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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteBacking the contract with the highest implied probability is only a starting point. A serious evaluation asks whether a strategy assigns useful probabilities across many comparable, resolved forecasts—and whether any advantage remains after spreads, fees, liquidity limits, execution choices and the market’s exact settlement rules. Measure forecast quality and trading profitability as separate questions, then connect them with a realistic transaction ledger.
What a strategy evaluation should actually measure
A single favorite can lose even when its probability estimate was sensible. If a market is priced at 70% and the event does not happen, that outcome is disappointing but not automatically evidence of a bad forecast. Conversely, repeatedly choosing winners does not prove that a strategy understands probabilities: a system that always selects the most popular outcome can have a respectable hit rate while assigning poor confidence and earning nothing after costs.
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Evaluate a strategy on a repeated sample of forecasts that share a defined setup. Record the probability—or the market-implied probability—you would have used at a predeclared time, preserve the event definition and resolution source, and wait for the contract’s official outcome. The core questions are:
- Are the probabilities calibrated over many forecasts?
- How good are the probability estimates according to a proper score such as Brier Score?
- Does the strategy outperform a stated baseline on the same contracts and horizon?
- Can its signals be traded at executable prices after fees, spread, depth and exits?
- Were the contracts counted as resolved according to their written rules and verification source?
These questions must be answered together, but one metric cannot substitute for another.
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Start by defining a comparable forecast sample
Fix the observation time
Write down when a forecast is sampled before collecting results. “At market open,” “seven days before resolution” and “at the final close” are different forecasting tasks. A price near resolution may incorporate information released during the event; it should not be treated as equivalent to an early estimate. If a strategy acts at several horizons, report each horizon separately rather than blending them into one score.
Keep the event and contract class stable
Define what counts as one observation. Comparable observations should use the same type of outcome, a consistent time-to-resolution window and a clearly stated event definition. Mixing a three-month economic forecast with a same-day weather contract can make an average score difficult to interpret, even if both resolve Yes or No.
Record the complete forecast, not just the pick
For every observation, store the probability assigned to Yes, the timestamp, contract identifier, market price used, event category, planned position and eventual official outcome. A record that says only “picked Yes” cannot be evaluated for calibration or probability quality.
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Predeclare inclusion and exclusion rules
Decide in advance how you will handle cancelled markets, changed contract terms, missing prices, partial fills and markets that have not settled. Do not remove an inconvenient loss after seeing the result. Keep an audit trail showing why an observation was included or excluded.
Calibration: test whether probabilities mean what they say
Calibration is a frequency question. Gather forecasts assigned a similar probability and compare that probability with the proportion that actually resolved Yes. If a strategy gives 70% to a large group of comparable events, approximately 70% of that group should resolve Yes for the strategy to be calibrated at that level. A few isolated forecasts cannot establish calibration; the property appears across repeated observations.
Use probability bins carefully
Group forecasts into predeclared ranges, such as 0–10%, 10–20% and so on, or use a method that reports the number of observations at each level. For each group, show the average forecast, the observed Yes rate and the sample count. A 70% group with 100 observations is more informative than a 70% group with four observations. Small groups can look dramatically over- or under-confident by chance.
Inspect the shape of errors
Calibration can fail in different ways. A strategy may be overconfident, assigning 80% while events occur only 65% of the time. It may be underconfident, assigning 55% to events that occur 70% of the time. It may be calibrated on average while being systematically wrong in particular categories or horizons. Report subgroup results when the sample supports them, and show counts so readers can distinguish a pattern from noise.
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Hit rate records whether the selected side won. It ignores the confidence attached to each selection. A strategy that calls every event 51% Yes can achieve a similar hit rate to one that distinguishes 51% from 95%, yet those forecasts convey very different information and produce different risk and pricing decisions. Calibration preserves the meaning of the probabilities; hit rate alone does not.
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Brier Score: a probability-level score for binary markets
Brier Score evaluates a probability against the eventual binary result. For each forecast, calculate the squared difference between the assigned probability p and the outcome o, where the outcome is 1 for Yes and 0 for No:
Brier Score = (p − o)2
Average those squared errors over the defined sample. Lower is better within a consistent setup. A forecast of 0.80 that resolves Yes has an error of (0.80 − 1)2 = 0.04; if it resolves No, its error is (0.80 − 0)2 = 0.64. The score therefore penalizes confident mistakes much more than modest misses.
Compare scores only on like-for-like samples
Brier Scores depend on the event mix, forecast horizon and sample definition. Compare two strategies on the same contracts, at the same observation time and with the same treatment of missing or cancelled markets. If horizons differ, publish separate scores. A lower score from a near-resolution sample cannot automatically be declared better than a higher score from an early-warning sample.
