Whale performance is not uniform across categories. Politics, sports, macro, crypto, and culture each produce different population-level win rates, different average payouts, and different volatility profiles in the resolved sample. The variation is large enough that a copy-trader who ignores category is implicitly betting on the average whale across all categories, which is rarely the optimal exposure. This article walks through the category-by-category breakdown of whale performance, the structural reasons each category produces the results it does, and the implications for selecting a copy-trading universe.
The leaderboard, for context
Before the category-level breakdown, the population-level view. The list below shows the largest paying whale trades on file across Polymarket and Kalshi.
- 1Will United States win on 2026-06-12?Polymarket·culture·stake $920K·@ 46¢+$1.1M
- 2Will IR Iran win on 2026-06-15?Polymarket·politics·stake $790.8K·@ 49¢+$823.1K
- 3Will Spain win on 2026-06-15?Polymarket·culture·stake $78.6K·@ 9¢+$794.4K
- 4Will Spain win on 2026-06-15?Polymarket·culture·stake $47.4K·@ 9¢+$479.4K
- 5UFC 328: Sean Strickland vs. Khamzat Chimaev (Middleweight, Main Card)Polymarket·sports·stake $103.6K·@ 19¢+$441.8K
- 6Will Canada win on 2026-06-18?Polymarket·culture·stake $1.3M·@ 76¢+$420.1K
- 7What will the announcers say during Jordan vs Argentina?Kalshi·culture·stake $4.1K·@ 1¢+$407.0K
- 8Spread: Spain (-2.5)Polymarket·culture·stake $345K·@ 46¢+$405K
- 9Spurs vs. ThunderPolymarket·sports·stake $198K·@ 33¢+$402K
- 10Roland Garros ATP: Jakub Mensik vs Andrey RublevPolymarket·sports·stake $370.0K·@ 48¢+$400.8K
The categorical concentration is visible in the list itself. Political and crypto contracts dominate the biggest payouts, sports contracts are largely absent, and macro contracts appear with smaller per-trade figures but with notable frequency. The structural reasons for the concentration are discussed below, with reference to the specific dynamics of each category.
Politics
Political markets produce the largest single-trade payouts on the platforms, but the underlying win rate is not particularly high. The combination of a low population- level win rate and an extreme payout distribution is characteristic of high-conviction, long-duration betting. Sharp traders in political markets are not winning more often than they lose; they are winning at prices that produce multiple-bagger returns when they are correct.
The structural reason is contract duration. Political markets often resolve months or even a year after entry, and the underlying probabilities can sit at extreme prices for the majority of that holding period. A trader who anchors on a thesis early and holds through cycles of news and counter-news is positioning for a payout that compounds asymmetrically. The temperament required to hold a long-dated political position through unfavorable news cycles is uncommon, which is part of why the payouts are large.
For copy-traders, political contracts are the highest- reward and highest-patience category. The trades that appear on the wins leaderboard are typically positions held for months. Copy-trading politics without the willingness to hold through adverse price action will produce a portfolio that exits before the eventual positive resolution.
Sports
Sports markets produce the highest population-level win rates of any category, paired with the smallest average payouts. The combination is characteristic of efficient pricing: the prices on sports contracts are quoted tightly because professional bettors are participating actively, and the edge available on any single contract is small. Sharp traders win frequently but in small increments.
The structural reason is the maturity of the underlying pricing models. Sports prediction-market contracts on major leagues compete with offshore sportsbooks for liquidity, and the models that drive both venues have been refined for decades. By the time a contract is listed, the price reflects most of the available public information. The remaining edge sits in narrow situations: late lineup changes, weather data, injury reports surfacing from beat reporters before the line adjusts.
For copy-traders, sports is the steady-paying category. The trades will not produce leaderboard entries, but the win rates support compounding at moderate risk. The copy-trader's edge is in selecting the wallets that have demonstrated category-specific skill, since the broader population of large sports bettors is meaningfully more efficient than the equivalent population in politics or crypto.
Crypto
Crypto contracts produce a bimodal distribution: a small share of trades produce extreme positive payouts, and a meaningful share produce negative returns, with less mass in the middle of the distribution than in other categories. The pattern reflects the characteristics of the underlying asset class. Crypto markets respond to catalysts in discrete events, often with large gaps in either direction.
Edge in crypto contracts tends to come from understanding the specific mechanics of an underlying ecosystem rather than from generic price action. The traders who win consistently are those with domain-specific knowledge: the on-chain dynamics of a protocol, the regulatory trajectory of a specific jurisdiction, the social momentum behind a particular token. Generic chart-watching does not produce sustained outperformance.
