Nice paper, and thanks for releasing the dataset.

The "top-1% winners are patient limit-order liquidity providers, not insiders" finding is interesting, and I'd love to see it extended cross-venue.

I work on tooling that normalizes orderbooks across Polymarket, Kalshi, Limitless, and Smarkets. From that angle, a lot of what looks locally like skilled Polymarket market-making turns out to be cross-venue arbitrage that happens to land on Polymarket. The same underlying question routinely trades 3-8% apart across venues for hours at meaningful depth, and a fast multi-venue stack rests limits on the lagging book at the exact moments the leading book moves. Locally that's indistinguishable from disciplined liquidity provision; cross-venue it's closer to FX triangular arb on the consensus price.

If your timestamps are fine-grained enough, a clean follow-up: for the top 1% of Polymarket profit-takers, what fraction of fills land within N seconds of a same-question move on Kalshi or Limitless? If it's materially above baseline, some of "skill" resolves into "cross-venue infrastructure" — which is also a more durable edge than within-venue alpha, so it could partly explain the weak monthly persistence you observe (the cross-venue gap closes when too many players run the same stack).

This might also be consistent with your insider-trading conclusion rather than against it: an insider on a real-world event has every reason to hit the lowest-friction venue with aggressive market orders (Polymarket: permissionless wallets, no KYC, no withdrawal limits). That's a fundamentally different profile from the patient limit-posting strategy your top bucket runs, so the two populations cleanly separate in the data even if both are present.

We study trading gains and losses on Polymarket, the largest prediction market. Using 588 million trades ($67 billion in volume), we show that the gains are highly concentrated: the top 1% of users capture 76.5% of profits. Successful traders provide liquidity using limit orders that resolve favorably relative to realized outcomes while unsuccessful traders take liquidity using market orders. Monthly performance is weakly persistent, however, this may represent sample selection rather than skill. A detailed analysis of the trading behavior of the most successful accounts suggests that "insider'' trading is unlikely to explain the performance of the largest winners.

Full dataset available at https://huggingface.co/datasets/vgregoire/polymarket-users

> the top 1% of users capture 76.5% of profits

This seems to be similar to OnlyFans, and the economy at large...

Wait- why isn't there any conflict of interest statement provided in this paper?

What's the baseline here - in a world where every person is betting randomly X times a month, what would the distribution look like? There'd still be a small percentage that wins most of it, right?

Just curious but how are bets arbritated on these website?

Meaning who decides if an outcome was yes or no? Answers to things like "Who will win the next Best Picture Oscar?" are fairly obvious and binary.

Can we make bets whose answers are not binary yes/no?

What about "Will celebraty X and Y break up?"? Does Polymarket go to X and Y to confirm if they broke up or something :D