DateTime64(3) Snapshot Schema for Sports Analysts
Resolved Markets provides sports analysts with real-time orderbook data from NBA, NFL, and EPL prediction markets on Polymarket, capturing market sentiment through bid/ask spreads and order flow dynamics before games occur. Access continuous orderbook snapshots with millisecond timestamps and full depth arrays, enabling you to identify when sharp bettors accumulate positions, detect line movement ahead of public perception, and forecast outcomes by analyzing collective prediction market pricing. The platform tracks 100+ sports-related markets with 20Hz capture rates during high-volume periods, WebSocket streaming for live updates, and historical data for detailed post-game analysis of market accuracy and predictive power.
Resolved Markets ships DateTime64(3) Snapshot Schema for sports analysts who study the structured prediction-market data layer. With DateTime64(3) timestamps with full bid/ask arrays, every shift in best_bid, best_ask, mid_price, spread, bids[], asks[] is captured, time-stamped, and queryable — pre-game, in-play, and post-game.
Data challenges Sports Analysts run into
DateTime64(3) Snapshot Schema from Resolved Markets is built around the data gaps Sports Analysts hit when they try to work with raw Polymarket feeds.
Traditional betting lines hide order flow and market depth
Sportsbooks publish final betting lines (point spreads, totals, moneylines) but don't reveal orderbook depth, spread width, or order accumulation patterns. Prediction markets on Polymarket show richer information—you can see exactly where liquidity clusters, how wide bid/ask spreads widen during uncertainty, and when large orders cause prices to shift. Traditional line data obscures this microstructure, leaving you unable to distinguish between genuine market consensus and thin, uninformed pricing.
Prediction market prices often move before public becomes aware
Sharp sports bettors and professional traders often move prediction market prices hours or days before casual bettors notice. By the time a line appears in mainstream betting forums, informed traders have already positioned. Resolved Markets captures continuous orderbook snapshots, revealing when unexpected buying (or selling) pressure emerges—the signal that sharpness is accumulating. Real-time WebSocket streams and millisecond timestamps let you detect position building moments before prices move 5-10 points.
Sharp money accumulation patterns remain invisible
Public betting lines show only final price, not the journey to reach that price. Did that NFL spread move 2 points because of sharp money or casual volume? Orderbook depth analysis reveals the answer—sharp orders typically result in steep spread compression and rapid order accumulation at specific price levels. Resolved Markets' full depth arrays show you the structure of orders behind each price level, distinguishing intelligent hedging from noise.
Post-game market analysis tools are limited or non-existent
After games conclude, sportsbooks don't publish how accurate their pre-game lines were or analyze why prediction markets mispriced certain outcomes. Resolved Markets' historical orderbook data enables detailed post-game reviews—compare final game results against the distribution of bid/ask prices throughout pre-game trading, identify which price ranges accumulated the most liquid orders (the market consensus), and measure how information (injury reports, weather changes) affected orderbook microstructure during the game.
Built for quantitative work on DateTime64(3) Snapshot Schema
Orderbook-level prediction-market data that doesn't exist anywhere else.
Detect sharp money movement before consensus shifts
Sharp professional bettors move prediction market orderbooks in distinctive ways—they accumulate large positions gradually (creating steep order accumulation at specific price levels) or strike suddenly when they find mispricing. Resolved Markets' continuous orderbook snapshots reveal these patterns through order clustering analysis and spread compression tracking. When you see sudden deepening of the buy side at a specific price, followed by gradual uptick in buy volume, that's often sharp accumulation—a signal to pay attention before the rest of the market reprices.
Quantify market confidence through bid/ask spread compression
Bid/ask spreads in prediction markets widen dramatically when market participants disagree about fair value. Resolved Markets captures spread width changes at millisecond intervals, revealing moments of high uncertainty and opportunity. Narrow spreads indicate consensus; suddenly widening spreads signal disagreement—often because new information is emerging. Track spread compression over hours before games as consensus hardens, identifying when markets have shifted from uncertain to confident about outcomes.
Track order accumulation patterns for directional signals
Unlike traditional betting lines where all liquidity pools at discrete points (7-point spread, 3-point spread), Polymarket orderbooks show continuous price levels with varying depth. Analyze where orders cluster to identify the market consensus—NBA game orderbooks might show dense selling at over 105.5 points and dense buying at under 104.5 points, indicating strong consensus around 105 points. Resolved Markets shows these order accumulation zones, revealing where market participants genuinely believe prices should settle.
