16 posts tagged “historical data”.
Learn a four-step audit to find capture gaps in any market-data archive: derive coverage, dark time and cadence from the rows, not the README.
Recount the $9 Kalshi tryout's pinned release and explore recorded spreads, book changes and taker flow, with clear clock and coverage limits.
We ran the numbers on our own $9 MLB matched-book tryout: cross-venue spread, a shared capture gap, and why a settlement-label check isn't a prediction.
A dated research snapshot on prediction-market liquidity, calibration, and execution across Polymarket and Kalshi — with current evidence caveats.
Buy just the Kalshi order-book slice you study: ATP/WTA/ITF tennis, MLB, WNBA, or LoL/CS2 esports — $39 each, same live capture as the full tape.
Kalshi L2 depth (up to 20 levels/side) plus a deduplicated trade tape with taker side, from a venue that doesn't retain its book — sold as dated vintages...
L2 depth for the first 48-team World Cup final: 1.01M rows on Spain vs. Argentina, 20.8M across 47 matches — including which we lost. $39.
We time-aligned sampled Kalshi and Polymarket books on the same MLB games for 16 days. Polymarket led on all 16 and divergences half-closed in ~34...
Vendor-neutral 2026 buyer's guide to historical Polymarket and Kalshi order-book data: capture it yourself or buy — what each vendor has, what it costs.
How we made our data archive buyable by an AI agent over x402 on Base, with on-chain proof. Update 2026-09-27: now x402 v2; new purchases can be paused.
Polymarket's API gives you trades but not historical depth, and the book can't be rebuilt after the fact. Your real options for order-book history.
Compare Polymarket price history, recorded book quotes and L2 depth for backtests. Check clocks, costs and capture gaps before choosing a dataset.
How we designed a single unified API schema that serves calibrated win probabilities for 12 different sports — basketball, hockey, baseball, football...
Counter-Strike 2 has the widest mispricings on Polymarket. It also has the worst data infrastructure. Here's how we built a 4-tier model that handles both.
How to get real-time sports scores using WebSockets in Python. Compares HTTP polling vs WebSocket streaming, with working code for building a live data feed.
Python examples for ESPN scoreboard and summary endpoints: event IDs, winprobability fields, playId joins, missing sports and data limits.