A significant wave of hiring indicates that trading firms are shifting their perception of Polymarket from a niche betting platform to a serious trading venue.

As trading volumes on Polymarket and Kalshi increase, quantitative trading firms are becoming more interested in prediction markets, not for event forecasting but for taking advantage of market inefficiencies to generate profits.

Key Insights:

• Prominent quantitative trading firms such as DRW, Wintermute, and IMC are establishing specialized desks to engage in trading on platforms like Polymarket and Kalshi, viewing these markets as legitimate asset classes.
• These firms prioritize exploiting short-term pricing inefficiencies rather than predicting outcomes, utilizing high-speed arbitrage and market microstructure strategies developed in traditional finance and cryptocurrency.
• While seasoned sports betting groups continue to influence pricing accuracy, institutional players are rapidly entering the space as trading volumes increase and new infrastructures, including on-chain exchanges like HyperLiquid, are established in anticipation of major events like the 2026 World Cup.

DRW, a Chicago-based trading powerhouse, has spent years capitalizing on discrepancies between asset classes and is now creating a dedicated prediction market desk aimed at platforms such as Polymarket and Kalshi. This development highlights that sophisticated quantitative trading firms—those employing intricate mathematics and analysis for strategy development—are increasingly recognizing prediction markets as viable trading environments rather than niche betting options.

The firm, which has been influential in derivatives, fixed income, and cryptocurrency markets since 1992, recently advertised positions requiring candidates to monitor real-time prices across both platforms simultaneously, identify mispricings, and respond swiftly to capitalize on profits before prices align. The strategies mentioned in these listings—including microstructure arbitrage, cross-platform arbitrage, and news-driven momentum trading at sub-second intervals—are techniques refined in cryptocurrency derivatives and are now being utilized for sports and political events.

DRW is not the only player in this space. Wintermute, an algorithmic market maker managing billions in daily crypto trading, is seeking algorithmic traders experienced in prediction markets. IMC, another proprietary trading firm, is also on the lookout for quantitative traders who can navigate binary event contracts. Additionally, traditional crypto exchanges such as OKX and Crypto.com have recently posted job openings.

This hiring surge indicates that institutional trading firms increasingly view prediction markets as a matured asset class that holds profitable opportunities.

Understanding the Driving Forces

What is fueling this sudden interest? The surge in trading volume on these platforms is a significant factor.

Polymarket alone processed between $22 billion and $40 billion in political, economic, and sports markets in 2025, a remarkable increase from virtually nothing three years prior, with a growing portion of that volume now focused on sports.

As of last week, Polymarket’s market for the UEFA Champions League Winner had processed $256 million, the 2026 NBA Champion market had seen $399 million, and the 2026 NHL Stanley Cup market stood at $79 million after dramatic fluctuations that pushed the Carolina Hurricanes’ implied probability from below 10% to around 50% as they progressed from the Eastern Conference.

Collectively, these three markets represent over $730 million in sports outcome volume, nearing the annual trading volume of some mid-sized European sports betting exchanges.

However, the real reason traditional firms are entering this space may not necessarily be to forecast outcomes better than others, according to market experts.

«I don’t anticipate that institutional capital significantly contributes to the accuracy of these markets, especially in sports,» stated Harry Crane, a statistics professor at Rutgers University who specializes in prediction market calibration.

«The accuracy of these markets is primarily driven by specialized sports betting groups, which are far more adept at pricing sports outcomes. Instead, Crane suggests that firms like DRW likely apply trading strategies developed in traditional financial markets to exploit pricing mismatches. «To the extent they are profitable, the institutions are likely employing techniques focused on short-term market dynamics and technical trading aspects that capitalize on short-term fluctuations without insights into the event outcomes.»

In simpler terms, DRW is not focused on predicting the Champions League winner; it aims to profit from the price movements prior to that determination.

A recent instance of this appeared in the market for the next Prime Minister of the UK. On May 14, Andy Burnham’s odds of becoming the next U.K. leader surged from 24 cents to 43 cents on Polymarket as political speculation heightened regarding a potential Labour leadership challenge. Meanwhile, Betfair, a London-based betting exchange with over a billion pounds in annual volume, had already priced Burnham at 50 cents while Polymarket still reflected 24 cents.

Polymarket took hours to adjust.

For casual bettors, this discrepancy was merely an interesting anomaly, but for a savvy quantitative trader, it represented a textbook example of cross-market inefficiency ripe for exploitation.

Theoretically, a trader could have acquired $10,000 worth of Burnham contracts on Polymarket at 24 cents upon noticing the discrepancy, then secured $7,900 in profit within hours by selling when the prices aligned with Betfair, achieving profit without needing the event to occur.

This strategy has been employed by traditional trading firms for decades: identifying a mispriced asset across exchanges and either executing simultaneous buy/sell orders, as in arbitrage, or purchasing the undervalued asset and waiting for its price to catch up.

However, prediction markets introduce an added complexity. Betfair settles in sterling, while Polymarket settles in cryptocurrency, necessitating infrastructure capable of transferring capital across various currencies, exchanges, and settlement systems. This complexity aligns well with the capabilities of large trading firms like DRW.

What Motivates Them?

Beyond simple arbitrage, traders identify two structural features that enhance the appeal of prediction markets today.

The first is the information lag. Traditional betting exchanges often react more rapidly than decentralized prediction platforms, creating opportunities where prices haven’t fully adjusted yet.

The second feature is liquidity fragmentation. Markets for the Champions League, NBA, and Stanley Cup can operate simultaneously across Polymarket, Kalshi, and traditional sportsbooks, meaning no single venue necessarily represents the complete market consensus.

For traders concentrating on outcome forecasting rather than market structure, the toolkit increasingly resembles quantitative finance.

Soccer traders often utilize “Dixon-Coles Poisson” models. This toolkit, introduced in a 1997 academic paper, estimates team strengths in attack and defense and generates probability distributions for potential scorelines. This approach mirrors how weather forecasters assign precise probabilities to every possible outcome rather than providing a singular prediction.

Meanwhile, basketball traders frequently employ “Bayesian Hierarchical” models that revise assessments of team strength as new information becomes available.

The objective for both models is to pinpoint discrepancies between the probability estimated by a model and the probability implied by market prices.

A trader whose model values Arsenal’s chances in the Champions League at 47% while contracts trade at 43 cents may choose to buy and profit if the market ultimately converges toward that estimate.

This concept is termed closing line value, or CLV.

Crane elaborates on the significance of CLV: «It encompasses all known pre-game information, such as injuries and lineup changes, and the sharpest players typically wait until closer to game time to place bets because that is when the limits are highest.»

Competition is Here

Nonetheless, Crane remains doubtful that institutional firms will dominate sports prediction markets merely because they possess larger balance sheets.

Despite the skepticism, the migration of talent is already in motion. Crypto market makers are delving into sports analytics and expected-goals models, while traditional sports betting experts are increasingly being recruited by crypto firms seeking the expertise that has taken years to cultivate.

And this is not just theoretical. HyperLiquid, the on-chain perpetuals exchange that reached over $10 billion in daily volume at its peak, is already preparing to launch prediction markets ahead of the 2026 World Cup, which will feature 64 games over six weeks and produce thousands of correlated binary outcomes.

The infrastructure is being developed, and the desks are now being staffed, with models analyzing potential outcomes. The primary question remains whether institutions can outperform seasoned sports bettors by identifying their advantages and applying advanced trading models employed in traditional finance. However, in terms of latency, market structure, and cross-platform inefficiencies, the competition has already commenced.