Prediction Market Traders Trail Behind Bots in Lopsided Performance

Prediction Markets: A Lucrative Side Hustle or a Losing Proposition?

Prediction markets have been touted as a lucrative side hustle for young Americans struggling with rent and student loan bills. However, a recent analysis of Polymarket, one of the largest prediction markets, suggests that most traders are actually losing money, with a significant amount of losses among many users.

According to a Bloomberg News analysis of every wallet active since the beginning of 2025, over 100,000 accounts lost at least $1,000 on Polymarket. This is almost twice the number that made at least that much. Among the winners, a majority of the profits were raked in by a tiny slice of automated bots, according to trade records compiled by the data firm Dune. Everyone else, in aggregate, lost $131 million.

Polymarket's blockchain ledger provides a unique window into the financial fortunes of the millions of people who have begun trading on prediction markets over the last year. Almost half of the two million wallets active since early 2025 made or lost less than $10, indicative of people experimenting with this new form of betting. Even among this group, most ended up in the red.

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A recent paper co-authored by Charles Martineau, a professor at the University of Toronto's business school, found that since 2022 around 69% of traders on Polymarket lost money, while the top 1% captured three-quarters of the profits. Martineau said that his students, who have been obsessed with prediction markets over the last year, were taken aback by his findings.

Note: The data for Kalshi is not provided in the original article, but it is mentioned that a report from analysts at Citizens earlier this year suggested that users are losing proportionately more on Kalshi than they do on sports betting apps.

While prediction markets have been described as peer-to-peer, the Polymarket records suggest the role of the sportsbook is now largely being played by automated, high-frequency traders. The most active accounts on the site were a small proportion of wallets, but accounted for most of the trading volume.

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Joshua Della Vedova, a professor at University of San Diego's business school, constructed a method for categorizing traders that labeled any wallet as a bot if it traded an average of at least 50 times on any day it was active or 1,000 times over the course of the data. Using Della Vedova's definition, Bloomberg's analysis found that since the beginning of 2025 the typical bot averaged 89 trades on each active trading day — compared to 2.2 for non-bots — with trades spread across a greater diversity of markets.

These high-volume accounts collectively turned a profit of $131 million, mostly concentrated among 823 users that netted more than $100,000 each. The less active traders, meanwhile, lost the equivalent amount when all their wins and losses were added up.

Della Vedova found that the bots did not outperform because they were better at predicting outcomes, but rather because they got into markets earlier and at better prices. The accounts that Della Vedova identified as retail traders actually picked the right outcome more frequently, but they ended up losing much more money — tens of millions of dollars in aggregate — because they traded late, at bad prices.

Despite the unparalleled transparency it provides, the Polymarket data still only offers a partial picture of how customers are faring. The blockchain does not provide information on who owns each wallet, so one trader might control multiple wallets pursuing different strategies, potentially taking both sides of the same contract at various points.

Polymarket has also faced allegations that it hosts a high volume of so-called wash trading, coming from customers who are seeking cryptocurrency rewards, rather than a return on their trading. However, this sort of activity would be unlikely to influence statistics on profits and losses because it would come from people who are trying to rack up lots of trades without taking long-term positions.

Pat Akey, one of the co-authors of the study, said that profitable traders generally have a clearly defined trading strategy and money to leave sitting on the exchange — not the sort of thing you'd expect from an inexperienced bettor looking to cover their monthly bills.