Polymarket Sports Trading We predict game results using models trained on historical data, then trade on Polymarket (NHL, NBA, Tennis, etc.) based on those predictions. Main idea: Collect game data (outcomes, scores, in-game prices) → train models on that data → use the trained models to predict game outcomes and price moves → trade when our predictions disagree with the market. Current implementation: NHL (Polymarket + ESPN). The same flow—data → train → predict → trade—extends to other sports by adding sport-specific data and market discovery. --- How It Works 1. Data — We gather historical game data: who won, scores, period/clock, and Polymarket token prices over time. This is the training set. 2. Training — We train ML models on that data: a pre-game model predicts who wins (P(home wins)); an in-game model predicts reward (will price go up if we buy now?) or min/max price in a window. 3. Prediction — At prediction time we feed current game state (and prices) into the trained models and get probabilities or price targets. 4. Trading — We compare our predictions to Polymarket’s pri

⚠ No verified equity curve — no track-record source connected.

No score history is stored yet — only the current score is shown.

⚠ No live verification account connected — ask for proof before buying.

Alerts on changes: coming soon

Prop-firm compatibility not provided.

Data-completeness & trust index (not a profitability rating)