State a benchmark
A score has meaning only against a stated baseline evaluated on the same observations. Possible baselines include the market-implied probability at the sampling time or a simple historical-rate forecast, provided the baseline is defined before scoring. Show the number of observations and the difference from the baseline. Do not imply that a favorable score by itself proves a tradable edge.
What established forecasting research supports
A 2016 CFA Institute 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 systematic scoring; it does not establish that any particular prediction-market strategy will be profitable.
Separate forecast quality from trading profitability
Maintain two linked but distinct records.
The probability record
- Contract and outcome definition
- Forecast timestamp and time to scheduled resolution
- Probability assigned to Yes
- Information set or model version used
- Official settled outcome
- Calibration group and Brier contribution
This record answers whether the estimates were informative.
The trade ledger
- Order time, side and intended size
- Displayed price and actual fill price
- Partial fills, cancellations and slippage
- Bid-ask spread and available depth
- Fees under the applicable schedule
- Exit price, settlement payment or loss
- Capital tied up and holding period
This ledger answers whether the estimates could have generated net returns under stated execution assumptions. A strategy can have useful probability estimates but fail to earn money because the market price already reflects them, spreads consume the edge, liquidity prevents the desired size, or fees exceed the expected value. A profitable short sample can likewise occur without proving forecast skill.
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Translate a probability view into an executable trade
Before placing a trade, compare your estimated probability with the price you can actually receive, not merely the last displayed quote. Define the payoff, entry price, exit rule and maximum position in advance. Then calculate expected value under the contract’s settlement terms and subtract all known costs. If the expected advantage is smaller than a plausible spread, fee and slippage allowance, the signal is not demonstrated to be tradable.
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Capture both sides of the market
For a Yes position, record the price paid and the amount returned if the contract settles Yes. For a No position, record the corresponding No price and settlement. If you plan to exit before resolution, record the assumed exit quote and a conservative fill condition. Do not use a mid-market price for entry when your order would have crossed the spread.
Model position size and depth
A quote for one small order does not establish that the same price is available for a larger position. Record displayed depth, the amount you attempted, the amount filled and the average fill price. Run a sensitivity case with worse fills so that a paper edge is not mistaken for capacity.
Apply the correct, dated fee schedule
Fee schedules differ by market and can change. Kalshi’s official schedule effective February 5, 2026 includes a formula for specified S&P 500 and Nasdaq-100 contracts; those terms are specific to those contracts and are not a universal charge for every event market. Use the schedule that applied to the actual contract and trade date, and preserve a copy or dated reference in your records. If a schedule is ambiguous, mark the result as conditional rather than assuming zero cost.
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Read the contract rules before deciding that a forecast resolved
The event described in ordinary language may not be the event the contract settles on. Market-specific rules define the criteria for a Yes or No result, the measured value, the timeline and the verification source. The rules summary is an orientation; the full contract terms contain additional conditions.
Check the measured variable
Confirm the exact threshold, unit, geographic area, time window and data release being used. “Inflation rises” is not sufficient if the contract specifies a particular index release and publication date. “A storm makes landfall” is not sufficient if the contract identifies an official agency and a precise location.
Check the verification source and revisions
Settlement may wait for official source data after trading closes. Official figures can be delayed or revised, and the market’s rules determine which release counts. An event that appears obvious from news coverage is not necessarily settled until the designated source and procedure say it is.
Use actual settlement in historical scoring
When evaluating past trades, use the contract’s recorded outcome and settlement time, not an informal interpretation of what happened. A market remaining open after the apparent event is not, by itself, proof that the result has been determined. As the Kalshi Help Center puts it: “Make sure to read market rules before trading!” (March 17, 2026).
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Market activity can be relevant to execution and information quality, but activity is not proof of causation. More volume may coincide with tighter spreads, greater depth or more information; it can also reflect a crowded trade. A low-volume market may be difficult to trade at size, yet volume alone does not establish that its forecasts are inaccurate.
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What the Kalshi study reports
Kalshi Research’s August 2026 study analyzed 2,243,741 resolved markets across eleven categories, from the platform’s launch in 2021 through mid-2026, excluding Exotics. In that dataset and analysis, reported Brier Scores fell from approximately 0.08–0.09 at a three-month horizon to roughly 0.02 at close. The study also reports stronger calibration with greater event-level volume and with more unique traders, and describes calibration, market microstructure, prediction accuracy, participant heterogeneity and belief updating as research themes.
Those figures describe Kalshi’s covered markets and methodology, not universal prediction-market performance. The relationships are observational and do not show that volume causes better forecasts or that every low-volume contract is unusable. No independent replication or market-wide profitability statistic is established here.
