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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteA prediction-market favorite can win most of the time and still be a bad trade. The key distinction is between the chance that a contract settles at $1 and the price you must pay for that chance. A 90-cent contract needs to win more than 90% of the time before fees—and still more after fees, spread, and slippage—to produce a positive expected return. If your probability estimate is exactly the market price, you have no pre-cost edge, even when the favorite wins repeatedly.
What “favorite” means in a prediction market
In a binary market, a contract settles at a stated amount—often $1—if an event occurs and at $0 if it does not. Its quoted price is commonly read as an implied probability: a 70-cent contract appears to represent about a 70% chance of settling at $1. That interpretation is useful, but it is not a guarantee, and the quote is not automatically a scientifically calibrated probability.
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A favorite is simply the side with the higher quoted probability. It can be the “Yes” side of a question such as whether a candidate wins, a temperature exceeds a threshold, or a central bank changes rates. Calling it a favorite says that the market assigns it a higher chance than the alternative; it does not say the contract is cheap.
To decide whether buying is sensible, separate three questions:
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- What probability does the executable price imply?
- What probability do you independently estimate after reviewing the event and the settlement rule?
- After all costs, is your estimate high enough to create positive expected value?
The break-even arithmetic
Consider a simplified binary contract paying $1 on success. Let p be the price you actually pay, q your estimated probability of success, and f a per-contract cost for fees and execution. Before costs, expected profit per contract is:
Expected profit = q − p
After a dollar-denominated cost, the simplified expression is:
Expected profit = q − p − f
The buyer breaks even when the estimated probability equals the amount paid plus costs. For a contract with a payout P rather than $1, the simplified break-even condition is q > (p + f) / P. Actual exchanges can calculate fees on a different base, so use the venue’s current formula rather than assuming a flat deduction.
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| Executable ask | Your estimated win probability | Gross expected profit | Illustrative cost | Net expected profit |
|---|---|---|---|---|
| $0.90 | 90% | $0.00 | $0.01 | −$0.01 |
| $0.90 | 93% | $0.03 | $0.01 | $0.02 |
| $0.90 | 88% | −$0.02 | $0.01 | −$0.03 |
| $0.60 | 65% | $0.05 | $0.01 | $0.04 |
These are arithmetic illustrations, not market statistics. A 90-cent ask and a 90% estimate are merely break-even before costs. The favorite may be likely to win, but the purchase has no mathematical cushion. If you pay the displayed midpoint while the available ask is higher, your calculation is already too optimistic.
How a favorite loses money despite winning often
You paid too much for the probability
Suppose ten comparable trades each cost $0.90 and each has a true 90% chance of winning. On average, nine contracts settle at $1 and one settles at $0. The nine wins return $9 against $9 paid, before fees. The losing contract removes the entire $0.90 stake that was not offset by a profit on the other nine. A high win count therefore does not create a positive return when the price already reflects that high win rate.
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If the true chance is 89% but you buy at $0.90, the expected loss is one cent per contract before costs. That small disadvantage can accumulate over hundreds of trades even though the majority of positions win.
Fees, spread and slippage move the hurdle
The price visible on a chart may be a last trade, midpoint, or indicative quote. Your order fills at an executable bid or ask, and a thin order book can move while you trade. A commission, settlement charge, or taker fee reduces the payoff. The relevant input is therefore the all-in amount: price paid, expected execution slippage, and the fee under your maker or taker role.
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Small errors matter most near the favorite boundary. At a 90-cent price, overestimating the chance by three percentage points changes a modest theoretical edge into a loss. News, polling error, model uncertainty, correlated events, and an ambiguous definition of the outcome can all make an apparently precise estimate unreliable.
The settlement rule differs from the event you had in mind
A contract may resolve using a specified data release, timestamp, geographic area, or official definition. “The event happened” in ordinary language is not enough. A favorite can lose because the settlement source records a different value, a deadline passes before an expected announcement, or an exception in the rules applies.
Position size turns variance into a cash loss
Even a genuinely favorable bet can lose on a particular outcome. A 95% contract still loses about five times in 100 trials if the estimate is accurate. Concentrating too much capital in one high-probability position can force a large realized loss before the long-run edge has time to appear. Expected value describes an average over repeated comparable opportunities, not a promise for one contract.
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What empirical studies actually find
The strongest findings in the available evidence are venue- and sample-specific. They describe average behavior in Kalshi markets, not a universal rule for every exchange, category, or individual trade.
| Study | Data and scope | Main result | What it does not prove |
|---|---|---|---|
| Kalshi Research, August 2026 | 2,243,741 resolved markets across 11 categories, from the platform’s 2021 launch through mid-2026 | Aggregate prices track realized outcomes more closely as resolution approaches. Excluding Exotics, the reported Brier score falls from approximately 0.08–0.09 at a three-month horizon to roughly 0.02 at close. | These are calibration measures, not returns from buying favorites, and they do not guarantee profit on any position. |
| Bürgi, Deng and Whelan, 2025, “Makers and Takers: The Economics of the Kalshi Prediction Market” | Transaction-level data covering more than 300,000 contracts | The analysis reports a favorite–longshot bias: low-price contracts won less often than their prices required to break even, while higher-price contracts won more often and showed small positive returns in that study. | The pattern is an average result for the studied Kalshi sample, not a sure-fire strategy or a claim about every high-price contract. |
| Whelan, University College Dublin, 2023 | Working-paper analysis of prediction-market fee structures | Fees affect prices and their relationship with average public beliefs. In the Kalshi model examined, price-dependent fees were less distortionary than flat fees. | This does not supply a current fee quote or establish that one fee schedule is optimal for your trade. |
A Brier score measures probabilistic accuracy, with lower values indicating better forecasts under that scoring rule. The reported move from roughly 0.08–0.09 to about 0.02 says that aggregate forecasts became better calibrated near closing; it does not say that buying near closing is automatically profitable. By then, new information may already be incorporated into the quote, and the remaining spread and fees can eliminate any edge.
