Big trading firms are treating prediction markets like real markets — and they’re staffing up accordingly.
Chicago trading powerhouse DRW, a decades-long player across derivatives, fixed income and crypto, is building a dedicated prediction-market desk aimed at platforms such as Polymarket and Kalshi. Job listings from the firm call for traders who can watch prices across platforms in real time, spot mispricings, and execute strategies — microstructure arbitrage, cross‑platform arbitrage and news-driven momentum trading — at sub‑second speeds. Those are the same quantitative techniques honed in crypto derivatives markets, now being redeployed on sports and political contracts.
DRW isn’t alone. Algorithmic market maker Wintermute is recruiting traders with prediction‑market experience, IMC is hunting quantitative traders comfortable with binary event contracts, and traditional crypto exchanges like OKX and Crypto.com have posted similar roles. The hiring wave signals that institutional quant shops increasingly view prediction markets as a legitimate asset class — not just a niche betting product.
What’s driving the rush? Volume.
Polymarket alone reportedly processed between $22 billion and $40 billion across political, economic and sports markets in 2025, up from almost nothing a few years ago. A growing share of that action is in sports: recent volumes include
- UEFA Champions League Winner: $256 million
- 2026 NBA Champion: $399 million
- 2026 Stanley Cup: $79 million
Together those three sports markets exceed $730 million in volume — approaching the annual trading of some mid‑sized European sports betting exchanges. That liquidity, plus fragmented markets and information lags, is fertile ground for quant strategies.
Institutional traders aren’t necessarily trying to out-forecast the best sports bettors, though. Rutgers statistics professor Harry Crane, who studies prediction market calibration, says institutional capital likely isn’t improving accuracy in sports markets. The sharpest sports prices are still driven by longstanding specialist groups. Instead, Crane and market observers argue quant shops are exploiting short‑term market dynamics — price convergence, latency and fragmented liquidity — to make profits without having superior knowledge of the underlying outcomes.
A recent example illustrates the point. On May 14, Polymarket’s “Next UK Prime Minister” market showed Andy Burnham’s odds jump from $0.24 to $0.43 amid churn, while Betfair (which trades in sterling) had already priced him near $0.50. Polymarket took hours to catch up. For a quant trader spotting that gap, buying the underpriced contracts and selling once Polymarket aligned with Betfair could have locked in a substantial profit — and the event itself didn’t need to resolve to generate the return. Executing that play, however, requires infrastructure to move capital across currencies, exchanges and settlement rails — an operational strength of large trading firms.
Two structural features make prediction markets attractive right now:
- Information lag: centralized, faster exchanges often update prices sooner than decentralized platforms, creating exploitable windows.
- Liquidity fragmentation: the same event can trade across Polymarket, Kalshi and traditional sportsbooks, so no single venue always reflects the full market consensus.
On the modeling side, institutional and specialist traders use familiar statistical toolkits. Soccer traders often lean on Dixon‑Coles Poisson models to estimate attack/defense strengths and score probabilities, while basketball traders use Bayesian hierarchical models that update team strength as new info arrives. The goal is identical to other markets: identify where a model’s probability differs from implied market prices, then trade to capture “closing line value” (CLV). Crane notes CLV is valuable because it aggregates late-breaking pregame information — the time when the sharpest players often act.
Despite skepticism that institutions will instantly dominate sports prediction markets, the talent shift is underway. Crypto market makers are studying sports analytics and expected‑goals models, while veteran sports‑betting specialists are being recruited into crypto teams. Exchanges and builders are preparing for bigger sport-driven product launches: HyperLiquid — an on‑chain perpetuals exchange that peaked at over $10 billion in daily volume — is planning prediction markets for the 2026 World Cup, which would produce thousands of correlated binary outcomes.
The takeaway: the desks are being staffed, the infrastructure is being built, and the models are running. Whether big firms can outcompete veteran sharps at predicting sports outcomes remains open. But on latency, market structure and cross‑platform inefficiencies, the competition has clearly begun.
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