We extract fixtures, real-time match events, player lineups, commentary, and league standings from Livescore. 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 Matches objects from livescore.com. All fields typed and schema-versioned.
"match_id": "ls-match-98241", "competition_name": "Premier League", "home_team": "Arsenal", "away_team": "Chelsea", "status": "IN_PROGRESS", "match_minute": "67", "score_home": 2, "score_away": 1, "start_time_utc": "2026-08-15T14:00:00Z"
| # | match_id | competition_name | sport | home_team | away_team | status |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Match Events objects from livescore.com. All fields typed and schema-versioned.
"event_id": "evt-74921", "match_id": "ls-match-98241", "event_type": "GOAL", "minute": "42", "player_name": "Bukayo Saka", "team_name": "Arsenal", "assist_by": "Martin Odegaard", "var_review_result": "GOAL_CONFIRMED"
| # | event_id | match_id | event_type | minute | player_name | player_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Lineups & Formations objects from livescore.com. All fields typed and schema-versioned.
"match_id": "ls-match-98241", "team_name": "Arsenal", "formation": "4-3-3", "player_name": "Declan Rice", "position": "MID", "shirt_number": 41, "is_starter": true, "captain": false
| # | match_id | team_name | formation | player_id | player_name | position |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for League Standings objects from livescore.com. All fields typed and schema-versioned.
"competition_id": "comp-pl-26", "season": "2026/2027", "rank": 1, "team_name": "Arsenal", "played": 8, "won": 7, "drawn": 1, "lost": 0, "goal_difference": 14, "points": 22
| # | competition_id | season | rank | team_name | played | won |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Head-to-Head (H2H) objects from livescore.com. All fields typed and schema-versioned.
"team_a": "Arsenal", "team_b": "Chelsea", "total_matches": 68, "wins_team_a": 28, "wins_team_b": 22, "draws": 18, "goals_team_a": 94, "goals_team_b": 85, "last_match_date": "2026-03-14", "last_match_score": "1-1"
| # | team_a | team_b | total_matches | wins_team_a | wins_team_b | draws |
|---|---|---|---|---|---|---|
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Our Livescore scraper processes high-frequency sports data: real-time match events, historical fixtures, and league standings — with low-latency polling and timezone standardisation built in.
Capture live match scores, current minute, and status updates across football, tennis, basketball, cricket, and hockey fixtures.
Extract goals, cards, substitutions, and VAR decisions with exact minute markers and player attributions as they happen.
Retrieve starting XIs, substitutes, formations, and manager details before kickoff. Track injury updates and late changes.
Monitor live league tables, points, goal difference, and form guides across global competitions.
Extract previous encounter statistics, win probabilities, and historical performance metrics between opposing teams.
Unified schema for football, tennis, cricket, basketball, and hockey — normalising specific scoring systems into standard formats.
All fixture times and event timestamps are converted to strict UTC, resolving local time display variances.
Extract play-by-play text commentary for major fixtures, useful for NLP sentiment analysis and match summarisation.
Run one-off historical dumps or configure continuous real-time pipelines with sub-minute polling intervals.
Brief in. Clean data out.
Provide target leagues, sports, or specific match IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for livescore.com.
Schema validation, null-rate checks, event sequence verification, and latency testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sports data requires sub-second precision and handles heavy dynamic rendering. Here's how we maintain low-latency extraction without triggering rate limits.
Real-time sports data requires constant polling. We distribute requests across thousands of residential IPs to maintain sub-minute updates for live matches without triggering 429 Too Many Requests errors.
Livescore relies heavily on client-side rendering and WebSockets for live updates. We use Playwright to execute JavaScript and capture the hydrated state, ensuring no events are missed.
Fixture times vary based on the user's IP location. Our pipeline intercepts the client-side timezone offset logic and standardises all match times and event timestamps to UTC before delivery.
Live sports data often features corrected events (e.g., a disallowed goal). We maintain state for each match and emit clean diffs or updated event records, preventing duplicate entries in your warehouse.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, stale match clocks, and schema drift — and respond before you notice. SLA uptime is contractual, not aspirational.
Data scientists build predictive models using historical H2H data, lineup configurations, and match event timelines.
Fantasy applications consume real-time player stats, goals, and assists to update user points and league standings instantly.
Sports portals automate match reports, live ticker updates, and post-match analysis using structured event data.
Platforms monitor live match states (score, time, red cards) to correlate with betting odds movements across exchanges.
Mobile applications trigger push notifications for goals, half-time scores, and full-time results based on our webhook feeds.
Analysts query years of fixture results and league tables to identify trends, team form, and managerial impact over time.
"Livescore processes millions of match events globally, but extracting that high-frequency data requires infrastructure built for sub-second polling."
Most teams underestimate the compute required for real-time sports scraping: managing concurrent polling, normalising timezones, and parsing dynamic DOM updates without IP bans. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our livescore.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 interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. 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 livescore.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated match statistics, scores, and fixtures. We do not extract personal data or circumvent DRM for video content. Clients should review Livescore's ToS and consult legal counsel for specific use cases.
We distribute high-frequency requests across a large pool of residential proxies to avoid rate limits. Our infrastructure can maintain sub-minute polling intervals for active matches, delivering updates via Webhook for minimal latency.
Our pipeline supports all major sports covered by Livescore, including football (soccer), tennis, basketball, cricket, and hockey. Each sport has a normalised schema to account for different scoring systems (e.g., sets in tennis vs quarters in basketball).
For live matches, data is extracted and delivered within seconds of appearing on the site. Historical data and fixture lists are typically refreshed on a daily or weekly schedule depending on your requirements.
Yes. We can configure pipelines to crawl historical seasons, extracting past match results, lineups, and final league standings based on the archives available on Livescore.
Our packages start with tracking specific leagues or competitions with daily delivery. For real-time webhook feeds covering global fixtures, we price based on request volume and concurrency requirements. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical database of match results or a real-time webhook feed for live fixtures — we scope, build, and operate the pipeline. Tell us what you need.
