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A prediction-market favorite can win most of the time and still lose money for its buyers. The reason is simple: the chance of winning and the price paid for that chance are separate questions. A contract priced at 90 cents needs a probability above 90%—after fees and execution costs—to have positive expected value. If its true chance is exactly 90%, it is only break-even before costs, and any fee or slippage turns the trade negative.
The same logic works in reverse. A contract that loses more often can be a good purchase when its price is low enough relative to a carefully estimated probability. The useful question is not “How often does this favorite win?” but “Is my estimated probability higher than the all-in break-even probability at the price I can actually execute?”
Probability is not the same as a profitable price
What a prediction-market price tells you
In a binary contract, the buyer receives a fixed payout if an event occurs and nothing if it does not. A quoted price is commonly read as an implied probability. A 90-cent contract appears to represent a 90% chance of success, while a 25-cent contract appears to represent 25%.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThat interpretation is a starting point, not a guarantee. Prices reflect the orders currently available, the information and beliefs of participants, liquidity, fees, the contract’s settlement wording and the time remaining. The displayed price may also be a midpoint between bids and asks rather than a price at which you can immediately buy or sell.
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To decide whether a position is attractive, replace the market’s implied probability with your own probability estimate and compare it with the complete cost of entering the trade. A high probability can describe a likely outcome without describing a good investment.
The break-even calculation
For a simple contract paying $1 on success and $0 on failure, let q be your estimated probability, p the price paid and f all per-contract costs expressed in dollars. In a simplified model:
Expected profit = q − p − f
The position has positive expected value only when q > p + f. If the contract pays a different amount, use the equivalent threshold:
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Actual exchanges can calculate fees as a percentage, charge different amounts to makers and takers, or apply fees to a transaction’s notional value or payout. Use the venue’s current fee formula rather than assuming a flat cost. The equations above are a transparent way to understand the decision, not a substitute for the settlement and fee rules of a particular contract.
A 90-cent favorite can be a losing trade
One contract
Suppose the executable ask is 90 cents and your best estimate of the event’s probability is 90%. Before costs, the expected profit is:
0.90 − 0.90 = $0.00
The contract will win roughly nine times out of ten if the estimate is accurate, but that does not create a positive edge. Add a one-cent fee and the expected result becomes:
0.90 − 0.90 − 0.01 = −$0.01
You have bought a likely winner at a price that already consumes the entire estimated advantage. The tenth-outcome loss is not a contradiction; it is part of the probability you paid for.
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Why a lower-probability contract can be better
Consider these illustrative, arithmetic-only examples for $1 contracts. The fee is shown as one cent per contract solely to demonstrate the calculation; it is not a current venue quote.
| Estimated chance of success | Executable ask | Illustrative cost | Expected profit | Interpretation |
|---|---|---|---|---|
| 90% | $0.90 | $0.01 | −$0.01 | Likely to win, but priced too high after cost |
| 94% | $0.90 | $0.01 | $0.03 | Positive expected value if the estimate is sound |
| 80% | $0.70 | $0.01 | $0.09 | Lower win rate, larger price edge |
| 60% | $0.40 | $0.01 | $0.19 | Not a favorite, but potentially the strongest value |
These figures do not predict any particular event. They show why “favorite” and “good buy” are different labels. A contract can have a higher chance of winning and a worse expected return than another contract if the first one is more expensive relative to its chance.
Repeated trades do not remove a negative edge
Over many positions, a small disadvantage can accumulate even when most individual positions win. If you repeatedly buy contracts whose expected profit is negative, the average result remains negative before unusual luck, bankroll limits or changing information are considered. A high hit rate can make losses psychologically difficult to diagnose because the losing positions are less frequent but can be large relative to the small gains on winners.
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Fees and execution raise the hurdle
Use the executable price, not a convenient headline number
When buying, the relevant number is normally the ask you can actually take. A midpoint or last-traded price can be unavailable at the size you want. If the order book is thin, buying several contracts may move you through progressively higher offers. That difference is slippage, and it belongs in the cost term.
- Record the ask or bid that matches your side of the trade.
- Include the expected fill price for your entire order, not just the first available quote.
- Account for exchange fees, clearing charges or other per-contract costs under the venue’s live schedule.
- Check whether you are providing liquidity as a maker or removing it as a taker, since treatment can differ.
- Read the settlement criteria: an apparently favorable probability estimate is unusable if the event definition differs from your model.
Fee design can change observed prices
Fees do more than reduce a trader’s net payout. They can alter which orders participants submit and therefore affect the relationship between quoted prices and public beliefs. A 2023 University College Dublin working paper by Whelan reports that the effect depends on fee structure. In the Kalshi fee model examined there, price-dependent fees were less distortionary than flat fees.
That finding is about the model and period studied, not a current fee quote or a universal rule. Before trading, consult the venue’s current schedule and recalculate the break-even probability for the exact contract, side and order type.
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Researchers often use “favorite–longshot bias” to describe an average pattern in which low-priced contracts win less often than their prices require to break even, while high-priced contracts win more often and can show small positive returns in a particular sample. It is an empirical tendency, not a law that identifies the correct price of every contract.
