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A prediction-market favorite can win most of the time and still be a poor investment. The key distinction is between how likely an outcome is and how much you pay for that likelihood. A binary contract bought for 90 cents needs a win probability above 90%—and usually higher after fees, spread and slippage—to have positive expected value. A favorite can also lose outright in a single event, because a 90% probability still implies about a one-in-ten chance of failure.
Probability is not the same as a profitable price
A binary prediction-market contract typically pays $1 if a specified event occurs and $0 if it does not. Its quoted price is often read as an implied probability: a 90-cent contract is commonly interpreted as roughly a 90% market estimate. That interpretation is useful, but it is not a guarantee, and it is not proof that 90% is the event’s true probability.
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For a buyer, the investment question is narrower: is the buyer’s best probability estimate higher than the amount paid after all trading costs? If your estimate is 94% and an executable ask is 90 cents, there may be an edge before costs. If your estimate is exactly 90%, the position is only break-even before costs. If your estimate is 88%, a high chance of winning does not rescue the purchase from negative expected value.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Prices can be informative without being perfectly calibrated. Kalshi Research’s August 2026 calibration study examined 2,243,741 resolved markets across eleven categories from the venue’s 2021 launch through mid-2026. It found that aggregate prices tracked realized outcomes more closely as markets approached resolution. That is evidence about a large sample, not a promise about any individual contract.
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The break-even calculation
Use a simplified model before deciding whether a favorite is worth buying. Let q be your estimated probability of success, p the executable price, and f all fees and other per-contract costs expressed in dollars. For a $1 payoff, pre-cost expected profit is:
Expected profit = q − p
After costs, the simplified result is:
Expected profit = q − p − f
The break-even probability is therefore the amount paid plus costs, divided by the contract’s payout. If the contract pays something other than $1, use the same logic with the actual payout in the denominator. Real venues may calculate fees from price, quantity, side of the trade or other variables, so replace f with the venue’s current formula and use the price you can actually execute—not a midpoint or last-traded price.
Arithmetic example: a 90-cent favorite
Suppose the ask is $0.90 and the contract pays $1 on success. At a 90% estimated probability, the pre-fee expected profit is 0.90 − 0.90 = $0. A fee, bid-ask spread or slippage turns that into a loss in expectation. At a 94% estimate, the pre-fee expected profit is four cents per contract, leaving only four cents to cover every cost and modeling error.
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The same contract can lose the entire stake in one trial even when the 90% estimate is accurate. A 10% failure chance is not an anomaly; it is part of the stated probability. Over many trades, realized results can also differ from estimates for a while, especially when positions are concentrated in a small number of events.
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What the favorite–longshot bias actually says
Favorite–longshot bias describes an average relationship observed in some betting and prediction-market data: low-priced, unlikely contracts can win less often than their prices require to break even, while high-priced favorites can win slightly more often than their prices imply. It is a statistical pattern, not a rule that identifies a winning trade every time.
Bürgi, Deng and Whelan’s 2025 study, Makers and Takers: The Economics of the Kalshi Prediction Market, analyzed transaction-level data on more than 300,000 contracts. In its Kalshi sample, the authors reported that low-price contracts won less often than needed to break even and that higher-price contracts won more often, with small positive returns in the paper’s analysis. The result is tied to that venue, sample and methodology. It does not show that every Kalshi favorite is underpriced, nor that the pattern transfers unchanged to another exchange, category or time horizon.
A small average return can also be overwhelmed by implementation. If the observed advantage is only a fraction of a cent after converting prices into expected returns, a fee schedule, a wide spread, partial fills or an inaccurate probability estimate can erase it. Treat the bias as a reason to test prices and costs—not as a shortcut around them.
Why prices tend to improve near resolution
Information arrives over the life of a contract. As an event approaches its settlement time, uncertainty usually narrows and more relevant facts become public. Kalshi Research’s 2026 study reported approximate Brier scores of 0.08–0.09 at a three-month horizon and roughly 0.02 at close, excluding Exotics. A lower Brier score indicates more accurate probabilistic forecasts under that scoring measure; it is not a return figure and does not establish that a particular late trade is profitable.
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Nearer resolution, a price may therefore be better calibrated while offering less room for a mispricing. New information can already be incorporated into the quote. The remaining edge may be too small to cover the cost of entering and exiting. A late trade is not automatically safer financially simply because the outcome is easier to forecast.
Fees and market mechanics can change the hurdle
Two contracts with the same displayed price can have different expected returns if their fee treatment, liquidity or execution conditions differ. Compare the executable ask for a buyer, the executable bid for a seller, the expected fill size and whether you are providing or taking liquidity. A displayed last trade is historical; it may not be available now.
Whelan’s 2023 University College Dublin working paper, How Do Prediction Market Fees Affect Prices and Participants?, reports that fees affect prices and their relationship with average public beliefs. Its results depend on fee structure; in the Kalshi model examined, price-dependent fees were less distortionary than flat fees. This finding does not supply a current fee quote. Check the venue’s live schedule for the contract type and date, then include those charges in the break-even calculation.
