ÷ 5 = 0.16. C gains from greater confidence on the four YES outcomes but loses more heavily on the NO.

This tiny constructed sample illustrates scoring; it does not establish any forecaster’s long-run quality.

Test calibration across comparable forecasts

Calibration asks whether forecast probabilities match observed frequencies. Across a sufficiently large set of comparable forecasts near 80%, roughly 80% should resolve YES. That is the interpretation given in scikit-learn’s calibration guide.

For market evaluation, compare forecasts taken at the same lead time—for example, one day before each event’s outcome-determining release or deadline, using only information available at the forecast timestamp. Separate materially different contract categories, and report the number of observations in each probability range. Treat several contracts tied to one underlying event as related observations, not independent confirmations.

A lower Brier score does not necessarily mean better calibration: it also reflects how well forecasts distinguish more likely events from less likely ones. Use Brier score and calibration together.

The practical unit of binary outcome prediction is therefore a defined question, a timestamped probability and a resolved outcome. Keeping all three lets you evaluate confidence as well as outcomes, rather than measuring hit rate alone.