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| Study | Scope reported by the authors | Main finding relevant to buyers | Important limit |
|---|---|---|---|
| Kalshi Research, August 2026 | 2,243,741 resolved markets across 11 categories, from the venue’s 2021 launch through mid-2026 | Aggregate calibration improves as markets approach resolution. Excluding Exotics, approximate Brier scores fall from 0.08–0.09 at a three-month horizon to roughly 0.02 at close. | Brier scores measure probabilistic accuracy, not the profit of a single favorite or a trading strategy. |
| Bürgi, Deng and Whelan, 2025, “Makers and Takers: The Economics of the Kalshi Prediction Market” | Transaction-level data covering more than 300,000 contracts | Prices are informative and become more accurate near closing. Low-price contracts won less often than required to break even, while higher-price contracts won more often and showed small positive returns in the paper’s analysis. | The result is specific to the Kalshi sample, contract environment and analysis; it does not establish a universal edge. |
| Whelan, 2023, University College Dublin working paper | Analysis of how prediction-market fee structures affect prices and participants | Fee effects vary by design; the Kalshi model studied found price-dependent fees less distortionary than flat fees. | It does not provide a current fee schedule or prove that any individual contract is profitable. |
The table’s findings should be read together with venue, category, contract design and time horizon. They do not show that every favorite is overpriced, that every longshot is a bargain, or that the same pattern holds uniformly on every prediction market.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why calibration improves near resolution without guaranteeing profit
What the Brier-score figures mean
A Brier score evaluates probabilistic forecasts by comparing a stated probability with the eventual binary result; lower is better. Kalshi Research’s approximate decline from 0.08–0.09 three months before resolution to roughly 0.02 at close, with Exotics excluded, indicates that aggregate prices tracked outcomes more closely nearer settlement in that study.
Better calibration does not mean a late buyer automatically earns money. By the time uncertainty has fallen, the price may have moved to reflect the same information. If the ask already equals or exceeds your updated probability after costs, there is no positive edge left. A forecast can become more accurate while the opportunity to profit disappears.
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Information arrives at different speeds
Markets can reprice when polls, economic releases, court decisions, weather data or other settlement-relevant information appear. The time remaining determines how much unknown information can still arrive and how much capital is tied up while waiting. Compare contracts at the same horizon where possible; a 90% estimate one day before settlement is not interchangeable with a 90% estimate three months out.
Near-close contracts also require careful attention to settlement timing and rules. A market that appears nearly certain can remain exposed to a final qualifying condition, an extension, a cancellation or a definition that differs from ordinary language.
How to test whether a favorite has an edge
- Read the settlement rule first. Identify the exact event, cutoff time, data source and treatment of cancellations or revisions. Your probability estimate must refer to that definition.
- Capture the executable quote. For a purchase, use the current ask at your intended size. Note the spread and visible depth rather than relying on a chart midpoint.
- Estimate probability independently. Use a documented model, base rate, forecast process or other method that is separate from the price you are evaluating. Record assumptions and the date of the estimate.
- Convert all costs into the same units. Include venue fees, expected slippage and any cost associated with the order type. If the fee formula is nonlinear, calculate it for the actual quantity.
- Compute the break-even probability. For a $1 payout, add the all-in cost to the amount paid. For another payout, divide the total amount at risk by the payout.
- Stress-test the estimate. Recalculate using a lower and higher probability, a worse fill and a delayed resolution. A tiny edge that vanishes under modest changes is fragile.
- Compare alternatives. Examine contracts with different prices, categories and horizons. The favorite is attractive only if its risk-adjusted, fee-adjusted edge is better than available alternatives.
- Plan the exit and settlement exposure. Decide whether you can hold to resolution and how you will handle a wide spread or new information. An apparently positive entry can become costly to unwind.
Common reasons a favorite loses money
- The price already includes the good news. Everyone may agree that an outcome is likely, so competition bids the contract up until the expected return is zero or negative after costs.
- The probability estimate is overconfident. A 90% judgment is not the same as a 99% certainty. Model error, correlated information and overlooked failure modes can make favorites less reliable than they appear.
- Fees consume a narrow edge. A two-cent advantage before fees can become a loss after commissions, spread and slippage.
- The quote is not executable. A last trade at 90 cents does not mean you can buy your desired quantity at 90 cents.
- Settlement differs from the headline. The contract may depend on a specific timestamp, official release, threshold or data revision.
- Capital is tied up. Even a small positive expected return may not compensate for the time and liquidity risk of waiting for a distant resolution. This is an opportunity-cost consideration, not a change to the binary payoff equation.
- Results are noisy. A correctly priced favorite can lose. One outcome cannot establish that the estimate or the market was wrong.
Comparing two contracts without being misled by win rate
| Question | Contract A: high-probability favorite | Contract B: lower-probability alternative |
|---|---|---|
| What should you estimate? | Its probability under the exact settlement rule | Its probability under the exact settlement rule |
| What price matters? | Executable ask for a buyer, including size and spread | Executable ask for a buyer, including size and spread |
| What determines value? | Estimated probability minus price and costs | Estimated probability minus price and costs |
| What can mislead you? | Confusing frequent wins with positive expected return | Rejecting it solely because it loses more often |
| What additional checks matter? | Late information, settlement details and whether the edge survives fees | Model uncertainty, liquidity and the possibility that a low price reflects a genuinely low chance |
Both contracts require the same discipline. The lower-probability alternative is not automatically better; it becomes better only when its estimated probability exceeds its all-in break-even threshold by a larger or more reliable margin.
A practical decision checklist
Before submitting an order, confirm:
- I can state the settlement condition in one precise sentence.
- My probability estimate was made independently of the market quote.
- I used the executable price and intended order size.
- I included the venue’s current fee formula, spread and plausible slippage.
- The estimated probability is above break-even after those costs.
- The edge remains under reasonable probability and price changes.
- I understand how long capital may be locked up and whether I can exit.
- I am treating the position as uncertain, not as a guaranteed payout.
High-probability contracts can be useful when the market price understates a well-supported probability estimate. They can also be expensive insurance against an outcome that is merely likely. Separating probability, price, costs and realized outcome is what prevents a high win rate from being mistaken for a profitable strategy.
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