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A prediction-market strategy should be judged by the quality of its probabilities across many resolved, comparable forecasts—not by whether it picked the winner of one event. Record forecasts before resolution, measure calibration and Brier Score against a stated baseline, then test whether any advantage survives fees, spreads, limited depth, position size, exit assumptions, and the contract’s actual settlement rules.
A high hit rate can coexist with poorly assigned probabilities, and a useful forecast can still lose money after execution costs. Treat forecasting skill and tradable profitability as separate questions, using the same horizon, contract class, outcome definition, and evidence standard for every strategy you compare.
What you are actually evaluating
“Backing the most likely outcome” produces a binary result: the selected side either wins or loses. That is too little information to evaluate a probability-based strategy. A serious review asks whether the strategy’s stated probabilities were accurate over a large set of resolved contracts and whether those probabilities could have been traded profitably at available prices.
For each forecast, preserve four items:
- the event and contract identifier;
- the probability assigned to the Yes outcome, or the market-implied probability you are testing;
- the exact time at which that probability was recorded; and
- the eventual, officially settled outcome.
Only after that record is complete should you add orders, fills, fees, exits, and realized profit. This separation prevents a favorable trade result from being mistaken for evidence of forecasting skill.
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Define the forecast before collecting results
Fix the event definition
Use a stable outcome definition and a stable verification source. “Will inflation fall?” is not a testable event until you specify the measure, reference period, threshold, publication and revision rules, and the date by which the result is determined. A strategy can appear inconsistent simply because contracts that sound similar use different definitions.
Record the contract’s Yes and No meanings exactly as written. Include the measured value, unit, threshold, reference period, deadline, and source agency or data provider. If the contract uses a range or multiple conditions, preserve those conditions rather than reducing them to an informal summary.
Declare the forecast time
A probability captured three months before resolution is not comparable with one captured one minute before the close. Near the deadline, prices can incorporate information released during the event; an early forecast is making a harder prediction with less information.
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- one forecast exactly 90 days before resolution;
- one forecast at 30 days, seven days, and one day before resolution, scored as separate cohorts; or
- a continuously sampled series with each observation labeled by its remaining time to resolution.
Never pool those horizons into one score without showing the mix. If a contract resolves early, use the actual remaining time at the recorded timestamp and place it in the appropriate predeclared band.
Freeze the information set
Use only information that was available when the forecast was recorded. A later revision, news report, or settlement announcement belongs in the post-forecast record, not in the original rationale. Store a timestamp in a consistent time zone and keep the original probability immutable; corrections should be appended as an audit note.
Measure calibration, not just wins
Calibration is a frequency question. If a strategy repeatedly assigns 70% to a group of comparable events, approximately 70% of that group should resolve Yes. The test concerns the probabilities themselves, not only which side had the largest number.
Create probability bins or cohorts large enough to be informative. For each cohort, report the number of forecasts, the mean assigned probability, the number of Yes resolutions, and the observed frequency.
| Assigned-probability cohort | Forecast count | Mean probability | Observed Yes frequency | Calibration gap |
|---|---|---|---|---|
| 0.50–0.59 | n | p | frequency | frequency minus p |
| 0.60–0.69 | n | p | frequency | frequency minus p |
| 0.70–0.79 | n | p | frequency | frequency minus p |
| 0.80–0.89 | n | p | frequency | frequency minus p |
| 0.90–0.99 | n | p | frequency | frequency minus p |
For example, a 70% cohort that resolves Yes 85% of the time is overconfident in the opposite direction: its forecasts are too low for the observed frequency. A cohort resolving Yes 55% of the time is overconfident if its mean assigned probability is 70%. Report the sample count beside every frequency; a 4-for-5 result should not be read like a 400-for-500 result.
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Calibration does not tell you whether a strategy is profitable. A perfectly calibrated strategy may quote probabilities that leave no advantage after the market price and costs. Calibration is the first test of whether the probability statements deserve trust.
Use Brier Score for probability-level accuracy
For a binary contract, let p be the forecast probability of Yes and y be 1 when the contract settles Yes or 0 when it settles No. The per-forecast Brier loss is (p - y)^2; the Brier Score is the mean of those losses over the sample. Lower is better.
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Make the score comparable
Compute the score on a clearly defined set: same contract class, event categories, resolution horizon, settlement convention, and inclusion rules. Do not compare a three-month macroeconomic sample with a close-to-resolution sports sample and call the lower number a superior strategy without explaining the difference.
Include a stated benchmark evaluated on the identical contracts and timestamps. Possible baselines include the market price at the sampling time, a constant base-rate probability, or another predeclared model. The benchmark must use the same outcome labels and exclude no contracts that the strategy was allowed to forecast.
CFA Institute’s 2016 article Portfolio Structuring: The Science of Forecasting describes Brier Score as the metric selected by Good Judgment to track forecasts against eventual outcomes and discusses repeated review as a way to refine forecasting skill. That discussion supports disciplined scoring; it does not demonstrate that any particular market strategy will earn a profit.
