What Actually Reversed in Kalshi’s Longshot Data?

No research reviewed here establishes a platform-wide reverse favorite-longshot bias on Kalshi. Broad transaction-level research through April 2025 finds the conventional pattern: cheap contracts underperform while higher-priced contracts perform better. A weather-market analysis suggests a possible exception in cheap tail bands, while a post-sports-launch study uses “reversal” to mean that certain longshot returns changed from positive to negative—not that Kalshi longshots became broadly underpriced.

Short answer: Kalshi’s overall bias has not been shown to be reversed

A conventional favorite-longshot bias occurs when low-probability contracts trade too richly relative to their eventual outcomes, while favorites trade relatively cheaply. Buyers consequently tend to lose more on longshots than on favorites.

A reverse favorite-longshot bias is the opposite relative pattern: longshots are systematically underpriced compared with favorites. Establishing that pattern requires comparisons across price levels, not merely evidence that one bucket of cheap contracts settled more often than its quoted prices implied.

That pricing question is also distinct from investment performance. Calibration measures whether contracts settle successfully as often as their prices suggest. Gross realized returns incorporate purchase prices and payouts. Executable net returns also account for fees, spreads, slippage, liquidity, and holding time.

A time-series reversal is different again. If returns on the same kind of position are positive in one period and negative in another, the return has reversed sign. That does not show that the market moved from conventional bias to reverse bias, or vice versa.

This distinction reconciles the principal Kalshi findings:

• Broad research compares low- and high-priced contracts across a large transaction sample.
• The weather analysis examines cheap tail bands within a particular contract structure.
• The sports-launch analysis asks whether longshot taker activity and returns changed after a specific date.

They address related but different questions.

The broad Kalshi evidence shows the conventional bias

A working paper by Constantin Bürgi, Wanying Deng, and Karl Whelan studies Kalshi transaction data from 2021 through April 2025. Its filtered sample contains 46,282 contracts from 12,403 events and 313,972 purchased-contract price observations after both YES and NO positions are included. Contracts purchased below 10 cents reportedly lost more than 60% of invested money, while those above 50 cents produced small, statistically significant positive returns. The pattern appeared among both makers and takers but was stronger for takers; makers earned higher overall returns under the period’s fee structure, when takers paid transaction fees and makers did not. Included markets had to remain open for at least 24 hours, record at least $1,000 in total closing volume, and finish with a bid-ask spread no wider than 20 cents; hourly-reset markets were excluded. Maker fees began only after the sample ended, according to the Bürgi, Deng, and Whelan working paper.

Those results indicate conventional longshot overpricing, but their scope matters. The filters exclude low-volume, wide-spread, hourly, and very short-duration contracts. The findings therefore should not be extended automatically to every market available on Kalshi.

The April 2025 endpoint also prevents the paper from establishing whether the measured maker advantage or the size of the bias persisted after maker fees and other market conditions changed. The paper is a working paper rather than settled peer-reviewed consensus.

Nor do positive historical average returns for higher-priced contracts establish a guaranteed or currently executable strategy. Actual results depend on entry price, order role, fees, spread, available size, and time to settlement. A historical pattern can also weaken as participants respond to it or the platform’s market mix changes.

A comparison table resolves the three meanings of “reversed”

The sources appear to conflict only when different periods, market segments, and units of analysis are treated as equivalent.

Source	Platform, period, and unit	Meaning of “reversal” and main result	Fees and principal limitation
Broad Kalshi working paper	Kalshi, 2021–April 2025; purchased-contract observations across filtered events	No reversal: purchases below 10 cents lost more than 60% of invested money, while contracts above 50 cents had small positive returns. This is conventional favorite-longshot bias.	Period-specific taker fees were incorporated; makers then paid no fee. The filtered sample cannot describe excluded markets or later conditions.
OddsShopper weather analysis	Kalshi weather ladders; study period and current sample size not reported in the cited material; individual tail-band contracts	“Reversed” means cheap tail bands reportedly settled more often than quoted prices implied. This is consistent with a narrow reversal but does not compare the full price gradient.	Cost-adjusted returns, effect size, statistical tests, execution assumptions, and detailed fee treatment are not reported.
Post-sports-launch event study	Kalshi before and after January 23, 2025; taker purchases from 1 to 20 cents, including a 60-day-resolution subsample	Longshot returns in the 60-day comparison changed from positive 8.3% to negative 21.6%. Longshot performance worsened; this is not evidence that longshots became underpriced.	Fees were omitted. Pre-trends, election effects, composition changes, and the lack of a valid control limit causal interpretation.
Polymarket preprint	Polymarket, not Kalshi; individual contracts versus grouped parent events	Longshots lost 6.3 cents per dollar at the contract level but gained 4.1 cents per dollar when related contracts were grouped by parent event.	Fee treatment is not reported in the cited material. The result is a methodological warning, not evidence that the same aggregation reversal occurs on Kalshi.

