John Pederson, 33, couldn’t work. The former Outback Steakhouse line cook was recovering from a car crash and running out of money. Kalshi, the prediction market, promised a quick way to fix that. He took out a variable-interest loan and started betting.
At first, it worked. Pederson turned about $2,000 into close to $8,000 by betting on daily snowfall totals in Detroit, where he lives. He parlayed that into $41,000 by trading on sports, using a strategy he developed with the help of AI, according to a Wall Street Journal review of his account records.
Then he placed his most audacious bet yet: All $41,000 that a celebrity would say a particular word on TV. He lost it all.
Pederson isn’t alone in walking away empty-handed from the bet-on-anything markets, which cover sports, celebrities, news and more.
Kalshi and its competitor Polymarket advertise themselves as life-changing tools for regular people—implying everyone has a fair chance to score. “I was about to be unable to pay my rent, but I got two years of rent through Kalshi’s predictions,” gushed one woman in a Kalshi ad on TikTok.
But for most users the reality is nothing like that.
Instead, casual traders are bleeding cash while a small number of sophisticated pros—including trading firms with access to vast streams of data—eat their lunch, according to a Journal analysis of platform data and interviews with traders.
On Polymarket, the Journal found, 67% of profits go to just 0.1% of accounts. That means less than 2,000 accounts netted a total of nearly half a billion dollars. The Journal analyzed 1.6 million Polymarket accounts that have traded since November 2022. There are at least 2.3 million total accounts on the site.
On Kalshi, too, losers vastly outnumber winners. Spokeswoman Elisabeth Diana said there are 2.9 unprofitable users for each profitable one based on data from the past month. She said the number is subject to change as the exchange grows. The company doesn’t make public comprehensive data on users’ profits and doesn’t share its total number of users.
Total trading volume on both platforms has rocketed to $24.2 billion in April, up from $1.8 billion a year earlier, according to analytics firm The Block.
Proponents say the markets don’t count as gambling, and that they harness the wisdom of crowds to accurately predict future events. Federal Reserve research shows Kalshi is an effective tool to forecast economic trends.
Traders have been shelling out for access to big-data streams from third-party providers to have a leg up. Computers use the data and algorithms to predict price movements and manage risk, faster than any human. The pros also make use of their scale to make frequent, strategic trades—sometimes tens of thousands a day—and book profits on incremental moves, with a measure of attention and discipline rarely seen in recreational users.
Casual traders “have no chance. Systematically,” said Michael Boss, a former professional poker player and a statistician by training. On Kalshi, Boss places 60 trades a minute and modifies his bids and asks 30 times a second.
Diana said that many financial markets exhibit similar patterns of wealth concentration, and that more users make money on Kalshi than by day trading or on traditional sportsbooks. She said that Kalshi no longer runs the “pay my rent” ad.
A spokeswoman for Polymarket declined to comment on the Journal’s analysis.
Polymarket has a data partnership with Dow Jones, the publisher of the Journal. The Journal used only publicly available data for this analysis.
In the case of Pederson, the out-of-work cook who lost his entire stake, he had fallen for a category rife with sucker bets: mention markets.
Professional traders say they don’t touch these bets because they are unpredictable and even millions of dollars in data can’t give them a reliable edge.
The mention-market bets, where users wager on what a person will say during a public appearance, routinely pay out less often than expected, according to the Journal’s analysis. Casual bettors are taking bigger risks than they realize, in part because of a phenomenon called “long shot bias,” in which bettors, driven by excitement, overvalue unlikely events.
Monthly trading volume on Kalshi’s mention markets—which dwarfs Polymarket’s—has exploded since mid-2025, according to The Block. The bets are popular with the young users the sites are courting—including influencers who promote them on social media livestreams and other videos touting their wins.
For all types of bets, Polymarket and Kalshi make a simple pitch—users can monetize what they already know to make a quick buck—that has taken the world by storm.
Yet more than 70% of Polymarket users lose money, the Journal’s analysis found. A working paper last month from researchers in France and Canada had similar findings. They found that the gains on prediction markets go almost entirely to sophisticated traders, while long-shot bets and unsophisticated traders take losses.
The Journal’s analysis of Polymarket trading data indicates a typical user is down between $1 and $100, while the least successful 10% of Polymarket traders have lost an average of $4,000 each.
Some make emotional decisions—following their gut or based on information they’ve gleaned from public sources.
A self-described problem gambler in Connecticut lost $2,000 in one day betting on the Super Bowl on Kalshi—all during the tense fourth quarter. A 31-year-old in Indiana, who called the trading “like a drug,” lost about $5,000 betting nearly daily on sports on Kalshi in the first months of the year.
