Chicago-based trading giant DRW has spent decades profiting from mismatches between different asset classes, and is now building a dedicated prediction market desk aimed at platforms like Polymarket and Kalshi.
The move is one of the clearest signs yet that sophisticated “quant trading” firms – traders who use complex mathematics and analysis to set strategies – increasingly view prediction markets as a legitimate trading venue rather than a niche betting product.
The company that has been a dominant force in the derivatives, fixed income and cryptocurrency markets since 1992, recently posted a job opening that requires candidates to monitor real-time prices on both platforms simultaneously, identify gaps where one is mispricing an outcome relative to the other, and react quickly to make profits before prices converge. The strategies listed in these posts, including microstructure arbitrage, cross-platform arbitrage, and news-driven momentum trading at sub-second speeds, are techniques honed in the crypto derivatives markets and now being applied to sporting and political events.
DRW is not alone. Wintermute, the algorithmic market maker that processes billions in daily crypto volume, is hiring algorithmic traders with experience in prediction markets. IMC, another trading company of its own, is also looking for quantitative traders who are comfortable trading binary event contracts. Meanwhile, traditional crypto exchanges like OKX and Crypto.com have also recently posted job openings.
The wave of hiring suggests that institutional trading firms increasingly believe that prediction markets have matured into a major asset class and are poised to profit.
Exploiting the mismatch
So what is driving this sudden push? The catalyst is the volume that is traded on these platforms.
Polymarket alone processed between $22 billion and $40 billion in the political, economic and sports markets in 2025, up from virtually nothing three years ago, and an increasing proportion of that figure is concentrated in sports.
As of last week, Polymarket’s market for the UEFA Champions League winner has processed $256 million, the 2026 NBA champion market has generated $399 million and the 2026 NHL Stanley Cup market sits at $79 million after wild swings that saw the Carolina Hurricanes rise from less than a 10% implied probability to around 50% as they emerged from the Eastern Conference.
Combined, those three markets alone represent more than $730 million in sports results volume, approaching the annual trading volume of some mid-sized European sports betting exchanges.
But the real reason traditional companies are getting into this industry may not be predicting outcomes better than everyone else, market watchers say.
“I don’t expect institutional capital to contribute significantly to the accuracy of these markets, especially in the case of sports,” said Harry Crane, a statistics professor at Rutgers University who studies prediction market calibration.
“The precision of the markets is driven by specialized sports betting groups, who are much more precise in pricing sports results.
Instead, Crane argues, companies like DRW are likely applying trading techniques developed in traditional financial markets to exploit price mismatches.
“To the extent they are profitable, institutions are likely applying techniques on short-term market dynamics and other technical aspects of trading that capitalize on short-term market fluctuations without knowing the outcome of the event.”
Simply put, DRW does not try to predict who will win the Champions League. You are trying to take advantage of the way prices move before that question is answered.
A recent example appeared on the market for Britain’s next prime minister.
On the morning of May 14, Andy Burnham’s odds of becoming the UK’s next leader in the ‘UK’s Next Prime Minister’ bet at Polymarket rose from 24 cents to 43 cents as political speculation around a possible challenge to the Labor leadership intensified. But Betfair, the London-based betting exchange with an annual turnover of more than £1bn, had already identified the move, pricing Burnham at the equivalent of 50p, while Polymarket was still showing 24p.
Polymarket took hours to catch up.
To casual bettors, the gap was an interesting anomaly, but to a sophisticated quantitative trader, it was a textbook cross-market inefficiency waiting to be exploited.
In theory, a trader could have bought $10,000 worth of Burnham contracts on Polymarket at 24 cents after noticing the mismatch, before making a profit worth $7,900 in a matter of hours by selling when it reached Betfair, which would have made a profit without the event even having to take place.
It is a technique that has been used for decades by traditional trading firms: find an asset with a wrong price on the exchanges and buy/sell simultaneously, as in arbitrage, or buy the asset with a lower price and wait for it to catch up.
However, prediction markets introduce an additional challenge. Betfair is settled in pounds sterling, while Polymarket is settled in cryptocurrencies, requiring infrastructure capable of moving capital between currencies, exchanges and settlement systems.
That kind of complexity plays directly to the strengths of large commercial companies, like DRW.
What drives them?
Beyond outright arbitrage, traders point to two structural features that make prediction markets attractive today.
The first is the delay of information. Traditional betting exchanges typically react more quickly than decentralized prediction platforms, creating windows where prices have not yet fully adjusted.
The second is the fragmentation of liquidity. The Champions League, NBA and Stanley Cup markets can be traded simultaneously on Polymarket, Kalshi and traditional bookmakers, meaning that no one venue necessarily reflects the full market consensus.
For traders focused on forecasting outcomes rather than market structure, the toolset looks increasingly familiar to quantitative finance.
Football traders often rely on “Dixon-Coles Poisson” models. The toolset, developed in a 1997 academic paper, estimates the team’s attack and defense strength and generates probability distributions for possible outcomes. This is somewhat similar to how a weather forecaster assigns precise probabilities to each possible outcome rather than making a single prediction.
Meanwhile, basketball traders frequently use “hierarchical Bayesian” models that update assessments of team strength as new information arrives.
The goal of both models is to identify discrepancies between a model’s estimated probability and the probability implied by market prices.
A trader whose model values Arsenal’s Champions League chances at 47% while contracts are trading at 43 cents can buy and profit if the market eventually converges towards that estimate.
The concept is known as closing line value or CLV.
Crane explains why CLV is important: “It incorporates all known pre-game information, such as injuries and lineup changes, and smarter players tend to wait until closer to game time to place bets because that’s when the limits tend to be highest.”
The competition is here
Still, Crane remains skeptical that institutional companies will dominate sports prediction markets simply because they have come in with bigger balance sheets.
“Right now, the smartest players in the sports betting markets are not the institutions,” he said. “The strongest players have been in these markets for decades, and the prevailing prices in the market are likely driven by the same groups and the same sources of information since long before prediction markets existed.”
Despite skepticism, talent migration is already underway.
Crypto market makers are studying sports analytics and expected target models, while crypto companies are increasingly hiring traditional sports betting specialists seeking expertise that took years to develop.
And it’s not just theoretical.
HyperLiquid, the on-chain perpetual exchange that processed more than $10 billion in daily volume at its peak, is already preparing to launch prediction markets ahead of the 2026 World Cup, running 64 games over six weeks and generating thousands of correlated binary outcomes.
Infrastructure is being built and offices are already being staffed, with models working on potential outcomes.
The main question is whether institutions can outperform veteran sports bettors by finding their edge and applying sophisticated business models used in traditional finance. But on latency, market structure and cross-platform inefficiencies, the competition has already begun.
Read more: Hyperliquid is becoming a challenger to traditional exchanges and prediction markets, says FalconX