Report uncertainty and composition
Publish the number of contracts, category mix, horizon distribution, and confidence intervals or another uncertainty summary appropriate to your sample. A single aggregate score can improve merely because the sample contains easier events. Break out categories and horizons when the mix is heterogeneous, and state whether contracts were selected before or after seeing their outcomes.
Keep forecast quality separate from trading performance
Maintain two ledgers
The probability ledger contains every eligible forecast, including forecasts that were never traded. It is used for calibration and Brier Score. The trade ledger contains attempted orders, fills, cancellations, entry and exit prices, contract quantity, fees, and realized settlement cash flow.
Use the probability ledger to answer, “Were the estimates accurate?” Use the trade ledger to answer, “What happened when those estimates were converted into positions under these execution rules?” Never remove an untraded forecast from the quality sample merely because it could not be filled.
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Define the trading rule in advance
State the threshold at which a forecast becomes a trade. For example, a rule might buy Yes only when the estimated probability exceeds the executable ask by a specified margin, or sell when the estimated edge exceeds a cost buffer. Specify maximum position size, whether partial fills count, when positions may be exited, and what happens if the contract remains open at the end of the test period.
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Use executable prices, not a midpoint that could not have been traded. For a buy, record the ask and filled quantity; for a sell, record the bid. If the strategy assumes an exit before settlement, record the actual exit price or a predeclared mark-to-market rule.
Model fees, spreads, depth and liquidity
An apparent probability edge is not a net edge until all trading frictions are included. For each trade, calculate:
- entry price and side;
- exit price or settlement value;
- bid-ask spread crossed;
- quantity filled and available depth at the intended price;
- platform and regulatory fees under the applicable schedule;
- any funding, transfer or other transaction charge; and
- the capital tied up and the holding period.
Use the contract’s actual fee schedule and its effective date. Kalshi’s official schedule effective February 5, 2026 includes a formula for specified S&P 500 and Nasdaq-100 contracts. That formula is specific to those contracts and must not be presented as the fee for every event market. Schedules can change, so preserve the version used in the calculation and rerun the analysis when terms change.
| Layer | Question to answer | Evidence to retain |
|---|---|---|
| Price | What price could the strategy actually execute? | Timestamped bid, ask, and fill records |
| Spread | How much value is lost crossing the market? | Quoted spread and side of each fill |
| Depth | Can the intended size trade without moving the price? | Order-book levels and filled quantity |
| Fees | What dated schedule applies to this contract? | Schedule version, formula and calculation |
| Exit | Can the position be closed as assumed? | Exit fill, settlement result or marked value |
Run sensitivity cases for optimistic fills, observed fills and conservative fills. If the strategy is profitable only under midpoint execution or zero fees, describe that as a paper edge rather than a demonstrated net result.
Read the resolution rules before scoring an outcome
The event that happened in the world and the event that settles a contract are not always identical. Market-specific rules define the Yes/No criteria, the measured value, the timeline and the verification source. The full terms can contain exceptions that are absent from a short title.
- Open the market’s full rules before recording the forecast.
- Write down the exact criterion and official source.
- Note the scheduled close and any rule allowing delayed settlement.
- After the apparent event, wait for the contract’s official settlement status.
- Use that settled status as
yin the score, not an informal interpretation. - Preserve any later correction or revision in the audit trail.
Kalshi’s Market Rules and Rules Summary help pages explain that market-specific rules determine the criteria and verification sources, while the summary identifies the measured value and timeline. Its Market Outcomes help page likewise says the contract terms and source determine the outcome. The FAQ notes that official data can be delayed or revised and that settlement timing depends on the market rules. As the Kalshi Help Center puts it on its Market Rules page dated March 17, 2026: “Make sure to read market rules before trading!”
A market remaining open after the apparent event is not proof that the outcome is already determined. Treat an unresolved contract as unresolved in the evaluation, and state how cancellations, voids and rule changes are handled.
What the 2026 Kalshi study can and cannot tell you
Kalshi Research’s August 2026 analysis covers 2,243,741 resolved Kalshi markets across eleven categories, from the platform’s launch in 2021 through mid-2026, excluding Exotics. In that dataset, reported Brier Scores fell from approximately 0.08–0.09 at a three-month horizon to roughly 0.02 at close.
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| Reported element | How to interpret it |
|---|---|
| 2,243,741 resolved markets | Large platform-specific sample, not a universal count for prediction markets |
| Eleven categories | Results reflect the study’s category mix and exclusions |
| 2021 launch through mid-2026 | Historical window; later market conditions may differ |
| About 0.08–0.09 at three months | Reported average range for that horizon in the covered dataset |
| About 0.02 at close | Reported near-resolution result, when more information is available |
Those figures are findings from Kalshi’s dataset and analysis, not a market-wide performance guarantee. The publication reports stronger calibration with greater event-level volume and more unique traders, and it studies market microstructure, participant heterogeneity and belief updating. The relationships are observational: they do not prove that volume causes better forecasts, and they do not make every low-volume market unusable. No universal conversion from a given Brier Score to net profitability is established by these figures.
