Evaluate a prediction market on three separate tests: whether you can execute your intended order at a reasonable average price, what the complete round-trip cost will be, and how well a defined set of past forecasts performed. A displayed probability is not necessarily an executable quote, a narrow spread does not prove there is enough depth, and a high win rate does not prove good probability calibration.
What you are actually measuring
Liquidity, fees, and accuracy answer different questions and should not be blended into one score.
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- Liquidity: Can you buy or sell your intended number of contracts now, and at what average price? This is an execution question.
- Fees: What explicit charges apply to this contract, order type, side, and exit plan? These are platform costs, separate from spread and slippage.
- Accuracy: How closely did forecasts match outcomes across a specified sample, forecast horizon, and scoring method? This is a historical statistical property, not a promise about your next trade.
The practical test is to compare your expected forecast edge with the spread, likely slippage, fees, and the cost of closing the position. Then assess whether the historical evidence is comparable to the market you are considering.
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Start with the executable bid and ask
Identify the best bid (the highest current buying price) and best ask (the lowest current selling price). The spread is the ask minus the bid. A buyer crossing the spread pays the ask; a seller crossing it receives the bid.
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Do not assume the large number displayed beside a contract is the price you will receive. Polymarket documentation states that its displayed price is ordinarily the bid–ask midpoint, while the last-traded price is shown instead when the spread exceeds $0.10. Its documentation describes prices as emerging from users trading with one another rather than being set by the platform. In either case, the number on the screen can differ from an executable quote.
For a buy order, begin your calculation at the ask. For a sell order, begin at the bid. If you plan to use a limit order, the quote is a target rather than a guarantee: the order may never fill.
Measure the cost at your intended size
A spread is only the cost of crossing the first price level. To estimate a real fill, read the quantities resting at successive price levels and walk through the book until your entire order is accounted for. Kalshi’s order-book guide describes this display as price levels paired with the quantities available at each level.
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For a buy, calculate a weighted average:
average fill = (quantity1 × price1 + quantity2 × price2 + …) ÷ total quantity
For a sell, use the bids in the same way. Compare the resulting average with the headline quote and with the price at which you could later exit.
A simple depth example
Suppose a hypothetical order book offers 40 contracts at $0.60, 30 at $0.61, and 50 at $0.63. A 40-contract buy would average $0.60. A 70-contract buy would cost (40 × 0.60 + 30 × 0.61) ÷ 70 = $0.6043. A 120-contract buy would average (40 × 0.60 + 30 × 0.61 + 50 × 0.63) ÷ 120 = $0.6158. The displayed best ask remains $0.60, but the larger order pays materially more.
This example is only a method illustration; use the live quantities and prices for the market you are evaluating. Recalculate shortly before submitting because displayed depth can change or disappear.
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A position can be easy to enter and expensive to close. Inspect the opposite side of the book for the quantity and prices available when you expect to sell. If your plan is to hold to settlement, identify the settlement date and the rules that determine the final outcome. If you may exit early, model the exit against current bids rather than assuming you can sell at the midpoint.
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Use volume as context, not proof
Recent volume tells you that trading occurred during a period; it does not show how much is available at the moment you need to trade. A market can have high historical volume and a thin current book. Conversely, a newly listed market may have little volume but sufficient visible depth for a small order. Treat volume, trade frequency, and time since the last trade as context around the live order book.
Understand limit-order trade-offs
A limit buy controls the maximum price you will pay, and a limit sell controls the minimum price you will accept. The trade-off is execution risk: the order can remain partially filled or unfilled while the event moves. A marketable order provides more certainty but consumes available levels and can incur slippage. Decide which risk matters more for your strategy before choosing the order type.
Do not use a universal “good spread” threshold
A two-cent spread may consume an entire one-cent forecast edge and be minor relative to a ten-cent edge. The effect depends on the contract probability, side of the trade, order size, time to settlement, expected exit, and fee schedule. Evaluate the spread and modeled slippage as a percentage of your expected edge, not against a fixed rule.
How to calculate fees correctly
Separate every cost component
Build an all-in estimate with separate lines for:
- the spread paid when crossing from bid to ask;
- slippage from consuming multiple price levels;
- maker fees for orders that rest and provide liquidity;
- taker fees for orders that execute against resting liquidity;
- fees on the exit, if you expect to close before settlement; and
- any funding, withdrawal, payment-provider, or other account charge that actually applies.