In contrast, the prediction markets have increasingly drawn firms staffed with dozens of employees that pay millions of dollars for specialty sports and finance data and run trading algorithms. They aim to win against the students, recreational gamblers and other low-volume traders who make up most of the users on the sites.
In traditional gambling, bookmakers set the odds, take in the bets and pay off the winners. In prediction markets, there is no “house,” and users instead trade with other users. The platforms themselves take in fees on the trades, which can vary based on the price of the contracts a user buys, the type of market and other factors.
In a SoHo office, a college dropout stares at a computer screen showing the flow of millions of dollars in bets from casual traders on the price of bitcoin.
Samuel Wood-Soloff left Princeton this year and collected a half-million dollar check from Alliance Capital, an accelerator for crypto startups backed by prominent Silicon Valley investors including crypto-entrepreneur Balaji Srinivasan. Wood-Soloff, who took math classes at UC Berkeley in high school and traded crypto during a gap year before Princeton, moved with four friends to New York to trade prediction markets full time, betting on sports, politics and the future prices of cryptocurrencies.
“Our only competition is market makers,” he said in an interview, referring to other firms like his that continuously quote buy and sell positions. He declined to disclose his company’s profits or losses, but said it has between $500,000 and $1 million deployed across Polymarket, Kalshi and other smaller prediction markets.
Boss, the former professional poker player, has made more than $668,000 on Kalshi, mostly on sports bets, according to his public profile on the platform, since he started taking trading seriously about three months ago. In addition to the speed of his trades, he is rigorous about how he prices his bids and asks.
“You’re going to find that the easiest money is going to be in sports,” he said. “Sports has the attention of all the sick young men, I guess.” By “sick,” he clarified, he meant gambling addicts.
On Kalshi, he observed, a large number of casual traders just bet “yes” on what they hope will happen. “That’s not what you see in crypto or a stock exchange where people are trading securities,” he said.
Jonathan Stall-Ryan is co-founder of another firm with around a dozen staff—like him, all college students. The firm is among the top-five traders by volume betting on crypto prices on Kalshi, and Stall-Ryan said it has turned $1,000 into seven-figure profits.
The company pays more than $200,000 a year for live data feeds, AI coding agents and servers, and uses algorithms to execute live trades—tens of thousands a day.
Stall-Ryan once was with a member of his fraternity at the University of Virginia and saw the man casually bet on the price of bitcoin on Kalshi. “That guy’s gonna lose,” he said he thought.
Most of these professional traders serve as market makers. Kalshi and Polymarket said they rebate some market makers’ fees and sometimes even pay them for providing liquidity.
Quantitative trading firm Susquehanna International Group signed on as Kalshi’s first major institutional market maker in 2024. Susquehanna is believed to trade hundreds of millions of dollars through Kalshi each week, according to professional traders who monitor Kalshi’s order book. It isn’t clear how much money the firm is making because its profile is private. Susquehanna declined to comment.
Jump Trading, a quantitative trading firm, is also active on both Polymarket and Kalshi. In mid-April, Citadel Securities President Jim Esposito said at a Semafor event that the firm was “absolutely keeping an eye” on developments in prediction markets. Some traders who buy high-risk options contracts are now flocking to prediction markets.
“All of sports betting, all of playing poker, all of options trading, is making sure you’re betting against someone you’re smarter than,” Susquehanna co-founder Jeff Yass said on a sports betting podcast in 2020. In the same podcast, he described his role supporting what would become prediction markets as “an MFG—a mission from God.”
For one thing, Americans deserve to be able to bet on sports even though some states bar the practice, he said. But also: “I expect to make a lot of money.”
Yass declined to comment through Susquehanna.
The platforms design contracts for users to bet on yes-or-no questions about future events. Contracts are usually set up to pay $1 if a bet proves correct and zero if not. The contracts’ prices reflect traders’ assessed probability of those events. If, for example, a contract tied to an event is trading for 41 cents, the prediction market would say there’s a 41% chance of that happening. If you win, the contract you bought at 41 cents pays out at $1; if wrong, you lose your money.
The price of a contract is constantly changing before it’s settled, depending on the market forces of buyers and sellers. Traders profit from marginal changes in price, much like traders on Wall Street.
Many naive prediction-market players are following in the footsteps of solo speculators searching for easy money in financial markets, where decades of studies have documented that day traders rarely make money. In recent years, many casual traders, spurred on by social media, have lost their shirts betting on highly volatile memestocks.
