Chicago-based trading giant DRW has spent decades profiting from mismatches between different asset classes and is now building a dedicated prediction market desk targeting platforms such as Polymarket and Kalshi.
The move is one of the clearest signs yet that sophisticated “quantitative traders” – traders who use complex math and analysis to set strategies – are increasingly seeing prediction markets as a legitimate trading venue rather than a niche betting product.
The firm, which has been a dominant force in the derivatives, fixed income and crypto markets since 1992, recently posted a job posting that requires candidates to monitor real-time prices across both platforms simultaneously, identify gaps where one is mispricing an outcome relative to the other and react quickly to profit before pricing converges. 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 crypto-derivatives markets and are now being applied to sports 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 proprietary trading firm, is also looking for quantitative traders who are comfortable operating across binary event contracts. Meanwhile, traditional crypto exchanges like OKX and Crypto.com have also recently published job postings.
The hiring wave suggests that institutional trading firms increasingly believe that prediction markets have matured into a serious asset class and are ripe for profit.
Exploiting the mismatch
So what’s driving the sudden push? The catalyst is the volume traded on these platforms.
Polymarket alone processed between $22 billion and $40 billion across political, financial and sports markets by 2025, up from virtually nothing three years ago, and a growing share of this is concentrated in sports.
Since last week, Polymarket’s UEFA Champions League Winner market has handled $256 million, the 2026 NBA Champion market has delivered $399 million, and the 2026 NHL Stanley Cup market is sitting at $79 million after wild swings that saw the Carolina Hurricanes rise from under 10% suggested 5% probability for Easter to around 0%.
Combined, these three markets alone represent over $730 million in sports betting volume, approaching the annual trading volume of some mid-sized European sports betting exchanges.
But the real reason traditional companies are pushing into this industry may not be to predict outcomes better than everyone else, market observers say.
“I don’t expect the institutional capital to contribute meaningfully to the accuracy of these markets, especially in the case of sports,” said Harry Crane, a statistics professor at Rutgers University who studies predictive market calibration.
“The accuracy of the markets is driven by specialist sports betting groups who are much sharper at pricing sports outcomes.
Instead, Crane argues that firms like DRW are likely using trading techniques developed in traditional financial markets to exploit price mismatches.
“To the extent that they are profitable, institutions likely employ techniques of short-term market dynamics and other technical aspects of trading that capitalize on short-term market fluctuations without insight into the outcome of the event.”
In short, DRW is not trying to predict who will win the Champions League. It tries to take advantage of the way prices move before that question is answered.
A recent example emerged in the market for Britain’s next prime minister.
On the morning of May 14, Andy Burnham’s odds to become the next UK leader in the “Next UK Prime Minister” bet on Polymarket rose from 24 cents to 43 cents as political speculation intensified around a potential Labor leadership challenge. But Betfair, the London-based betting exchange with over a billion pounds in annual volume, had already identified the move, pricing Burnham at the equivalent of 50 cents, while Polymarket was still showing 24 cents.
It took Polymarket hours to catch up.
To casual bettors the gap was an interesting anomaly, but to a sophisticated quant trader it was a textbook cross-market inefficiency waiting to be exploited.
In theory, a trader could have bought $10,000 of Burnham contracts on Polymarket at 24 cents after noticing the mismatch, before locking in a $7,900 profit in a matter of hours by selling when it caught up with Betfair, which would have made a profit without the event even having to happen.
It’s a technique that has been used for decades by traditional trading firms: finding a mispriced asset across exchanges and either simultaneously buying/selling, as in arbitrage, or buying the underpriced asset and waiting for it to catch up.
However, prediction markets introduce an additional challenge. Betfair settles in sterling, while Polymarket settles in crypto, which requires infrastructure capable of moving capital across currencies, exchanges and settlement systems.
That kind of complexity plays directly into the strengths of large trading companies such as DRW
What drives them?
Beyond direct arbitrage, traders point to two structural features that make prediction markets attractive today.
The first is information delay. Traditional betting exchanges often react faster than decentralized prediction platforms, creating windows where prices are not yet fully adjusted.
The second is liquidity fragmentation. The Champions League, NBA and Stanley Cup markets can be traded simultaneously across Polymarket, Kalshi and traditional sportsbooks, meaning that no single venue necessarily reflects the full market consensus.
For traders who focus on predicting performance rather than market structure, the toolbox looks increasingly familiar to quantitative finance.
Football traders often rely on “Dixon-Coles Poisson” models. The toolkit, developed in a 1997 academic paper, estimates team offense and defense strength and generates probability distributions for potential scorelines. This is somewhat similar to how a weather forecaster assigns precise probabilities to every possible outcome rather than making a single prediction.
Meanwhile, basketball traders often use “Bayesian hierarchical” models that update assessments of team strength as new information becomes available.
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 trade at 43 cents, could buy and profit if the market eventually converges towards this estimate.
The concept is known as closing line value or CLV.
Crane explains why CLV matters: “It incorporates all known information before the game, such as injuries and lineup changes, and the sharpest 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 firms will dominate the sports prediction markets simply because they have come with larger balance sheets.
“Right now, the sharpest players in the sports betting markets are not the institutions,” he said. “The sharpest players have been in these markets for decades, and the prevailing market prices have likely been driven by the same groups and sources of information since long before prediction markets existed.”
Despite the skepticism, the migration of talent is already underway.
Crypto market makers study sports analytics and models for expected goals, while traditional sports betting specialists are increasingly being recruited by crypto firms seeking expertise that took years to develop.
And it’s not just theoretical.
HyperLiquid, the onchain perpetuals exchange that handled over $10 billion in daily volume at its peak, is already preparing to launch prediction markets ahead of the 2026 World Cup, with 64 games over six weeks and generating thousands of correlated binary results.
The infrastructure is being built and the desks are now being staffed with models working on potential outcomes.
The main question is whether institutions can outperform veteran sports bettors by finding their edge and employing sophisticated trading models used in traditional finance. But on latency, market structure and cross-platform inefficiencies, the competition has already begun.
Read more: Hyperliquid emerges as a challenger to traditional exchanges and prediction markets, FalconX says
