Prediction markets have ceased to be a niche for betting on elections, sports, and high-profile events. Major trading firms have started assembling dedicated teams to work with Polymarket, Kalshi, and other platforms where prices reflect the probability of specific outcomes.
For such players, the answer to who will win a match or election is not the main interest. They are focused on something else: price discrepancies between platforms, delays in reacting to news, and short-term liquidity imbalances. In other words, prediction markets are increasingly becoming not entertainment for bettors, but a new zone for arbitrage and quantitative strategies.
Polymarket and Kalshi Become a Market for Quants
The interest of large trading companies is explained by the growth in volumes. Just a few years ago, prediction markets looked like a small experiment at the intersection of crypto, betting, and news. Now they process billions of dollars in political, economic, and sports events.
Against this backdrop, firms that have earned for decades on inefficiencies in derivatives, bonds, currencies, and cryptocurrencies are eyeing the segment. Their model is simple: look for price differences for the same risk on different platforms and close them faster than others.
One of the most telling signals came from Chicago. Major trading firm DRW began seeking specialists for a separate division focused on prediction markets. The job postings mention monitoring prices on Polymarket and Kalshi in real time, searching for discrepancies, and executing trades quickly before the market aligns prices.
Institutions Are Not Trying to Predict Outcomes
Major participants are entering this segment not as ordinary users. Their goal is not to predict the winner of the Champions League or the next UK prime minister better than anyone else. They work with the market structure itself.
Job postings and participant comments show several directions: arbitrage between platforms, microstructure trading, reacting to news with minimal delay, and searching for price imbalances between crypto markets and traditional betting platforms.
This is where professional firms have an advantage. They already know how to work with different currencies, exchanges, settlement systems, and data sources. Prediction markets add a new asset, but the logic of trading is familiar to them.
DRW, Wintermute, and IMC Build Teams
DRW is not the only company expanding work with such instruments. Wintermute, one of the largest algorithmic market makers in the crypto industry, is also seeking traders with experience in prediction markets.
IMC, another major proprietary trading participant, is hiring quantitative strategy specialists to work with binary contracts. Crypto exchanges are also joining this movement: job postings related to this direction have appeared at OKX and Crypto.com.
This is an important shift for the market. When individual traders look for ideas, that’s one thing. When companies with high-speed trading infrastructure start hiring teams, the segment moves to another level of maturity.
Sports Become a Major Source of Volume
One of the reasons for growing interest is sports markets. Large liquidity pools for football, basketball, and hockey have already formed on Polymarket.
The Champions League winner market processed about $256 million. Contracts on the 2026 NBA champion reached approximately $399 million. The market for the 2026 Stanley Cup winner collected about $79 million after sharp probability swings around the Carolina Hurricanes.
In total, just these three sports directions provided more than $730 million in volume. This is already comparable to the turnover of some mid-sized European betting exchanges. For professional traders, such volumes mean that there is now liquidity in the segment worth building infrastructure for.
Price Differences Become the Main Source of Profit
Professionals are most interested not in the forecast, but in price mismatches. If one market has already reacted to news and another has not, an opportunity for a trade arises.
A similar example occurred in the market for the next UK prime minister. On Polymarket, the probability of Andy Burnham winning rose from 24 to 43 cents on the morning of May 14 amid political rumors. At the same time, Betfair was already pricing the same outcome at about 50 cents, while the crypto platform still showed a lower price.
For an ordinary user, this looks odd. For a quant, it is a working opportunity. If you buy a contract where it is cheaper and then sell after the prices align, you can make a profit even before the event itself occurs.
Arbitrage Gets Complicated Due to Different Settlement Systems
Such trades sound simple, but in practice require complex infrastructure. Betfair operates in pounds, Polymarket uses crypto settlements, and other platforms may have their own rules for deposits, withdrawals, and outcome calculations.
A trader needs to move capital quickly, account for currency risks, fees, withdrawal times, spreads, and the likelihood of settlement delays. This is no longer a simple bet on an event, but a full-fledged trading operation between different financial environments.
This is where major firms feel confident. They are used to building systems that connect multiple platforms, currencies, and asset types. Prediction markets give them a new venue to apply old skills.
Why Prices Diverge
Prediction markets have two structural features that make them interesting for arbitrage. The first is a delay in reacting to information. Traditional betting exchanges sometimes update prices faster than decentralized platforms.
The second is fragmented liquidity. The same outcome can be traded on Polymarket, Kalshi, Betfair, with bookmakers, and on new on-chain platforms. Because of this, no single platform always shows the full market picture.
When liquidity is split, imbalances appear. Somewhere the news is already priced in, somewhere it is not. For trading firms, such discrepancies are the basis of their strategy.
Models From Sports Move to Crypto Markets
Not all participants work only with arbitrage. Mathematical models that estimate outcome probabilities more accurately than the average user have long been used in sports markets.
In football, models based on goal distribution and team strength are often used. They assess attack, defense, form, lineup, and possible scores. In basketball, models that update team ratings as new data appears are popular.
The idea is the same: compare the model’s internal probability with the contract price on the market. If the model gives a team a 47% chance and the contract trades at 43 cents, the trader can buy it and wait for the price to converge with the calculated estimate.
Experienced Bettors Remain Strong Competitors
The arrival of major firms does not mean they will immediately become the main players in sports markets. Rutgers University statistics professor Harry Crane believes that the accuracy of sports lines is largely shaped by specialized groups that have worked in betting for decades.
In his view, institutions may earn not from better understanding of events, but from short-term market dynamics. That is, they will be strong where speed, liquidity, and trading structure matter, not deep expertise in a particular match.
This is an important distinction. Sports betting veterans better understand teams, injuries, lineups, and outcome probabilities. Quant firms are better at working with price discrepancies, delays, liquidity, and execution.
Hyperliquid Prepares a New Level of Competition
Competition in the segment is just beginning. Hyperliquid, a major on-chain perpetual futures exchange, is already preparing prediction markets for the 2026 World Cup.
Such an event is almost perfect for the product. The tournament includes 64 matches over six weeks, and each match can generate dozens of related contracts: winner, score, totals, handicap, group stage qualification, and other outcomes.
If the platform can connect prediction markets with existing derivatives liquidity, competition with Polymarket and Kalshi will intensify. For traders, this will create even more opportunities to compare prices between platforms.
Prediction Markets Move Out of the Niche
The hiring boom shows that major firms no longer see this sector as peripheral. For them, it is a new trading environment where models from traditional finance, crypto derivatives, and sports betting can be applied.
Polymarket and Kalshi have already proven there is demand. Now the question is who will best exploit the market’s infrastructural weaknesses: old groups of sports bettors, crypto market makers, or classic quant firms.
The main change has already happened. Prediction markets have ceased to be just a place for betting on outcomes. They are becoming an asset class where liquidity, speed, probability calculation, and arbitrage between platforms matter.
What's Next?
In the coming months, competition will intensify. Major firms will hire traders, build models, connect data, and look for price gaps between platforms. The greater the volume, the more such opportunities there will be.
For users, this may mean more accurate prices and deeper liquidity. For platforms, it means increased turnover and a new level of infrastructure requirements. For old bettors, it means tougher competition from players used to profiting from microscopic market inefficiencies.
The main takeaway is simple. Polymarket, Kalshi, and new on-chain platforms no longer look like niche betting tools. Professional trading firms are coming not to guess outcomes, but for arbitrage, speed, and market errors. This is what turns prediction markets into a full-fledged part of crypto finance.
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