Sportsbook Spillovers: Retail Trading and the Favorite-Longshot Bias on Kalshi

Abstract

I study the January 23, 2025 launch of sports markets on Kalshi, a federally regulated event exchange, as a shock to the composition of the platform's user base, and measure its spillover into the platform's non-sports markets. Using the complete trade history of approximately 593 million trades from 2021 through August 2026, I document three facts. The share of taker order flow buying longshot contracts (priced 1 to 20 cents) rose from 52.1 to 65.7 percent after the launch, across political, financial, and miscellaneous categories alike. Taker excess returns on longshot positions deteriorated sharply, reversing sign from +8.3 to −21.6 percent in the cleanest 60-day-resolution comparison. Realized trader losses exceeded pre-launch loss rates by roughly $13 million, an estimate reported as accounting because it is not statistically distinguishable from zero under market-clustered inference. The effects operate through newly created markets rather than within existing ones. Eight of twelve headline tests survive a Holm multiple-testing correction; failed specifications are reported.

1. Introduction

The economic case for prediction markets rests on the composition of their participants. Prices aggregate information effectively when the typical trader holds a genuine view about the underlying event (Wolfers and Zitzewitz 2004); they do so less effectively when the typical trader is purchasing a small chance of a large payoff, in the manner of a lottery participant. The betting literature summarizes the lottery-like pattern with the favorite-longshot bias: the tendency of bettors to pay too much for unlikely outcomes and too little for likely ones, documented from early parimutuel studies (Griffith 1949) through modern syntheses (Thaler and Ziemba 1988; Snowberg and Wolfers 2010). The strength of this bias in a market is a compact indicator of who is trading in it.

Kalshi's sports launch provides an unusually clean setting in which to observe a change in participant composition. Before 2025, the platform's users had self-selected into forecasting markets on elections and economic data. The official launch of January 23, 2025 introduced a product with mass-market appeal, and within two months sports contracts accounted for more than half of platform volume. The platform imposes no separation between products: an account opened to bet on sports can trade contracts on inflation or elections at no additional cost, so habits formed in one product can carry into the other.

If sports-betting behavior spilled into the forecasting markets, the effect should be observable in three measurable quantities: the side of the market that aggressive order flow takes, the distribution of that flow across the price range, and its subsequent realized returns. This article measures all three and makes three contributions. First, it documents a large, category-wide shift of taker flow toward longshot contracts after the launch. Second, it shows that the returns to that flow deteriorated to levels typical of conventional betting markets, on a platform where the favorite-longshot bias had previously been absent. Third, it demonstrates that the shift operates through the extensive margin, newly created markets and the flow arriving in them, rather than within pre-existing markets, consistent with a mechanism of new participants and new market types rather than repricing of existing instruments.

The design is an observational, platform-level event study rather than a controlled experiment. Section 2 describes the data, Section 3 the empirical strategy, and Section 4 the results. Section 5 sets out identification concerns, including pre-trends, and the checks that bound them. Section 6 separately describes the platform's post-May-2026 transformation, which the statistical analysis deliberately excludes, and Section 7 concludes.

2. Data

2.1 Collection

The analysis is based on what is, to the author's knowledge, the largest public dataset of prediction-market activity, assembled with an open-source collection framework. Following the refresh conducted for this article, the dataset comprises approximately 593 million Kalshi trades and 30 million market records spanning June 2021 through August 4, 2026. Each trade record identifies the taker side, the prices of both contracts, and the quantity; each market record supplies the resolution outcome and its timing.

A collection constraint of independent interest: Kalshi's public data interface returns trades from approximately the most recent 72 days only, and older history is deleted. This was verified directly; a market with millions of recorded trades returns data for May 26, 2026 and returns nothing for May 5, 2026. The assembled history is therefore complete through May 1, 2026, contains an unrecoverable 23-day gap, and is complete again from May 25, 2026 onward. The platform's history cannot be reconstructed retrospectively and must be recorded continuously, as this project has done since 2021.

2.2 Sample construction

By mid-2026 the platform listed tens of millions of short-lived cryptocurrency markets with durations measured in minutes and cleared on the order of eight million trades per day, roughly one hundred times its 2024 rate. Data from this later era differs from the historical sample in both platform character and collection method, and no statistical comparison in this article crosses the May 2026 boundary. All tests use the consistently collected sample ending May 1, 2026, combined with market resolutions recorded through August 2026; the resolution update is material, as thousands of markets open in the spring have since settled. The post-gap era is described separately in Section 6.

All results are restricted to markets with at least 100 contracts of lifetime volume, the rule applied throughout the historical collection, so that the measured universe remains stable over time. Classification of markets as sports or non-sports uses a curated list of ticker prefixes matched longest-first; keyword-based classification was evaluated and rejected during auditing (among other errors, it assigns UKRAINE-prefixed markets to a weather category because the name contains the string RAIN).

