The first commercially-available dataset of paired order intent + fill outcomes + post-fill markouts (30s/60s/90s) from a live Polymarket sports trading bot. 24K+ orders across 7 sports + 3 market types. Includes the bot's pre-trade fill predictions, compression diagnostics, and shield-policy reasoning. Ideal for execution-alpha research, fill-probability modeling, and adverse-selection auditing.
Neither Polymarket nor Kalshi publishes historical orderbook data — their public APIs give you trades and last price, never the full book over time, and never joined to the game. That makes this data physically non-reconstructable retroactively: the score-sync has to be captured live, tick by tick, as each game happens. We have been recording it. A provider starting today cannot backfill what they did not capture. Every snapshot here carries the live score, period and clock at tick time — the one thing every other "tick data" seller is missing.
24,278 orders · markouts at 30s/60s/90s · NCAAMB/NBA/NHL/NCAAWB/NFL/EPL/LALIGA
Join your win-probability output on event and clock, and measure how many seconds the book trailed a real probability shift after a score.
Full depth lets you model slippage and partial fills against what the market actually showed — not an assumption that you hit last price.
With Polymarket and Kalshi on one timeline, measure which book moves first after a scoring event — the basis for cross-venue arbitrage research.
Citable depth and trade data for prediction-market efficiency and price-discovery studies, with provenance metadata.
We publish real score-synced sample rows — not a marketing mockup. Load them in DuckDB, check the schema, confirm the score-join is there, then decide.