Do not subtract a fee once at entry and then assume the exit is free. A round-trip strategy needs an entry estimate and an exit estimate using the applicable side and order type for each leg.
Check the contract-specific schedule
Fee rules can vary by market, category, and maker-versus-taker status. Read the fee shown for the exact contract and order you are about to place. A venue-wide headline rate may not apply to the event in front of you.
Kalshi: verify the live event and order details
Kalshi’s official help material says fees can differ by market and discusses maker fees in some cases. It directs users to a complete fee schedule. The accessible material does not establish one universal current event-contract rate, so do not insert a single Kalshi percentage into a comparison. Record the rate displayed for the specific market, side, and order type at the time of trading.
Polymarket: apply the documented formula and category rule
Polymarket documentation gives the taker-fee formula:
fee = C × feeRate × p × (1 − p)
Here, C is the number of shares, p is the share price, and feeRate is the rate for the applicable category. The documentation says makers are not charged trading fees and that geopolitical or world-event markets are fee-free. Because the documentation and market parameters can change, check the live market’s fee fields before quoting or using a rate.
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For example, if you buy C shares at probability p, calculate the fee from the current category rate, then repeat the calculation for any later taker sale. Do not treat the formula as evidence that every market has the same rate or that a maker order will necessarily fill.
Compare fees with the edge, not in isolation
Suppose your analysis estimates a six-cent advantage over the current executable price. If the entry spread and slippage consume two cents, the entry fee is one cent, and an expected exit costs another two cents in spread, slippage, and fees, only one cent of the original edge remains. That arithmetic is more useful than labeling a platform “cheap” or “expensive.” Keep the assumptions visible so you can update them when the book or fee schedule changes.
How to evaluate forecast accuracy
Calibration is different from a hit rate
Calibration asks whether forecasts near a stated probability resolve at roughly that frequency. If a collection of independent forecasts is priced near 70%, a well-calibrated set should resolve “yes” about 70% of the time over a suitable sample. A hit rate alone does not answer that question: a forecaster can win often by making only very high-probability calls, while being badly calibrated at other probabilities.
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Discrimination is another property: can the forecasts rank more likely outcomes above less likely ones? Profitability is a third: did prices, costs, timing, and position sizing produce a positive return? Do not present one as proof of the others.
Use a proper probabilistic score
The Brier score compares a probability forecast with the eventual binary outcome; lower scores indicate better probabilistic accuracy under that scoring rule. It penalizes confident errors more heavily than a simple correct-or-incorrect count. Report the scoring convention, forecast timestamp, and treatment of markets that were cancelled, ambiguous, or never resolved.
Define the dataset before comparing results
Write down these choices before calculating a score:
- venue and market categories;
- the resolution period and the forecast horizon, such as three months before settlement or at close;
- the exact timestamp used for each forecast;
- rules for cancelled, voided, or ambiguous contracts;
- whether markets are weighted equally or by volume; and
- the alternative forecast being used for comparison.
Compare like horizons. A quote made one day before resolution should not be ranked against a quote made three months earlier without labeling the difference.
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What published Kalshi figures show—and what they do not
Kalshi Research reported in August 2026 on 2,243,741 resolved Kalshi markets across eleven categories, from the platform’s launch in 2021 through mid-2026. Excluding Exotics, it reported Brier scores of approximately 0.08–0.09 at a three-month horizon and roughly 0.02 at close. Its reported naive accuracy rose from 88.3% at three months to 97.2% at close.
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Those numbers describe the study’s platform-specific dataset and timing choices. They are not a guarantee for an individual contract, a future period, or another venue. The same publication reports stronger aggregate calibration as resolution approaches and says calibration improves with volume and unique traders; that does not prove that every high-volume market is well calibrated or that the pattern applies universally.
Account for favorite–longshot bias
A 2026 CESifo working paper using a transaction-level dataset of more than 300,000 contracts reports favorite–longshot bias in Kalshi prices: low-price contracts won less often than their prices implied for break-even, while high-price contracts won more often. This is a warning against assuming that every quoted probability is equally reliable across the price range. Check calibration by probability bucket instead of relying on one aggregate number.