Measure prediction market accuracy for sports outcome forecasting
Historical orderbook analysis enables post-game confidence calibration. Did markets price an outcome at 65% likelihood (odds of 1.85), but that outcome occurred in 70% of similar situations? Did early game orderbooks show lopsided depth on one side that disappeared as game progressed? Resolved Markets' historical snapshots let you analyze pre-game confidence distribution and measure whether prediction markets accurately reflected true event probabilities—critical for improving your forecasting.
How Sports Analysts use DateTime64(3) Snapshot Schema
Seven categories, hundreds of markets
Prediction markets across crypto, sports, economics, weather, and more — live and historical orderbook data, all queryable through one API.
Crypto
BTC, ETH, SOL, XRP — up/down markets every 5m to 1d.
Equities
S&P 500 (SPX) daily open — up or down predictions.
Social
Elon Musk tweet counts — weekly prediction ranges.
Sports
NBA, NFL, EPL — game outcomes and season predictions.
Economics
Fed decisions, jobs reports — FOMC meetings and macro data.
Weather
44 cities daily — temperature, hurricanes, Arctic ice.
Hyperliquid
BTC, ETH, SOL, XRP perp orderbooks — 1/sec sampling.
Tick-level orderbook snapshots
Every snapshot includes full bid/ask depth, mid prices, spreads, and crypto spot price.
| Side | Bid | Size | Ask | Size | Spread |
|---|---|---|---|---|---|
| UP | 0.5400 | 1,240 | 0.5500 | 1,100 | 1.00% |
| UP | 0.5300 | 980 | 0.5600 | 1,450 | 3.00% |
| UP | 0.5200 | 1,560 | 0.5700 | 890 | 5.00% |
| UP | 0.5100 | 2,100 | 0.5800 | 2,300 | 7.00% |
| UP | 0.5000 | 1,800 | 0.5900 | 1,700 | 9.00% |
| UP | 0.4900 | 3,200 | 0.6000 | 3,100 | 11.00% |
cryptoLowCardinality(String)BTCtimeframeLowCardinality(String)5mtoken_sideEnum8('UP','DOWN')UPtimestampDateTime64(3)2026-05-09 03:14:12.061crypto_priceFloat64$80,471.01best_bidFloat640.5400best_askFloat640.5500mid_priceFloat640.5450spreadFloat640.0100bidsArray(Tuple(F64,F64))[(0.54,1240),...]asksArray(Tuple(F64,F64))[(0.55,1100),...]Comprehensive market coverage
Prediction markets across multiple categories, captured continuously with high-frequency precision.
DateTime64(3) Snapshot Schema ships with
What Sports Analysts build with DateTime64(3) Snapshot Schema
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Three steps from signup to live DateTime64(3) Snapshot Schema in your application.
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Browse 11 endpoints with live examples. Test requests directly from the docs.
API ReferenceStart Building
Integrate live DateTime64(3) Snapshot Schema into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
curl -H 'X-API-Key: rm_xxx' 'https://api.resolvedmarkets.com/v1/markets/live' | jq '.[] | select(.category=="sports")'rm-api download --category sports --days 30Wiring DateTime64(3) Snapshot Schema into your workflow
Sports analysts pull DateTime64(3) Snapshot Schema via REST for pre-game analysis, WebSocket for live game monitoring, and the CLI for bulk season downloads. The MCP server lets AI agents flag unusual betting patterns automatically.
- Snowflake Snowpipe ingest for streaming DateTime64(3) Snapshot Schema
- AWS Glue catalog integration for DateTime64(3) Snapshot Schema Parquet files
- Databricks notebook starter kit for DateTime64(3) Snapshot Schema
Why Sports Analysts pick DateTime64(3) Snapshot Schema
- Real-time orderbook access reveals sharp money movement hours before public betting lines respond
- Full bid/ask depth arrays expose market consensus and liquidity clustering across NBA, NFL, and EPL markets
- Millisecond-precision timestamps enable precise correlation between order flow changes and external information (injury reports, weather, line news)
- Historical orderbook data supports post-game analysis and confidence calibration for sports prediction accuracy improvement
Why DateTime64(3) Snapshot Schema matters
DateTime64(3) Snapshot Schema matters for sports analytics because it captures the moment-to-moment evolution of best_bid, best_ask, mid_price, spread, bids[], asks[], not just final lines. Sports analysts get a microstructure-grade view of the same markets sportsbooks already model.
