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.
An October 2026 snapshot of ZenHodl’s Polymarket, Kalshi, MLB and combo releases, with exact catalog counts, distinct clocks and measured capture limits.
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.
A practical guide to ZenHodl's recorded Kalshi L2 depth and deduplicated trade prints: what the fields mean, what the sample checks, and the hard limits.
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.
Corrected Oct 2026: a five-sport study reverses this MLB study's 32-second direction, and 1-second data shows the venues move within ~1 s.
Compare official history, free recorded books and ZenHodl archives. Check dates, depth, clocks, matching rules and license before choosing data.
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.
Find recorded Polymarket books through official data, free archives or paid files. Check the exact fields, clocks, captured window and gaps first.
Recorded Polymarket prices can replay decisions but not fills. See a checked 400-row sample, schema limits, and declared cost scenarios for backtests.
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.