Use independent comparisons where available
A preliminary Federal Reserve staff paper provides a macroeconomic example of comparing Kalshi-implied forecasts with survey and market-implied forecasts. The authors state that the views are theirs and not necessarily those of the Board of Governors or the Federal Reserve System. Treat such working-paper evidence as bounded analysis, and match the event definition, horizon, and release timing before drawing a conclusion.
How to compare two markets or venues
First establish that the contracts are genuinely comparable. Differences in wording or settlement can explain different prices without implying that traders disagree about the same event.
| Comparison axis | What to record | Why it matters |
|---|---|---|
| Question and outcome | Exact event definition, “yes” condition, exclusions, and edge-case treatment | Similar headlines can represent different contracts. |
| Resolution | Deadline, verification source, publication used, and settlement procedure | Different sources or timelines can produce legitimate price differences. |
| Executable price | Best bid, best ask, midpoint or last-trade convention, and weighted average fill at your size | A headline probability may not be tradeable at your intended quantity. |
| Depth and exit | Quantities at successive levels on both sides and expected exit price | Entry liquidity does not guarantee an affordable exit. |
| Activity | Recent volume, trade recency, and order-book refresh immediately before trading | Historical activity and current depth measure different things. |
| Fees | Maker/taker status, market-specific rate, entry charge, exit charge, and other applicable costs | Fees can erase an apparent price advantage. |
| Accuracy evidence | Resolved sample, category, horizon, calibration, Brier score, and weighting method | Unmatched samples cannot support a fair ranking. |
Kalshi says its rules summary includes the measured value, timeline, and verification source, but the summary is not the full rules text. Read the complete contract terms before treating two markets as equivalents.
A repeatable evaluation workflow
- Read the full contract. Record the event definition, resolution source, deadline, measured value, and edge-case rules.
- Set the decision horizon. Decide whether you expect to exit early or hold through settlement, then use that choice consistently in your cost model.
- Capture the live book. Note the best bid and ask, visible quantities at each level, and the time of observation.
- Size the order. Walk through the levels on the side you will trade and calculate the weighted average fill for the exact number of contracts.
- Model the exit. Inspect the opposite side and calculate a conservative sale price, including partial fills if the visible depth is insufficient.
- Apply fees. Identify maker or taker treatment and the market-specific rate for both entry and exit. Keep fees separate from spread and slippage.
- Estimate the net edge. Subtract all modeled costs from your forecast advantage. Re-run the arithmetic if the quote, depth, or fee parameter changes.
- Audit historical evidence. Use resolved outcomes at a matched horizon, report calibration and Brier score where possible, and note sample-selection and platform limitations.
- Recheck immediately before execution. A snapshot can become stale; confirm the book, terms, and fee display again.
Common errors and how to correct them
Using the midpoint as a guaranteed fill
Problem: The midpoint or last trade is treated as the purchase or sale price.
Correction: Use the ask for a buy, the bid for a sell, and a weighted average across all levels needed for your size.
Calling a tight spread “liquid” without checking quantity
Problem: The first price level is narrow but contains too few contracts.
Correction: Inspect depth through your full order and on the expected exit side.
Comparing only posted fee percentages
Problem: A maker rate, a taker rate, and an entry-only estimate are compared as if they were equivalent.
Correction: Calculate the actual order type, category, side, and round-trip costs for the contract.
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Problem: A high close-to-resolution hit rate is presented as evidence of superior long-horizon forecasting.
Correction: Match forecast horizons and report calibration, Brier score, sample construction, and resolution rules.
Assuming contracts with similar names are interchangeable
Problem: Different deadlines or verification sources are ignored.
Correction: Compare the complete terms and identify exactly what event each contract settles on.
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Decision checklist
- Can I identify the actual bid and ask rather than relying on a displayed midpoint or last trade?
- Does visible depth cover my full entry and likely exit?
- What weighted average fill should I expect at my size?
- Which maker or taker fee applies to this exact market and order?
- Have I included exit costs and separated them from spread and slippage?
- Are the historical forecasts resolved, matched to my horizon, and scored with a proper metric?
- Do the contracts use the same outcome definition, deadline, and verification source?
- Have I rechecked the live book and fee display immediately before trading?
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