How to Improve Forecast Accuracy in Prediction Markets

To improve forecast accuracy, define the outcome precisely, start with comparable past events, update for relevant evidence and score your estimates after resolution. The important step is closing the loop: use your record to identify a specific weakness, then test a change on future forecasts.

You can practise in a spreadsheet without risking money. If probability estimates are new to you, start with our introduction to forecasting; this guide focuses on improving and measuring them.

1. Define the question and starting estimate

Before assigning a probability, identify the Yes condition, deadline, time zone, resolution source and exclusions. Polymarket’s resolution guidance, for example, states that markets resolve according to their predefined rules—not simply their headlines.

Consider one hypothetical market: “Will Company A launch Product B by November 30, 2026?” Assume its rules require paid public availability by 11:59 p.m. UTC, documented on the company’s official product page. An announcement or invitation-only beta does not qualify.

Start with a base rate: how often the qualifying outcome occurred among comparable cases. Suppose 12 of 20 products at the same stage, with the same time remaining, met their deadlines:

Starting probability = 12 ÷ 20 = 60%.

Different launch definitions, approval requirements or lead times could make those cases misleading. Twenty cases also leave substantial uncertainty.

Suppose stronger distribution readiness leads you to raise the estimate to 65%. Record why. That five-percentage-point adjustment is a judgment, not a statistically derived result.

Reference classes and avoiding base-rate neglect were among the methods taught in the Good Judgment Project’s probability-training module. They provide a disciplined starting point, not a guarantee of accuracy.

2. Update for information, not repetition

Write down an initial estimate before checking the market price, where practical. Then use the price and other forecasts as comparison points: what evidence might explain the disagreement?

For each new item, ask:

• Is it relevant to the actual condition? A launch announcement does not establish paid availability.
• Is it new evidence? Several articles quoting one company statement are not several independent confirmations.
• Does it change the probability? Record the direction, size and reason for any revision.

Set review triggers, such as an official availability update or a missed milestone. You need not change the number at every review. A dated “unchanged; no new qualifying evidence” entry is useful too.

Keep a compact record:

Field	What to save
Question	Exact condition, deadline and resolution source
Estimate	Date, probability and base rate
Reasoning	Key evidence, assumptions and strongest contrary evidence
Updates	Revised probability and what changed
Result	Resolved outcome and forecast score

Preserve earlier entries rather than overwriting them. Record all questions you choose to track, not just memorable successes.

3. Measure probability error, not just wins

For binary questions, the Brier score measures squared probability error. Using the 0–1 convention, lower is better and zero is perfect. The Joint Working Group on Forecast Verification’s guide explains the score and its limitations.

Score = (probability − outcome)², where probability is a decimal and outcome is 1 for Yes or 0 for No.

For the 65% launch forecast:

• If it resolves Yes: (0.65 − 1)² = 0.1225.
• If it resolves No: (0.65 − 0)² = 0.4225.

Average scores across resolved questions. One result cannot establish whether your method works: a 65% forecast still allows a 35% chance of No.

Decide which forecast snapshot to score before seeing the results—for example, your estimate 30 days before each deadline. Keep other updates for analysis, but do not let a question you revised ten times receive ten times the weight of one you revised once. Only use a binary outcome score where the resolution maps to Yes or No; record cancellations or other exceptional resolutions separately.

Also inspect calibration: among forecasts near 65%, does Yes occur roughly 65% of the time across a sufficiently large set? Show the number of cases alongside each probability band. Sparse results are noisy, and many forecasts about one underlying event should not be treated as independent evidence of skill.

4. Test a specific improvement against a baseline

Compare your forecasts with a recorded baseline—such as the base rate or a consistently defined market-price snapshot—on the same questions at the same forecast times. Do not count switching to easier questions or forecasting closer to resolution as proof that your reasoning improved. The verification guide notes that some event frequencies make a low Brier score easier to achieve without real skill.

Review your log for a recurring, actionable weakness:

• Poor comparison groups: tighten the criteria for selecting historical cases.
• Excessive confidence: check forecasts in your highest and lowest probability bands against their observed outcomes.
• Missed updates: add a review trigger tied to a relevant milestone.
• Repeated evidence: trace reports back to their original sources before revising.

Choose one change, write it down and apply it to the next set of paper forecasts. A lower average score relative to the same baseline is more informative than remembering that your latest prediction was right; small differences still require more observations before drawing strong conclusions.

5. Keep accuracy separate from trading costs

Suppose the hypothetical launch contract pays $1 for Yes and $0 for No. Its Yes bid is 60¢ and ask is 64¢: a 4¢ spread. The ask is the available immediate purchase price for the quantity offered there, not the 62¢ midpoint. Kalshi’s glossary defines bids, asks and spreads and describes this binary payout structure.

Assume one contract fills at 64¢, an illustrative 2¢ entry fee, holding to settlement and no other costs. This is not a live fee quote.

• Expected payout at your 65% estimate: 0.65 × $1 = $0.65.
• Total entry cost: $0.64 + $0.02 = $0.66.
• Expected net result: $0.65 − $0.66 = −$0.01.

Do not subtract the spread again: the calculation already uses the ask. Under these assumptions, break-even requires 66%, even though your forecast exceeds the midpoint.

Keep two separate records: How well did my probabilities predict resolution? And, if you traded, what happened after execution costs and fees? Before taking a position, use the pre-trade reading checklist. Improving a forecast does not remove financial risk.