DateTime64(3) Snapshot Schema in context
Sports analytics is shifting from closing-line analysis to live orderbook study. DateTime64(3) Snapshot Schema fits that shift: continuous capture of best_bid, best_ask, mid_price, spread, bids[], asks[], DateTime64(3) timestamps with full bid/ask arrays, and integration that drops cleanly into existing analytics stacks.
Frequently asked: DateTime64(3) Snapshot Schema for Sports Analysts
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How does Polymarket's orderbook data for NFL/NBA games compare to traditional sportsbook lines?
Sportsbooks publish a single price (7-point spread, for example) representing their midpoint—you don't see bid/ask or order depth. Resolved Markets shows you the full orderbook, revealing bid/ask spreads (maybe 6.8 to 7.2), order volume at each price level, and how prices move as new orders arrive. This microstructure is invisible in traditional lines and often moves 1-2 hours before sportsbooks adjust their published spreads.
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Can I use orderbook data to predict game outcomes better than published lines?
Prediction market prices already reflect sophisticated betting, so they're statistically difficult to beat. However, orderbook microstructure often reveals mispricings or sharp money signals before prices fully adjust. Track when order depth suddenly increases on one side (suggesting informed money), or when bid/ask spreads compress (indicating emerging consensus). Combine orderbook analysis with your own game analysis to identify moments when markets are temporarily inefficient.
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Do you capture orderbooks for all sports markets, or only major leagues?
Resolved Markets captures 100+ sports markets including NBA, NFL, and EPL at high frequency during active trading periods (pre-game, halftime for ongoing games). We also track lower-profile sports and specific bet types (player props, quarter/half bets) when Polymarket offers them. WebSocket streams prioritize high-volume periods; query historical data for any tracked sport.
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What's the latency between an event (like an injury report) and the reflected change in prediction market orderbooks?
With millisecond-precision timestamps, Resolved Markets captures the exact timing of orderbook responses to external events. Injury reports typically trigger reordering within 10-60 seconds on Polymarket, visible as sudden spread widening and order volume shifts. Historical data lets you measure typical response latencies for different event types, revealing whether prediction markets are fast or slow to incorporate sports information.
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Can I export historical orderbooks for all games in a season for research?
Yes—the REST API supports batch queries for date ranges, teams, or sports categories. Export complete orderbook snapshots for an entire NFL season with bid/ask depth, timestamps, and order volumes. ClickHouse backend processes season-long queries quickly, and exported data includes metadata enabling game-level analysis (teams, game time, final score) for correlation studies.
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Can DateTime64(3) Snapshot Schema detect sharp money before lines move publicly?
Yes. Sharp money typically causes rapid spread compression and depth shifts at specific prices. DateTime64(3) timestamps with full bid/ask arrays surfaces these microstructure events inside DateTime64(3) Snapshot Schema, often hours before mainstream odds move.
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Can sports analysts compare DateTime64(3) Snapshot Schema to traditional sportsbook lines?
Yes — DateTime64(3) Snapshot Schema exposes implied probabilities directly from Polymarket orderbooks, which can be benchmarked against any sportsbook line. Historical replay enables backtesting of when prediction markets lead vs lag.
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How do sports analysts use DateTime64(3) Snapshot Schema?
Sports analysts watch bid-ask spreads inside DateTime64(3) Snapshot Schema to identify sharp versus public betting, depth shifts before major events, and market overreactions. DateTime64(3) timestamps with full bid/ask arrays reveals intraday momentum that closing lines hide.
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Is DateTime64(3) Snapshot Schema compatible with Apache Iceberg or Delta Lake?
Yes. Bulk Parquet exports of DateTime64(3) Snapshot Schema drop directly into Iceberg or Delta tables for time-travel queries and ACID semantics.
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Can I use DateTime64(3) Snapshot Schema with dbt?
Yes. Most teams build dbt models that consume DateTime64(3) Snapshot Schema via the ClickHouse connector and derive downstream features (spread, depth imbalance, mid-price velocity).