A weather ladder contains several related bands tied to one underlying event.

The weather-ladder reversal is plausible but not yet established

An August 2026 OddsShopper article says cheap tail-band contracts in Kalshi weather ladders settle successfully more often than their quoted prices imply. It proposes Kalshi’s one-cent tick as an explanation: moving from two cents to three cents is a 50% markup, whereas moving from 55 cents to 56 cents is approximately a 1.8% markup. The article acknowledges that the sample is small and says roughly 1,000 settled contracts would be needed for confidence, according to its Kalshi weather analysis.

The tick arithmetic is valid, but the causal explanation remains a hypothesis. A fixed one-cent increment is economically larger near the bottom of the price range, yet that fact alone does not show that tick size caused the reported settlement pattern. Contract design, sampling time, forecast horizon, seasonal forecast errors, execution at the bid or ask, and dependence among contracts could also affect the result.

A stronger assessment would need to disclose:

• the number of contracts and parent events;
• observation dates, seasons, stations, and forecast horizons;
• the exact tail-price bands tested;
• quoted prices and actual execution prices;
• effect sizes and confidence intervals;
• maker or taker status;
• fees, spreads, slippage, and available liquidity; and
• the treatment of mutually exclusive or otherwise related bands.

The final issue is especially important. Uncertainty should be clustered at the weather-event or ladder level, with findings reported both by individual contract and by parent event.

If cost-adjusted, event-clustered analysis showed that cheap bands were systematically underpriced relative to matched higher-priced bands, that would support a segment-specific reverse bias. The current claim is suggestive but does not yet establish that relative pattern.

The sports-launch result describes a regime shift, not reverse bias

Will Guo’s self-published observational event study treats Kalshi’s January 23, 2025 sports launch as a possible change point. It reports that non-sports takers’ share of purchases involving 1-to-20-cent longshots rose from 52.1% before launch to 65.7% afterward. Weekly average taker excess returns on those entries fell from negative 2.5% to negative 42.2%; in markets resolving within 60 days, returns changed from positive 8.3% to negative 21.6%. For markets trading on both sides of the launch date, however, the estimated change in longshot buying was negative 1.2 percentage points and statistically insignificant. The author attributes the aggregate shift mainly to newly created markets and changes in market composition. Statistical analysis uses consistently collected data through May 1, 2026, followed by a 23-day collection gap and changed conditions after collection resumed. Fees were omitted, and the roughly $13 million estimate of excess post-launch losses was not statistically distinguishable from zero under a market-clustered bootstrap, according to the event study.

Here, “reversal” describes the sign of returns in the restricted 60-day comparison: positive before the launch and negative afterward. The post-launch result is consistent with stronger conventional longshot underperformance, not reverse favorite-longshot bias.

The launch date should be treated as a regime marker rather than a proven cause. The statistically insignificant within-market estimate also cautions against interpreting the aggregate change as a demonstrated behavioral shift inside continuing markets.

The defensible finding is an association: longshot-oriented taker activity increased as the market mix changed, and reported longshot returns deteriorated. The design does not isolate the arrival of sports bettors as the cause.

How to judge any future claim that the bias has flipped

A credible Kalshi-wide reversal would require more than a profitable-looking price bucket or a before-and-after change. An evaluation should address seven questions.

1. Which price is measured? Researchers should distinguish bids, asks, midpoints, closing prices, and executed purchases. They should also identify whether makers posted the relevant prices or takers accepted them.

2. Are entries connected to settlements? Historical candlesticks can describe bids, asks, traded prices, volume, and open interest. By themselves, they do not establish which side was purchased, whether the displayed price was executable at the required size, or the realized return after settlement.

3. Are economic costs included? Return calculations should incorporate fees, bid-ask spreads, slippage, available liquidity, order size, and holding time. Gross calibration error is not the same as executable net profit.

4. Does the result survive different definitions? Findings should be reported for several longshot thresholds, such as below 5, 10, and 20 cents. A smooth price gradient is stronger evidence than a result appearing at one selected cutoff.

5. Is the result broad or category-specific? Estimates should be stratified by category, resolution horizon, liquidity, calendar period, and market cohort. Weather tails or newly listed markets should not stand in for the platform as a whole.

6. Is dependence handled correctly? Uncertainty should be clustered by parent event, with results shown both for individual contracts and grouped events. Related contracts cannot safely be treated as fully independent observations.

7. Does the result replicate out of sample? The weather claim particularly needs tests across stations, seasons, forecast horizons, and post-May-2026 conditions that were not used to discover or refine the initial result.

The current record supports a qualified conclusion: broad Kalshi research finds conventional longshot underperformance, the weather evidence is a preliminary category-specific possibility, and the sports-launch “reversal” refers to longshot returns turning from positive to negative over time. Yes No Odds presents this analysis as informational, not trading advice.