Compare two strategies with a disclosed framework
When comparing strategies, publish the setup alongside the result. The following axes prevent a hit-rate table from hiding material differences:
| Axis | What to report | Why it matters |
|---|---|---|
| Probability quality | Calibration by probability group and Brier Score | Shows whether confidence levels match outcomes |
| Forecast timing | Time from observation to resolution, separated by horizon | Near-close information is not equivalent to an early forecast |
| Benchmark | Named baseline scored on the same contracts and horizon | Shows whether the strategy adds information |
| Tradability | Entry and exit assumptions, spread, depth and position size | Tests whether a paper edge can be executed |
| Net results | Fees and transaction costs under the dated schedule | Separates gross signal from realized return |
| Resolution integrity | Contract criteria, source and settlement status | Prevents scoring the wrong event or premature result |
| Evidence strength | Sample size, category mix and observational or causal status | Limits overgeneralization |
A practical evaluation workflow
- Write the protocol. Specify the market categories, contract classes, forecast timestamps, outcome definition, inclusion rules, benchmark, fee assumptions and evaluation period before examining returns.
- Freeze the forecast snapshot. At each scheduled timestamp, save the probability or executable market quote. Do not replace it later with a more favorable price.
- Verify the contract. Save the applicable rules, verification source, scheduled timeline and any amendments. Mark unresolved contracts as unresolved.
- Build probability groups. Report forecast count, average assigned probability and observed Yes frequency for each group.
- Calculate Brier Score. Score each resolved binary outcome, average over the sample and show the baseline difference.
- Reconcile trades. Match each forecast to orders, fills, fees, exits, settlement payments and capital usage. Keep unfilled signals separate from executed positions.
- Stress execution. Recalculate results with realistic spread, slippage, smaller available depth and alternative exit assumptions.
- Segment without cherry-picking. Break out horizons, categories and liquidity tiers that were specified in advance, and show counts for every segment.
- Report uncertainty and limits. State the sample period, number of markets, category mix, missing data, rule changes and whether any observed relationship is merely correlational.
Common evaluation mistakes and their fixes
Counting only winners
Problem: A favorite won 60% of the time, so the strategy is declared successful.
Fix: Publish calibration and Brier Score, including the probabilities assigned to the losing favorites.
Mixing horizons
Problem: Early forecasts and close-to-settlement prices are averaged together.
Fix: Sample and score each horizon separately.
Comparing unlike event mixes
Problem: One strategy covers highly predictable events while another covers uncertain ones.
Fix: Compare on the same contracts or disclose category composition and use a common benchmark.
Using theoretical prices
Problem: Backtests assume every order fills at the midpoint or last trade.
Fix: Use executable prices, depth, partial fills, fees and a stated exit rule.
Assuming a visible event is settled
Problem: News coverage is treated as the official result.
Fix: Follow the contract’s verification source and settlement record.
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Treating correlation as causation
Problem: Higher-volume markets show better scores, so volume is said to cause accuracy.
Fix: Describe the relationship as observational unless a causal design supports more.
How to present results so readers can audit them
A concise but complete report should include:
- The exact market universe, dates and contract categories
- Forecast timestamp or horizon
- Number of resolved and unresolved observations
- Calibration table with counts
- Brier Score, baseline score and scoring formula
- Gross and net trading results, with fee and spread assumptions
- Fill rate, average position size and depth constraints
- Settlement-rule treatment and any revised or delayed official data
- Subgroup results with sample sizes
- Limitations, including whether findings are platform-specific or observational
This format lets a reader distinguish a probability model that forecasts well from a trading process that can actually capture value.
Bottom line: the test is probability, execution and settlement together
Evaluate prediction-market strategies as repeated probability forecasts first, not as a sequence of favorite picks. Calibration shows whether stated probabilities occur at their claimed frequencies; Brier Score measures the size of probability errors; a common benchmark shows whether the strategy adds information. A separate trade ledger then tests whether that information survives prices, fills, spreads, depth, fees and exits. Finally, contract rules and official settlement determine which outcome belongs in the score. The Kalshi Research figures provide a useful platform-specific example of how horizon and activity can relate to calibration, but they do not establish a universal edge or profitability rule.
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Can a strategy be well calibrated and still lose money?
Yes. Calibration describes the quality of probabilities, while profitability depends on the prices available, contract payoff, fees, spread, liquidity, position size and exit decisions.
Should I score a market before it officially settles?
No. Keep it unresolved until the contract’s stated verification source and settlement procedure produce the official outcome; news or an apparent event is not a substitute.
Is a lower Brier Score always evidence that one strategy is better?
Only when the strategies are scored on comparable contracts, outcome definitions, horizons and samples, with the same treatment of missing observations and a stated benchmark.
Does high volume prove that a market is more accurate?
No. Higher volume and more participants may be associated with better calibration in a platform study, but that relationship is observational and does not prove that volume causes accuracy.
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