Quantitative#024

Sports Analytics Model

You run a sports model (ELO ratings, the Massey method, or a machine-learning model) that outputs a win probability for each game, then trade whenever Polymarket's price differs from your model by more than a set threshold. The edge is having a calibrated probability the casual crowd lacks, applied across many games. Your model has to be good enough to beat the sharp sportsbook consensus, or the gap you're trading is just your own error.

What you need to run it

Team/player-level dataset and trained win-probability model
Live injury, lineup, and weather feeds
Backtest showing the model beats consensus sportsbooks

Where this applies

Markets on Polymarket where sports analytics model is the natural play:

Will the Celtics beat the Knicks tonight?
Will Manchester City beat Arsenal in their next Premier League match?
Will the Dodgers beat the Padres tonight?

Capabilities this demands

Data ingestionModel / quantCustom code / API

At a glance

CategoryQuantitative

Requirements3

CapabilitiesData ingestion, Model / quant, Custom code / API

VenuePolymarket (CLOB, Polygon)

Build it

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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 (147 strategies) or the data resources directory.