WSJ Investigation: Almost Everyone Loses on Prediction Markets — Except a Small Group of Pros

A new Wall Street Journal investigation has confirmed what many skeptical observers long suspected: prediction markets like Polymarket and Kalshi disproportionately reward a tiny elite of professional traders while the vast majority of casual users consistently lose money.

What the Data Shows

The Wall Street Journal analyzed 1.6 million Polymarket accounts that have traded since November 2022, drawing on the platform’s own data. The findings were stark: 67% of all profits on Polymarket were captured by just 0.1% of accounts — fewer than 2,000 traders — who collectively netted nearly half a billion dollars. More than 70% of Polymarket users lose money overall. The typical user loses somewhere between $1 and $100, but the bottom 10% of traders lose an average of $4,000 each.

The picture at Kalshi is similarly lopsided. According to the company’s own estimates, for every profitable user in April there were 2.9 unprofitable ones — a ratio Kalshi spokeswoman Elizabeth Diana shared with the Journal. Polymarket declined to comment on the findings.

Who Is Actually Winning

Most of the profits in prediction markets flow to professional trading firms — organizations with dozens of employees, proprietary data pipelines, and algorithmic trading systems. These firms compete against students, recreational gamblers, and retail traders who rely on publicly available information or intuition. The imbalance is not accidental. Former Kalshi employee Adhi Rajaprabhakaran noted in a Substack post that the presence of inexperienced participants in prediction markets actively attracts more sophisticated players, because unsophisticated money is precisely what makes it worth their time. In trader parlance, casual users are referred to as “fish.”

Rajaprabhakaran told the Journal that the presence of inexperienced traders without access to big data serves as a powerful incentive for more sophisticated participants to enter, which in turn produces more accurate market forecasts. In other words, retail users losing money is a structural feature, not a flaw, of how these markets function.

The Odds Are Stacked on Mentions Markets

One area where the Journal found particularly skewed outcomes is Kalshi’s so-called “mentions” markets, where traders bet on whether a public figure will utter a specific word during a speech or broadcast appearance. After examining more than 35,000 completed mention contracts, the Journal found that “yes” bets carrying a stated probability of winning of 50% were profitable only about 40% of the time. Because contract prices must reflect their stated probabilities, buyers are effectively overpaying — and professional traders know to avoid these markets entirely.

What This Means for Sports Bettors

Prediction markets have grown rapidly since sports event contracts became widely available, and platforms like Kalshi and Polymarket have marketed themselves as tools that give everyday people a fair shot at profit. Advertising campaigns have featured users claiming to have resolved serious financial hardships through their winnings. The Journal’s findings complicate that narrative significantly.

For sports bettors weighing whether to move action to prediction markets, the data is a clear warning: the same dynamics that govern poker — where sharks systematically extract money from recreational players — are fully present on these platforms. Sportsbook reviews consistently show that regulated books, with established consumer protections and transparent pricing, at least let bettors know exactly what edge they are giving up on every wager. On prediction markets, the informational disadvantage facing retail users is far harder to measure — and, based on the Journal’s analysis, far more costly.