The raw ticks underneath the Microstructure Pack: 21.2M top-of-book burst snapshots + 9.9M mid-move events + 12.8M score-change windows. Dec 2025 → Apr 2026, 7 sports (NCAAMB, NBA, NFL, MLB, NHL, NCAAWB, NCAAFB). Companion to the Microstructure Pack — lets buyers re-derive existing metrics with custom thresholds, build new event detectors, or train fill-probability models on the underlying stream. Includes manifest, schema, methodology.
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.
44M rows across 3 Parquet shards · zstd-compressed
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.