Why time to resolution changes the calculation
Early in a market’s life, the quoted probability may reflect sparse information, wide uncertainty, and low liquidity. Near settlement, more facts are known and the outcome space narrows. The 2026 Kalshi Research study reports improved aggregate calibration as markets approach resolution, but calibration and profitability are different properties.
For a trade at a long horizon, ask whether you are being paid for bearing uncertainty and whether you can hold through intervening price swings. For a late trade, ask whether the apparent discrepancy is real or merely the cost of crossing a spread when little time remains. A late price can be more accurate and still leave no positive expected value after costs.
- Long horizon: account for scheduled information, model drift, and the opportunity cost of capital.
- Near close: verify the exact settlement timestamp and source, then use the executable quote rather than a stale chart price.
- Any horizon: treat a probability estimate as a range, not a single overconfident number.
How fee design and market microstructure affect favorites
A flat fee takes the same number of cents from trades at different prices. A price-dependent fee changes with the contract price or payout. Those designs alter the relationship between displayed prices and participants’ underlying beliefs. Whelan’s 2023 working paper finds that the effect depends on the structure and reports less distortion from price-dependent fees in the Kalshi model it studies.
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Do not turn that result into a current fee assumption. Before trading, check the venue’s live schedule for:
- maker versus taker charges;
- minimum or maximum fees;
- whether fees are charged on entry, settlement, profit, or notional value;
- withdrawal or account charges that affect your realized return; and
- how partial fills and canceled orders are handled.
Use the actual ask for a buy and the actual bid for a sale. A contract quoted at 90 cents is not interchangeable with a 90-cent fill if the order book offers only 91 cents for the quantity you need. Record the fill price and fee with each position so a later win-rate calculation does not hide a negative net return.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical test before buying a favorite
- Read the settlement rule first. Identify the source, cutoff time, geographic scope, threshold, and any tie or cancellation provision.
- Record the executable price. Use the ask for an immediate purchase, including the quantity available at that level. Do not substitute the last trade or midpoint.
- Estimate a probability independently. Use a range such as 88%–92% when the evidence does not justify a single point estimate. Document the assumptions that could move the range.
- Calculate the cost-adjusted hurdle. Add the applicable fee and a realistic slippage allowance to the amount paid. For a $1 payout, your estimated probability must exceed that total.
- Stress-test the estimate. Recalculate using a less favorable probability, a one-cent worse fill, and the possibility that the settlement source reports an unexpected value.
- Compare alternatives. A lower-priced contract with a larger probability gap may offer better expected value than the headline favorite, provided its rules and liquidity are acceptable.
- Size for the loss, not the win rate. Decide in advance how much capital can be lost if the favorite’s minority outcome occurs.
- Log the result correctly. Track net dollars and return on capital after fees, not just how many positions won.
Common analytical mistakes
Confusing implied probability with truth
Price is information produced by trading. It can be informative and still be biased, stale, or distorted by fees and liquidity. Treat it as a benchmark to challenge, not as an oracle.
Evaluating a strategy by wins alone
A strategy that wins 94 of 100 trades can lose money if the 94 wins earn one cent each and the six losses cost 90 cents each. Report average profit per contract, total net profit, return on capital, drawdown, and the distribution of outcomes.
Applying a sample-wide pattern to one contract
The favorite–longshot result from the 2025 Kalshi study is an average relationship across a large sample. It does not identify which particular favorite is overpriced today, and it does not show that every category or horizon behaves the same way.
Ignoring the side you could sell
If you already hold a contract, the relevant exit is the executable bid, not the theoretical settlement value. A favorable forecast can still produce a poor realized return if you must exit through a wide spread or pay another fee.
Using an outdated fee schedule
Fee terms can change and may differ by account role or order type. Re-run the calculation whenever the venue changes its schedule or you switch between maker and taker execution.
How to compare two favorite contracts
When two contracts both look likely to win, compare the variables that determine expected value rather than choosing the one with the higher headline probability.
| Comparison | Questions to answer |
|---|---|
| Price versus independent probability | What is the executable ask, and how far is your defensible estimate above it? |
| All-in break-even hurdle | What fee, spread, and slippage must the trade overcome? |
| Time remaining | How much information can arrive before settlement, and is the market’s calibration likely to change over that horizon? |
| Settlement design | Are the data source, deadline, threshold, and cancellation terms equally clear? |
| Liquidity | Can you enter and exit the intended size without moving the price materially? |
| Dependence on other positions | Would the same news make several favorites lose together? |
A contract with a lower win probability can be the better purchase when its price is lower by even more than the probability difference. Conversely, a 98% favorite can be unattractive if it costs 98.5% of the payout after execution and fees.
What a sensible conclusion looks like
High-probability prediction-market favorites lose money for an ordinary reason: probability describes how often an outcome should occur, while return depends on the price paid to obtain that outcome. A favorite priced at its estimated chance has no pre-cost edge; a favorite priced above that chance has negative expected value. Fees, spreads, slippage, settlement details, estimation error, and position size can widen the loss.
Research on Kalshi finds that prices contain useful information, that aggregate calibration improves near resolution, and that a favorite–longshot bias appeared in particular samples. Those findings are valuable benchmarks, not guarantees. The trade-level decision remains the same: use the executable price, estimate the probability independently, calculate the fee-adjusted break-even point, and risk only an amount you can afford to lose when the favorite’s less-likely outcome occurs.
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