For copy-traders, crypto is the category where category-fit screening matters most. A whale who is sharp in political contracts is not necessarily sharp in crypto contracts, and following political wallets into crypto trades dilutes the signal substantially. The defensible policy is to copy crypto trades only from wallets with documented category-specific resolved PnL.
Macro and finance
Macro contracts, primarily on Kalshi, attract professional traders from traditional finance who use the platform to express views on Federal Reserve decisions, employment data, inflation prints, and related releases. The population of bettors is more sophisticated than in any other category, and the pricing is correspondingly tight.
Macro contracts produce moderate win rates and moderate payouts. The category is structurally similar to sports in that the pricing is efficient and the available edge is small, but the trader population is older and the holding periods are typically longer than in sports. The trades that pay are usually those positioned ahead of data releases, where a sharp view on a release relative to consensus produces a predictable price reaction.
For copy-traders, macro is the category that most rewards selection of professional wallets. The retail edge in macro is essentially zero; the available edge is in identifying which wallets are operated by traders with day-job familiarity with the underlying data, and which are operated by retail participants with strong but uninformed views.
Culture
Culture contracts cover awards shows, entertainment outcomes, and other event categories that fall outside the four main categories above. Volume is much smaller than in politics or sports, and the population of traders is correspondingly thinner.
The win-rate data on culture contracts is noisier because of the smaller sample, but the available evidence suggests that the few traders who win consistently are those with industry-specific knowledge, particularly in entertainment and awards contracts. The category does not reward general analytical sophistication in the way that macro or politics does, but it does reward narrow expertise when it is present.
For copy-traders, culture contracts are a niche category. The defensible policy is to follow only wallets with demonstrated category-specific edge and to size positions smaller than in the more developed categories. The expected return is positive but the variance is high relative to the sample size.
How to select a category for copy-trading
The single most useful filter is the copy-trader's own domain knowledge. A trader who cannot independently evaluate whether a whale's political call is plausible will copy political trades without the context that determines whether each trade is well-considered or not. The same trader may have substantial knowledge of sports, where each copy-trade can be evaluated against the trader's own model. The category where the copy-trader can apply independent judgment is usually the category where copy-trading produces the best results, regardless of which category has the highest population-level win rate.
A defensible portfolio across categories includes a core position in the trader's most knowledgeable category, supplemented by smaller positions in adjacent categories where wallet-level evidence is strong. The trader's worst category, in the domain-knowledge sense, should be excluded entirely. The temptation to copy-trade across all categories because the data is available is the most common first-year mistake.
For the category-level breakdown of where the biggest payouts have come from, see where whales win the most. For the platform-by-platform breakdown of how category mix differs between Polymarket and Kalshi, see Polymarket vs Kalshi whales.
Frequently asked questions
Which category has the highest whale win rate?
Sports, by a meaningful margin. The win rate is accompanied by smaller average payouts, so the aggregate PnL contribution of sports is not proportional to the win rate. The categories with lower win rates but larger payouts, particularly politics and crypto, contribute more in absolute PnL terms despite worse population-level win rates.
Which category produces the largest single trades?
Politics, by a wide margin. The combination of long contract durations and extreme prices on long-tail outcomes produces the largest paying trades in the Rivo dataset. Crypto contracts are second; sports and macro contracts rarely produce single-trade payouts of the magnitude visible on the politics leaderboard.
Can a whale be sharp in more than one category?
Some are, but cross-category skill is the exception rather than the rule. The Rivo dataset shows substantial variation in category-specific PnL across wallets that have positive aggregate PnL. Treating whales as category-specialists, and copy-trading them only within their demonstrated areas of skill, produces better outcomes than treating them as general-purpose talents.
Are there categories I should avoid entirely?
Categories outside the copy-trader's own domain knowledge are appropriate to skip. There is no general list of categories to avoid; the defensible filter is whether the copy-trader can evaluate the underlying trade independently. Categories where the copy-trader cannot apply judgment are categories where copy-trading becomes blind mirroring, which produces worse outcomes than informed copy-trading.
Does the category breakdown change across platforms?
Yes, materially. Polymarket is dominated by political and crypto volume; Kalshi is dominated by sports and macro volume. The category mix on each platform reflects the underlying trader population, which is different on each venue. Selecting a platform on the basis of category fit, rather than on overall volume, is the correct first step.