2.3 Definitions

On every trade, the maker is the party whose order was resting on the book, and the taker is the counterparty who crossed the spread to trade against it. Takers skew toward retail participants; makers skew toward professional liquidity providers. A longshot denotes a contract priced at 1 to 20 cents, an implied probability below 20 percent. The excess return on a resolved position is the payout minus the entry price, divided by the entry price, dollar-weighted, following Buergi, Deng and Whelan (2025).

3. Empirical strategy

Event date. The event date is January 23, 2025, the official sports launch. A small number of sports markets existed from October 2024, but their volume was negligible. Following the official launch, growth was rapid: sports exceeded 25 percent of platform contract volume in the week of March 17, 2025, exceeded 50 percent the following week, and reached a maximum weekly share of 92 percent in late 2025.

Treatment of the 2024 election. The pre-launch period includes the 2024 United States election, the highest-volume and most retail-intensive episode in the platform's history before the launch. For the returns results this inclusion is conservative: election-period losses raise the pre-launch average loss rate, which reduces the measured post-launch deterioration. As a robustness check, all results were re-estimated with the pre-launch period restricted to the twelve months preceding the event date; the composition shift is larger under that restriction (+16.8 percentage points rather than +13.6) and the returns deterioration remains substantial (−28.8 percentage points, p = 0.003).

Estimation and inference. Weekly outcome series are tested with regressions of the outcome on a post-launch indicator using Newey-West standard errors (lag 4), with moving-block bootstrap p-values as a small-sample complement. The within-market specification uses market fixed effects and a market-age control, with standard errors clustered by market and absorbed effects counted in the degrees-of-freedom correction. Aggregate dollar figures are evaluated with a market-level bootstrap (1,000 draws), because all dollars staked in a single market settle together and cannot be treated as independent observations. Twelve headline tests constitute the inferential family, and every reported significance level carries a Holm-Bonferroni correction for the number of tests; the complete output of sixty tests, including failures, is available in the replication repository.

4. Results

4.1 Composition of taker order flow

Consider every non-sports trade in which the YES contract was priced between 1 and 20 cents, and ask whether the taker bought the longshot YES side or sold against it. Before the sports launch, takers bought the longshot side 52.1 percent of the time. After the launch, the figure is 65.7 percent, an increase of 13.6 percentage points (Holm-adjusted p ≈ 10⁻⁶).

Three further results establish the breadth of the change. First, the shift appears in every market category: +8.2 percentage points in political markets, +9.9 in financial and economic markets, and +5.9 in the residual category, each significant after adjustment. Second, the shift is not confined to the cheapest contracts; across the full 1-to-99-cent price range, taker buying tilted toward cheaper contracts after the launch, and the change in this price gradient is itself highly significant. Third, the shift operates through new markets rather than existing ones: when each market is compared only with itself, restricting attention to markets that traded on both sides of the event date and controlling for market age, the estimated within-market change is −1.2 percentage points and statistically indistinguishable from zero (p = 0.53). The platform-wide shift derives from newly created markets and the flow arriving in them.

A duration-restricted specification, used again below, gives the same answer on a stable population: restricting both periods to markets resolved within 60 days of creation, the composition shift is +8.0 percentage points (Holm-adjusted p ≈ 10⁻⁴).

4.2 Taker returns on longshot positions

Before the launch, the favorite-longshot bias was nearly absent from the platform. In the 60-day-resolution subsample, longshot taker positions earned +8.3 cents per dollar, whereas comparable positions lose money in nearly every betting market in the literature. After the launch, the pattern reversed on every measure examined.

| Specification | Before | After | Statistical assessment | | --- | --- | --- | --- | | Weekly average taker excess return, entries at 1-20 cents | −2.5% | −42.2% | significant after adjustment (p ≈ 6×10⁻⁵) | | Pooled dollar-weighted excess return | −37.8% | −47.6% | reported as description | | Markets resolving within 60 days only | +8.3% | −21.6% | significant after adjustment (p ≈ 10⁻⁶) | | Post-launch period beginning September 2025 | | | significant (p = 0.003; bootstrap p = 0.028) |

The rows serve distinct purposes. The first is the formal time-series test, treating each week as one observation. The second weights every dollar equally and is the appropriate measure of aggregate trader experience; it differs from the first because a small number of high-volume election weeks dominate dollar totals. The third is the cleanest like-for-like comparison, and it shows the sign of the returns reversing. The fourth shows that the result does not depend on the immediate post-election months: it holds when the post-launch period begins in September 2025, by which point sports constituted roughly 74 percent of platform volume.

One alternative explanation is that longshot contracts became worse purchases after the launch for reasons unrelated to who was buying them. That explanation implies a mirror-image improvement on the maker side of the same trades. The data show instead that the maker-taker return gap widened sharply after the launch (joint test p ≈ 3×10⁻⁴), indicating a change in the composition of demand rather than a change in the underlying events.