Kalshi and Polymarket’s U.S. arm, which was rolled out recently for a limited number of early users, are regulated by the Commodity Futures Trading Commission, and have said that the trading on their platforms is akin to other regulated financial markets. Nearly all of Polymarket’s activity is on its offshore platform, which is technically off-limits to Americans but can be easily accessed using a VPN.
Critics say the markets are susceptible to insider trading, among other problems. Recent instances include alleged insider trades on U.S. military action in Venezuela, announcements from Google and congressional races.
CFTC Chairman Michael Selig has defended prediction markets, and the agency has articulated its federal authority over the sites. It has cracked down on alleged insider trading and has signaled the onset of stricter government enforcement.
Polymarket said it has cooperated with the Justice Department on its crackdown on insider trading. Kalshi bans insider trading on its platform and has announced penalties for several traders who violated these rules in recent months.
Former Kalshi employee Adhi Rajaprabhakaran, who last year on Substack described casual traders as “fish,” bettor-speak for inexperienced players likely to lose money, said in an interview that, while he still thinks that is true in general, he also believes the presence of uninformed traders on prediction markets is a powerful incentive for more sophisticated traders to enter the markets. He said that would result in more accurate forecasts.
“Everyone, when they make a trade, thinks they’re the more informed trader,” he said. “In the long run, the people who are right more win more money. No one is being forced to do this.”
Until he got into mention markets, Pederson’s time on Kalshi was going well. “I follow finance broadly,” he said. “I’m always looking for ways to sharpen my acumen, if you will.”
Mention-market bets hinge on a single question: Will a public figure say a certain word? Traders on Kalshi bet more than $28 million on whether President Trump would say phrases such as “cartel,” “Somali” or “hockey” during his State of the Union speech this year. In total, Kalshi users bet almost $181 million on mention markets in the month of February, according to The Block.
The Journal’s analysis, which looked at Kalshi data, indicates that mention markets pay out far less often than bettors expect based on their listed odds.
The Journal analyzed over 35,000 completed mention markets on Kalshi and found that on average, “yes” trades on mention markets priced at a 50% winning probability pay out around 40% of the time. Since the contracts’ prices are meant to match their odds, those bettors are overpaying.
Trades on these markets, often exhibiting the long shot bias, routinely lose money, the Journal’s analysis found. On average, mention-market traders putting money on “yes” on the first price they see—a common pattern for retail traders—will lose 11% of the money they bet. Those returns are worse than most Vegas slot machines, according to research from the University of Nevada, Las Vegas.
Diana, the Kalshi spokeswoman, acknowledged that mention markets face long shot bias but said mention markets weren’t representative of the platform’s overall pricing and weren’t a good candidate for this type of pricing analysis. She added that Kalshi’s analysis showed mention markets were priced more accurately in the four hours prior to an event.
Kalshi has encouraged mention-market traders to livestream themselves trading during events, a tactic two livestreamers said was designed to boost engagement in the markets. “Mention-market streams on social media often go viral and improve Kalshi’s brand awareness,” Bank of America analysts wrote in an April report on prediction markets.
In January, Pederson placed all $41,000 he’d earned on the hip-hop artist A$AP Rocky to say “rapper” during an appearance on the Tonight Show Starring Jimmy Fallon—the celebrity had recently been in a movie where he played a rapper. He stood to win more than $168,000.
A full-length video of the interview posted on YouTube showed A$AP Rocky did say “rapper.” But that segment was edited out of the version broadcast on NBC. By Kalshi’s market rules, only what was said in the broadcast counted.
That portion of the rules wasn’t immediately visible to him on the platform’s website, according to a video Pederson posted, and he didn’t see it. (Kalshi has since updated its interface to make market rules more apparent.)
Pederson lost it all, and he had few other resources to turn to. He’s currently living in a homeless shelter in downtown Detroit, though he said he recently got an offer for a job selling mortgages.
Once he gets back on his feet, he said, he aims to work in finance to support his music career. Would he go back to trading on prediction markets? “Maybe,” he said. “I’d prefer to spend my time in more regulated markets.”
Write to Neil Mehta at neil.mehta@wsj.com, Katherine Long at katherine.long@wsj.com and Caitlin Ostroff at caitlin.ostroff@wsj.com
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Small, unsophisticated bets often result in significant losses. \n\n📊 **Pros Dominate**: A tiny fraction of accounts—less than **0.1%**—capture the majority of profits using **algorithms, big-data streams, and high-frequency trades**, leaving casual users at a disadvantage. \n\n🎯 **Risky Bets & Hype**: Popular “mention markets” and celebrity bets attract young users and influencers, but are highly unpredictable. 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