We extract fixtures, live odds comparisons, historical results, league tables, and H2H statistics from Betexplorer. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Match Fixtures objects from betexplorer.com. All fields typed and schema-versioned.
"match_id": "bx_1048291", "sport": "soccer", "country": "England", "league": "Premier League", "home_team": "Arsenal", "away_team": "Chelsea", "kickoff_time": "2026-04-18T14:00:00Z", "status": "finished", "score": "2:1"
| # | match_id | sport | country | league | home_team | away_team |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Odds Comparison objects from betexplorer.com. All fields typed and schema-versioned.
"match_id": "bx_1048291", "bookmaker": "bet365", "market_type": "1X2", "odd_1": 2.1, "odd_x": 3.4, "odd_2": 3.5, "payout_pct": 95.2, "timestamp": "2026-04-18T13:45:00Z"
| # | match_id | bookmaker | market_type | odd_1 | odd_x | odd_2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for League Tables objects from betexplorer.com. All fields typed and schema-versioned.
"league_id": "eng_pl_2026", "rank": 1, "team": "Arsenal", "matches_played": 32, "wins": 24, "draws": 5, "losses": 3, "points": 77, "form": "['W', 'W', 'D', 'W', 'L']"
| # | league_id | rank | team | matches_played | wins | draws |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for H2H Statistics objects from betexplorer.com. All fields typed and schema-versioned.
"team_1": "Arsenal", "team_2": "Chelsea", "total_matches": 68, "team_1_wins": 28, "draws": 20, "team_2_wins": 20, "team_1_goals": 94, "team_2_goals": 82, "last_match_date": "2025-10-21T16:30:00Z"
| # | team_1 | team_2 | total_matches | team_1_wins | draws | team_2_wins |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Streaks & Trends objects from betexplorer.com. All fields typed and schema-versioned.
"team_name": "Arsenal", "streak_type": "win", "streak_length": 4, "league": "Premier League", "next_match_id": "bx_1048305", "next_opponent": "Tottenham", "average_goals_scored": 2.4, "average_goals_conceded": 0.8
| # | team_name | streak_type | streak_length | league | next_match_id | next_opponent |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Betexplorer scraper handles every layer of the platform: match fixtures, dynamic odds grids, historical archives, and H2H statistics — with JavaScript rendering, timezone normalisation, and anti-bot circumvention built in.
Kickoff times, scores, match status, and detailed result lines across 7 sports including soccer, tennis, and basketball.
1X2, Over/Under, Asian Handicap, and BTTS markets extracted across 40+ tracked bookmakers with payout percentages.
Extract historical match outcomes and closing odds dating back to 2004 for comprehensive backtesting datasets.
Overall, home, and away tables with recent form guides, points, and goal differences updated after every match.
Mutual encounter histories, previous scores, and historical odds for specific matchups to inform predictive models.
Winning, drawing, losing, and over/under streaks for teams across all active leagues globally.
All kickoff times standardised to UTC, eliminating local timezone offset errors and ensuring dataset consistency.
Normalised bookmaker identifiers across different markets and odds formats for clean cross-referencing.
Run daily historical dumps or high-frequency pre-match odds updates with change-detection diffing.
Brief in. Clean data out.
Provide target leagues, sports, or historical date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and rate-limit handling for betexplorer.com.
Schema validation, timezone checks, odds-outlier detection, and sample matches before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sports data sites limit high-frequency requests to protect their odds data. Here's how we stay resilient — and why quants choose managed infrastructure over DIY.
Betexplorer limits high-frequency requests to block arbitrage scrapers. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access without IP bans.
Many odds comparisons and historical grids load dynamically via asynchronous requests. We run full Playwright browser sessions to capture data that standard headless HTTP clients miss entirely.
Sports data is inherently messy. We maintain mapping tables to ensure team names, league identifiers, and bookmaker names remain consistent across seasons and different sections of the site.
Betexplorer adjusts display times based on the user's IP address. We force strict UTC extraction at the crawler level to prevent offset errors in your downstream predictive models.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing odds, schema drift, and coverage drops — and respond before you notice.
Quants and data scientists use historical odds and match results to train predictive betting models and machine learning algorithms.
Syndicates monitor cross-bookmaker odds discrepancies to identify arbitrage opportunities pre-match across global markets.
Bettors compare current odds against historical closing lines and implied probabilities to identify positive expected value.
Analysts track bookmaker payout percentages, margin changes, and odds movement patterns over time to evaluate market positioning.
Publishers populate automated match previews, form guides, and statistical insights using structured data feeds.
Researchers study market efficiency by tracking how odds shift from opening lines to closing lines in response to market volume.
"Betexplorer holds two decades of historical odds and match data — the foundational dataset for any serious sports predictive model."
Most teams underestimate the investment required: reliable sports data scraping requires residential proxies, strict timezone normalisation, dynamic odds rendering, and anomaly monitoring. DataFlirt absorbs that complexity so your quants can focus on the models — not the infrastructure.
Everything supported by our betexplorer.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering for dynamic odds grids. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request to bypass rate limits. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About betexplorer.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports fixtures, historical results, and odds data is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or circumvent authentication walls. Clients should review Betexplorer's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate-limit spikes in real time and trigger pool rotation automatically.
We support extraction across all sports listed on Betexplorer, including Soccer, Tennis, Basketball, Hockey, Volleyball, Handball, and Baseball.
For major soccer leagues and prominent sports, historical match results and closing odds data typically stretch back to 2004. Coverage depends on Betexplorer's internal archives.
Pre-match odds can be updated at configured intervals ranging from daily to hourly. We do not support live, tick-by-tick in-play odds extraction from this source.
All kickoff times and timestamps are strictly converted to UTC at the crawler level. This ensures absolute consistency regardless of the proxy IP location used for the request.
Absolutely. We provide a sample run of up to 500 matches as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical archive dump or a continuous odds-monitoring feed across global leagues — we scope, build, and operate the pipeline. Tell us what you need.