4.3 Aggregate losses

Applying pre-launch loss rates, computed within each price range, to post-launch trading volumes yields expected taker losses of approximately $12.7 million in non-sports markets after January 2025. Realized losses were approximately $25.7 million. The difference, roughly $13.0 million, measures the additional cost associated with the post-launch shift in behavior.

The composition of the excess is informative. Approximately $5.4 million arises in the 1-20 cent range. The largest single component, $16.2 million, arises in the 61-80 cent range, where takers earned positive returns before the launch and approximately zero after; the pre-launch user base's advantage on likely outcomes disappeared together with its discipline on unlikely ones.

These dollar figures deserve limited statistical confidence. An earlier version of this analysis attached strong significance to the excess using a procedure that implicitly treated each dollar as an independent observation. That procedure is invalid: dollars within a market settle jointly, so the correct unit of evidence is the market. Evaluated with a market-clustered bootstrap, the excess is not statistically distinguishable from zero (p ≈ 0.27), because aggregate outcomes are dominated by a small number of very large markets. The findings of Sections 4.1 and 4.2 constitute the statistically supported results; the dollar figure represents the magnitude they plausibly imply and is presented as accounting rather than as evidence.

5. Threats to identification

Pre-trends. The longshot share of taker flow was already rising during 2024, before the event date, and the twelve-month pre-launch trend is statistically significant for both primary outcomes. Much of this drift coincides with the election cycle. Three checks bound the concern. A placebo exercise imposing an artificial event date inside the stable 2023 period detects no effect (p = 0.66). The results are unchanged when the post-launch period begins in September 2025, well after the election cycle. The composition shift also survives exclusion of the largest post-launch markets, so no single prominent market accounts for it. Attributing part of the measured magnitude to lingering election-era participants, rather than to arriving sports bettors, remains a defensible partial interpretation; the twelve-month-pre and September-2025 estimates bound its possible size.

Absence of a cross-platform control. The natural alternative design would compare Kalshi's markets against Polymarket's as a control group. That design was constructed and then set aside on the basis of its own diagnostics: the two platforms' volumes were already diverging significantly before the event date, so the parallel-trends assumption underlying the comparison fails. No estimate is reported from a design whose central assumption fails.

Unresolved markets at the sample boundary. Return calculations require known outcomes, and markets unresolved at the end of the sample are excluded, which could in principle distort estimates near the boundary. The 60-day-resolution specification addresses this concern directly, and the principal returns result survives it.

Fees. Trading fees are not modeled. Their inclusion would reduce every reported return, so the documented deterioration is understated in this respect.

6. The platform after May 2026

The post-gap data, covering May 25 through August 4, 2026, describes a platform transformed a second time. Kalshi now clears roughly eight million trades per day; competitive video gaming accounts for approximately 23 percent of contract volume, and short-duration cryptocurrency markets dominate the non-sports segment. In this later era, takers buy the longshot side 72.9 percent of the time, a higher figure than any observed in the estimation sample, and such positions lose approximately 17 cents per dollar. Because both the platform and the collection method differ across the boundary, these figures are presented as description rather than as tests. They give no indication that the patterns documented above are reversing.

7. Conclusion

For its first four years, Kalshi's forecasting markets were traded by participants who priced unlikely events with unusual care; the most robust bias in the betting literature was largely absent from the platform. In the months following the January 2025 sports launch, taker order flow in those same market categories came to resemble sportsbook flow, tilted heavily toward longshots and paying substantially for the tilt. The change arrived through new markets and new participants rather than through deterioration within existing markets, consistent with the most direct explanation: the platform's new customers brought their trading habits with them.

These findings do not by themselves impair prediction-market prices as forecasts, since professional liquidity provision continues to discipline quotes. They do document a measurable worsening in the outcomes of ordinary participants at precisely the moment the industry found its mass-market product. Both facts belong in any assessment of the social value of regulated event markets.

Data and code availability

The collection framework, analysis code, and the complete set of sixty statistical results, including specifications that failed, are available in the public repository. Sample: trades through May 1, 2026 under a stable 100-contract minimum-volume rule; resolutions as of August 4, 2026.

References

• Buergi, C., Deng, S., and Whelan, K. (2025). Favorite-longshot bias in prediction markets.
• Griffith, R. M. (1949). Odds adjustments by American horse-race bettors. American Journal of Psychology, 62(2), 290-294.
• Snowberg, E., and Wolfers, J. (2010). Explaining the favorite-long shot bias: Is it risk-love or misperceptions? Journal of Political Economy, 118(4), 723-746.
• Thaler, R. H., and Ziemba, W. T. (1988). Anomalies: Parimutuel betting markets: Racetracks and lotteries. Journal of Economic Perspectives, 2(2), 161-174.
• Wolfers, J., and Zitzewitz, E. (2004). Prediction markets. Journal of Economic Perspectives, 18(2), 107-126.