We extract live match events, player statistics, historical results, league standings, and betting odds from Flashscore. 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 Live Match Data objects from flashscore.com. All fields typed and schema-versioned.
"match_id": "gA1b2C3d", "sport": "Football", "tournament": "Premier League", "home_team": "Arsenal", "away_team": "Chelsea", "current_score": "2-1", "match_time": "74", "match_status": "LIVE"
| # | match_id | sport | tournament | home_team | away_team | current_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Match Statistics objects from flashscore.com. All fields typed and schema-versioned.
"match_id": "gA1b2C3d", "team": "Arsenal", "ball_possession": "58%", "goal_attempts": 14, "shots_on_goal": 6, "corner_kicks": 8, "fouls": 9, "yellow_cards": 2
| # | match_id | team | ball_possession | goal_attempts | shots_on_goal | shots_off_goal |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lineups & Formations objects from flashscore.com. All fields typed and schema-versioned.
"match_id": "gA1b2C3d", "team": "Arsenal", "formation": "4-3-3", "manager": "Mikel Arteta", "player_name": "Bukayo Saka", "position": "Forward", "shirt_number": 7, "starting_xi": true
| # | match_id | team | formation | starting_xi | substitutes | manager |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for H2H & Past Results objects from flashscore.com. All fields typed and schema-versioned.
"match_id": "hX9y8Z7w", "team_a": "Arsenal", "team_b": "Chelsea", "date": "2023-10-21", "home_score": 2, "away_score": 2, "winner": "Draw", "tournament": "Premier League"
| # | match_id | team_a | team_b | date | tournament | home_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Betting Odds objects from flashscore.com. All fields typed and schema-versioned.
"match_id": "gA1b2C3d", "bookmaker": "bet365", "market_type": "1X2", "odd_1": 2.1, "odd_x": 3.4, "odd_2": 3.5, "movement_indicator": "down", "timestamp": "2026-05-12T14:30:00Z"
| # | match_id | bookmaker | market_type | odd_1 | odd_x | odd_2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our Flashscore scraper handles every layer of the platform: live score websockets, dynamic odds updates, historical match data, and player statistics, with anti-bot circumvention built in.
Real-time updates for goals, points, sets, and match status across all supported sports, driven by websocket interception.
Extract ball possession, shots, fouls, cards, and corner kicks at the team and player level.
Capture 1X2, Over/Under, and Asian Handicap markets from integrated bookmakers, timestamped per change.
Historical match results, direct head-to-head records, and team form guides across all competitions.
Starting XIs, substitutes, tactical formations, missing players, and manager details prior to kickoff.
Overall, home, away, and form tables. Tournament bracket progression for knockout stages.
Football, tennis, basketball, ice hockey, cricket, and 30 other sports normalised into a unified schema.
HTTP POST callbacks triggered instantly on goal events, red cards, or significant odds movements.
Deep crawls of past seasons to build comprehensive training datasets for predictive modelling.
Player transfer history, loan agreements, and contract dates linked to player profiles.
Brief in. Clean data out.
Provide tournament lists, team IDs, or specific date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for flashscore.com.
Schema validation, null-rate checks, event latency testing, and data normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Flashscore relies heavily on obfuscated data structures and rapid state changes. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Flashscore live updates do not use standard HTTP polling. We intercept and decode the proprietary websocket payloads directly, ensuring sub-second latency for critical match events and score changes.
Odds tables, historical H2H data, and detailed statistics are heavily JavaScript-rendered. We run full Playwright browser sessions to hydrate these components, capturing data that headless HTTP clients miss entirely.
Flashscore employs aggressive rate limiting and bot detection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Flashscore frequently changes its internal match and player ID generation logic. Our extraction engine maps these obfuscated values back to stable, canonical identifiers to maintain relational integrity in your database.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, websocket disconnects, schema drift, and coverage drops. SLA uptime is contractual, not aspirational.
Quantitative syndicates use historical results, detailed match statistics, and odds movement data to train predictive models.
Operators ingest live player statistics, starting lineups, and injury reports to update fantasy point scoring in real time.
Digital publishers populate match centers, live blogs, and automated match reports using our low-latency JSON feeds.
Affiliates aggregate pre-match and live odds across multiple bookmakers to highlight arbitrage opportunities and value bets.
Professional clubs analyse opponent form, tactical formations, and statistical trends across entire league seasons.
High-frequency betting algorithms trigger automated trades based on specific in-play events like red cards or severe odds shifts.
"Flashscore holds the most comprehensive live sports data globally, but accessing it requires reverse-engineering complex websockets and dynamic payloads."
Most teams underestimate the investment required: reliable Flashscore scraping requires websocket interception, full JavaScript rendering, proxy rotation, and millisecond-level anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our flashscore.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, cookie sessions, and websocket interception. Combined via custom middleware.
We maintain pools of residential ISP proxies across EU and US regions. Rotation happens per-request with sticky sessions where required to maintain websocket stability.
Pipelines run on AWS Lambda for burst loads and ECS for sustained websocket connections. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About flashscore.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual sports data (scores, statistics, historical results) is generally permissible under applicable law, as raw facts are not subject to copyright. DataFlirt targets only public, non-authenticated match and odds data. Clients should review Flashscore's ToS and consult legal counsel for specific commercial use cases.
We bypass standard HTTP polling and intercept Flashscore's proprietary websocket connections directly. This allows us to receive push events for goals, cards, and points the millisecond they are broadcast to the browser.
Yes. We can run deep historical crawls to extract past match results, detailed statistics, and final league standings across decades of available archive data to build training sets.
Flashscore frequently updates its DOM structure and obfuscates internal IDs. Our selector strategy uses multiple fallback chains, and our extraction engine maps obfuscated values back to canonical identifiers to ensure schema stability.
We support all 30+ sports listed on Flashscore, including football, tennis, basketball, ice hockey, cricket, rugby, and esports. The data is normalised into a unified schema where applicable.
Yes. We extract pre-match and live in-play odds across major markets (1X2, Over/Under, Asian Handicap) from the bookmakers integrated into Flashscore's interface, complete with movement indicators.
For active live matches, our webhook delivery achieves sub-second latency from the moment the event appears on Flashscore's interface. Batch deliveries for completed matches can be scheduled at any frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical match archive or a sub-second live score feed across 30 sports, we scope, build, and operate the pipeline. Tell us what you need.
