Chicago-based trading giant DRW has spent decades profiting from disparities between different asset classes, and it 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 trading” firms – traders who use complex mathematics and analysis to set up strategies – are increasingly viewing prediction markets as a legitimate trading platform rather than a niche betting product.
The company, which has been a dominant force in the derivatives, fixed income and crypto markets since 1992, recently posted a job posting requiring candidates to simultaneously monitor real-time prices on both platforms, identify gaps where one misprices an outcome relative to the other, and respond quickly to take profits before prices converge. The strategies listed in these articles – including microstructure arbitrage, cross-platform arbitrage, and news-based momentum trading at sub-second speeds – are techniques perfected in crypto derivatives markets and now applied to sporting and political events.
DRW is not alone. Wintermute, the algorithmic market maker that processes billions in daily crypto volume, is recruiting algorithmic traders with experience in prediction markets. IMC, another proprietary trading firm, is also looking for quantitative traders comfortable with binary event contracts. Meanwhile, traditional crypto exchanges like OKX and Crypto.com have also recently posted job openings.
The hiring spree suggests that institutional trading firms increasingly believe that prediction markets have become a serious asset class ripe for profit.
Exploit the gap
So what’s driving this sudden surge? The catalyst is the volume traded on these platforms.
Polymarket alone handled between $22 billion and $40 billion in political, economic and sports markets in 2025, up from virtually nothing three years ago, and a growing share of that is concentrated in sports.
As of last week, Polymarket’s UEFA Champions League winner market transacted $256 million, the 2026 NBA Champions market generated $399 million and the 2026 NHL Stanley Cup market stands at $79 million after wild swings that saw the Carolina Hurricanes go from an implied probability of less than 10% to around 50% as they exit the Eastern Conference.
Together, these three markets alone represent more than $730 million in sports results volume, close to the annual trading volume of some mid-sized European sports betting exchanges.
But the real reason traditional companies are getting into this business may not be to predict 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 the calibration of prediction markets.
“The accuracy of the markets depends on specialized sports betting groups, who are much more precise in evaluating 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 that 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 having an idea of the outcome of the event.”
Simply put, DRW is not trying to predict who will win the Champions League. He tries to take advantage of the price movement before finding an answer to this question.
A recent example has emerged in the market for the next British Prime Minister.
On the morning of May 14, Andy Burnham’s odds of becoming Britain’s next leader in the ‘Next British Prime Minister’ bet on Polymarket jumped from 24 cents to 43 cents as political speculation intensified around a possible Labor leadership challenge. But Betfair, the London-based betting exchange with an annual volume of more than £1 billion, 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 punters, the gap was an interesting anomaly, but to a sophisticated quantitative trader, it was a classic 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 of $7,900 in a few hours by selling when he caught up with Betfair, which would have made a profit without the event even taking place.
This is a technique that traditional trading firms have used for decades: find an undervalued asset on exchanges and either simultaneously buy/sell, as in arbitrage, or buy the undervalued asset and wait for it to catch up.
Prediction markets, however, present an additional challenge. Betfair is moving into sterling while Polymarket is moving into crypto, requiring infrastructure that can move capital between currencies, exchanges and settlement systems.
This type of complexity plays directly to the strengths of large trading companies, such as DRW.
What motivates them?
Beyond outright arbitrage, traders point to two structural features that make prediction markets attractive today.
The first is information lag. Traditional betting exchanges often react more quickly than decentralized prediction platforms, creating windows where prices have not yet fully adjusted.
The second is liquidity fragmentation. The Champions League, NBA and Stanley Cup markets can trade simultaneously on Polymarket, Kalshi and traditional sportsbooks, meaning no single site necessarily reflects the complete market consensus.
For traders who focus on predicting outcomes rather than market structure, the toolkit looks increasingly familiar with quantitative finance.
Football traders often rely on “Dixon-Coles Poisson” models. The toolkit, developed in a 1997 academic paper, estimates team attack and defense strength and generates probability distributions for potential scores. This is something similar to the way a weather forecaster assigns precise probabilities to each possible outcome rather than making a single forecast.
Meanwhile, basketball traders frequently use “Bayesian hierarchical” 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 puts Arsenal’s Champions League chances at 47% while contracts are trading at 43 cents could buy and profit if the market eventually converges on that estimate.
The concept is known as closing line value, or CLV.
Crane explains why CLV is important: “It incorporates all known pre-match information, such as injuries and roster changes, and sharper players tend to wait closer to match time to place bets, as that is when the limits tend to be highest.”
The competition is here
Still, Crane remains skeptical that institutional firms will dominate sports prediction markets simply because they came in with larger balance sheets.
“Right now, the most important players in sports betting markets are not the institutions,” he said. “The most powerful players have been in these markets for decades, and prevailing market prices are likely determined by the same groups and information sources long before prediction markets existed.”
Despite skepticism, talent migration is already underway.
Crypto market makers study sports analytics and expected goal patterns, while traditional sports betting specialists are increasingly being recruited by crypto companies seeking expertise that has taken 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, featuring 64 matches over six weeks and generating thousands of correlated binary outcomes.
The infrastructure is being built and offices are now staffed, with models working on potential outcomes.
The main question is whether institutions can outperform seasoned sports bettors by finding their edge and applying sophisticated business models used in traditional finance. But on latency, market structure and cross-platform inefficiencies, competition has already begun.
Read more: Hyperliquide emerges as a challenger to traditional exchanges and prediction markets, says FalconX