Execution costs include more than an explicit commission:
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- Spread: the gap between the best bid and ask. Crossing it can consume much of a small statistical edge.
- Slippage: a worse average fill when your order moves through several price levels.
- Partial fills: only part of the intended position may execute, changing exposure and average price.
- Exit costs: selling before settlement can incur another spread and fee; holding to settlement may leave capital tied up.
- Settlement rules: the official source, cutoff time and definition of the event determine whether a seemingly correct forecast pays.
How to evaluate a high-probability favorite
- Read the contract language. Identify the exact event, geographic scope, time zone, observation window, settlement source and criteria for a yes or no result. Do not base a trade on a headline that is broader than the rules.
- Record the executable price. For a purchase, use the current ask at the quantity you intend to trade. Note the time, available depth and whether the quote is likely to disappear.
- Make an independent probability estimate. Use a documented model, base rate or transparent forecast process. Separate your estimate from the market price so you can identify whether an edge exists.
- Compute the cost-adjusted threshold. Add the venue’s applicable fees and a realistic allowance for spread and slippage. Divide the total expected outlay by the payout to obtain the probability required to break even.
- Stress-test the estimate. Calculate results if your probability is several points lower, the fill is worse, or settlement timing changes. A trade that is attractive only under one precise assumption is fragile.
- Check time and liquidity. Compare the remaining time to resolution, typical volume, open interest if supplied by the venue, and the cost of exiting. Better calibration near close does not remove liquidity risk.
- Size for the loss case. A favorite can still settle at zero. Use a position size that remains acceptable if the stated failure probability occurs, and avoid treating a high win rate as permission to risk the bankroll.
- Record the thesis and result. Log the entry price, estimated probability, fees, settlement result and realized return. Review calibration over a group of comparable contracts rather than judging skill from one outcome.
Comparing two apparent favorites
When two contracts both advertise a high chance of success, compare the complete economics rather than choosing the one with the higher percentage. The relevant dimensions are:
| Comparison | What to examine | Why it matters |
|---|---|---|
| Quoted price versus estimated probability | Your independently estimated probability and the executable ask | A lower-priced contract can have more expected value even if it wins less often. |
| Cost-adjusted break-even | Price, fee formula, spread and slippage | Costs raise the probability required for a positive expected return. |
| Time to resolution | Horizon, information schedule and opportunity cost of capital | Calibration may improve near close, while the remaining price edge may shrink. |
| Venue and category | Contract design, settlement source and evidence relevant to that venue | Findings from one sample do not automatically apply elsewhere. |
| Liquidity and role | Depth, likely fill, and whether you make or take liquidity | Execution can determine whether a theoretical edge survives in practice. |
If a value is unavailable, mark it as unknown rather than substituting the headline probability. A contract with a 97% estimated chance at 97 cents may be inferior to one with a 90% chance at 84 cents once costs are included.
Why a high win rate can still produce a negative account balance
The price can be too high
Winning 95 out of 100 trades sounds strong, but if each contract was priced as though it had a 96% chance—and costs consumed the difference—the account can lose despite the 95% win rate. Return depends on the surplus between probability and price, multiplied by the amount risked, not on wins counted without their prices.
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Buying a 90-cent contract risks 90 cents to make 10 cents before costs. One settlement at zero removes the profit from nine 10-cent winning contracts. The exact break-even count depends on entry prices, fees and payout, so count dollars won and lost rather than percentages of trades.
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Estimates can be correlated
Several favorites may depend on the same weather report, election result, economic release or other underlying factor. If that factor surprises the market, multiple positions can lose together. A list of individually high probabilities is not the same as a diversified portfolio.
Capital has a time cost
A contract that ties up funds for months can have a lower annualized return than a shorter trade with the same per-contract edge. The cited calibration studies do not establish a universal annualized strategy return; calculate holding time separately when comparing opportunities.
What the evidence does—and does not—establish
The Kalshi studies provide useful constraints on interpretation. Prices contain information, and aggregate calibration in the cited 2026 study improved toward resolution. The 2025 Kalshi analysis reported a favorite–longshot pattern and small positive returns for higher-price contracts in its sample. The 2023 fee paper showed that fee design can alter the link between prices and beliefs.
None of these findings proves a sure-fire strategy. They do not establish that the pattern is uniform across venues, event categories, horizons or individual positions. They also do not replace current contract rules, live prices or a personal probability estimate. Keep the venue, sample and date attached whenever you use these findings to make a decision.
A concise pre-trade checklist
- Is the settlement definition unambiguous?
- What price can you actually execute at your intended size?
- What probability do you estimate independently, and what evidence supports it?
- What fee formula and spread apply today?
- Is expected profit still positive after realistic slippage?
- How much can be lost if the favorite’s failure probability occurs?
- Are several positions exposed to the same underlying event?
- How long will capital remain committed, and what is the exit plan?
The Bottom Line
A high-probability favorite is only attractive when its executable price is below your cost-adjusted probability estimate. Check the contract, calculate the break-even threshold, include every trading cost, and size for the possibility that even a genuine favorite loses.
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