Polymarket Sports Data for Event-Driven Market Research
Study how sports markets on Polymarket reprice around lineup news, injury updates, and event windows. Combine historical prices, metrics, and L2 depth to test strategies under realistic execution conditions.
Live Dataset Surface
polymarket sports data
Rows
10B+
Historical archive
Markets
450K+
Open + resolved
Resolution
1m
Minute snapshots
Endpoint stream
/markets/{slug}/prices
42 ms
/markets/{slug}/metrics
50 ms
/markets/{slug}/books
61 ms
Dataset
What Is Included
- Historical prices for sports-related markets across selected windows.
- Historical market metrics for spread and liquidity analysis.
- Historical order book snapshots for depth-aware execution modeling.
- Market discovery filters to isolate sports events and related markets.
Code
API Example
curl "https://api.polymarketdata.co/v1/markets?search=nba&limit=50" \ -H "X-API-Key: YOUR_API_KEY"pythonApplications
Use Cases
- Model tradability changes around event-specific volatility windows.
- Compare strategy performance before and after execution costs.
- Build category-specific dashboards for sports market behavior.
Questions
FAQ
Which sports have the most markets on Polymarket?+
NFL and NBA have consistently high market counts, followed by soccer (Premier League, Champions League) and major combat sports events. Use the discovery search endpoint to explore what's available for a sport you care about.
Are player prop markets included, or just game outcome markets?+
Both. Polymarket hosts markets on individual player stats, season awards like MVP and Rookie of the Year, and game results. All of these are queryable through the same discovery and history endpoints.
How fast do sports markets reprice after a score or game result?+
This is one of the most interesting things you can study in the data. Repricing speed varies — some markets move within a minute of a score change, others lag noticeably. The 1-minute resolution captures most of the meaningful action.
Can I study how liquidity changes in the hours before a game?+
Yes. Pull the metrics and order book history for your market in the 24-48 hours leading up to kickoff. You'll typically see spread tighten and depth improve as the event approaches — a pattern worth quantifying before building a strategy around it.
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Use a production-grade dataset for research, strategy validation, and analytics without rebuilding the ingestion layer from scratch.