Use a common comparison framework
When comparing two or more strategies, disclose every axis that can change the result.
| Comparison axis | Required disclosure |
|---|---|
| Probability quality | Calibration table, Brier Score, sample size and benchmark |
| Forecast timing | Exact timestamp rule and separate results by time-to-resolution |
| Benchmark | Baseline probability and identical eligible contracts |
| Tradability | Executable side, spread, depth, fill rate, size and exit rule |
| Net results | Fees and other costs under the dated, applicable schedule |
| Resolution integrity | Contract criteria, official source, settlement status and exceptions |
| Evidence strength | Sample size, category mix and whether relationships are causal or observational |
If one strategy forecasts earlier and another trades near close, present separate rows rather than awarding the latter a quality advantage for using more information. If one strategy trades only liquid contracts, disclose that selection instead of comparing it with a strategy exposed to the entire market universe.
A repeatable evaluation workflow
- Write the protocol. Define eligible contract classes, categories, horizons, forecast timestamps, outcome labels, benchmark and trading rule before collecting results.
- Capture immutable forecasts. Save the probability, timestamp, contract identifier, information set and rationale. Do not overwrite the original value.
- Verify the rules. Store the full resolution criterion, source, deadline and treatment of revisions, cancellations and delayed data.
- Wait for official settlement. Mark the binary outcome only when the contract resolves under its rules.
- Build calibration cohorts. Group forecasts by assigned probability and report counts, means, observed frequencies and gaps.
- Compute Brier Scores. Score the strategy and benchmark on the same contracts and horizon; break out important categories.
- Reconstruct execution. Match each eligible signal to the contemporaneous order book, fills, partial fills, exits and position size.
- Apply dated costs. Calculate contract-specific fees, spread impact and any other charge. Keep the fee schedule with the result.
- Run sensitivity tests. Recalculate with observed, conservative and alternative exit assumptions to show how fragile the edge is.
- Report limitations. Identify selection rules, missing data, unresolved contracts, category concentration and any observational inference.
Worked example with hypothetical numbers
Suppose a strategy records 100 forecasts at a 30-day horizon. The mean assigned probability is 0.65 and 65 contracts settle Yes. That cohort is calibrated in aggregate, but the result alone says nothing about trading profit.
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Assume the strategy buys Yes whenever its estimate exceeds the executable ask by at least 0.04. On 40 signals it buys at an average ask of 0.61, but the average spread cost, fees and slippage total 0.03 per contract. If the strategy’s estimated probability is 0.65, its gross expected value before costs is 0.04; after the 0.03 friction allowance, only 0.01 remains, before considering estimation error, position limits and exits. This is an illustration of the calculation, not an empirical result.
Now suppose the same strategy’s Brier Score is 0.18 while the benchmark’s is 0.16. It may still have made money in this short sample, but the probability record does not show superior accuracy. Conversely, a better Brier Score would not prove positive net returns unless the executable-price ledger also survives costs.
Common evaluation failures and how to correct them
Counting only the favorite’s wins
Failure: Reporting that the strategy won 62% of its picks.
Correction: Preserve the assigned probabilities, test calibration cohorts and compute Brier Score. A 62% hit rate can result from always choosing events with a 60% base rate, or from making poorly calibrated 90% claims.
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Pooling incompatible horizons
Failure: Combining early and near-close prices into one score.
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Correction: Predeclare horizon bands and publish each band separately, with the sample mix shown.
Using settlement hindsight
Failure: Treating an apparent real-world result as settled before the contract’s official source confirms it.
Correction: Apply the contract rules and settlement status, including revisions and delays.
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Failure: Assuming the full intended size traded at the midpoint.
Correction: Use actual fills, partial quantities and available depth; show a conservative scenario when fills are uncertain.
Applying one fee to every market
Failure: Copying a fee formula from a specific index contract to unrelated events.
Correction: Identify the contract and dated schedule that actually governs each trade.
Claiming causation from volume
Failure: Concluding that more traders or higher volume caused better accuracy.
Correction: Label the relationship observational and consider alternative explanations, such as easier events attracting more participation.
Overreading a short sample
Failure: Treating a profitable month as proof of a durable edge.
Correction: Expand the resolved sample, report uncertainty, separate categories and retain losing and untraded forecasts in the record.
How to present a final strategy assessment
A decision-ready report can use this order:
- Scope: contracts, categories, dates, exclusions and horizons.
- Forecast record: number of observations, probability distribution and calibration table.
- Accuracy: Brier Score for the strategy and benchmark on identical samples.
- Execution: fill assumptions, spread, depth, position size and exits.
- Costs: dated fee schedule and all other modeled charges.
- Net outcome: realized and sensitivity-case returns, clearly separated from forecast metrics.
- Resolution: contract rules, source, settlement delays and handling of revisions.
- Evidence limits: sample composition, missing data and observational versus causal claims.
The result should let a reader answer three different questions without conflating them: Are the probabilities calibrated? Are they more accurate than the chosen benchmark at the same horizon? And, after realistic execution and settlement assumptions, did trading them produce a positive net result? A strategy earns a credible endorsement only when each question is answered with the evidence appropriate to it.
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