Sports · Quantitative#164

Live In-Game Win-Probability Divergence

A win-probability model fitted on historical play-by-play data can tell you what a 7-point lead with six minutes left is truly worth, and that number frequently differs from the live market, which overreacts to whatever just happened. You run the model against a live game-state feed and trade whenever the gap exceeds a threshold that covers fees. The hardest part is feed latency: if your data arrives later than everyone else's, you are the one being picked off.

What you need to run it

Low-latency play-by-play or game-state feed, ideally faster than the broadcast
Win-probability model trained on historical in-game states for that sport
Latency measurement and an automatic stand-down when your feed falls behind

Where this applies

Markets on Polymarket where live in-game win-probability divergence is the natural play:

Will the Heat beat the Bucks tonight? (traded live in the fourth quarter)
Will Real Madrid win after going down a goal?
Will this NFL game go to overtime?

Capabilities this demands

Low latencyModel / quantFeed ingestion

At a glance

CategoryQuantitative

MarketSports

Requirements3

CapabilitiesLow latency, Model / quant, Feed ingestion

VenuePolymarket (CLOB, Polygon)

Build it

Related sports strategies

This is documentation, not advice. Poly Research & Robotics publishes how these strategies work because the method should be checkable — not as a recommendation to trade them. See the full strategy database (297 strategies) or the data resources directory.