We extract real-time match events, player statistics, league standings, and historical head-to-head records from 365Scores. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or via Webhook 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 Events objects from 365scores.com. All fields typed and schema-versioned.
"match_id": "365-match-98274", "home_team": "Arsenal", "away_team": "Chelsea", "status": "In Play", "minute": 67, "home_score": 2, "away_score": 1, "possession_home": 58.4
| # | match_id | tournament | home_team | away_team | status | minute |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Player Statistics objects from 365scores.com. All fields typed and schema-versioned.
"player_id": "p-48291", "name": "Bukayo Saka", "team": "Arsenal", "position": "Midfielder", "matches_played": 28, "goals": 14, "assists": 8, "rating": 8.2
| # | player_id | name | team | nationality | position | age |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for League Standings objects from 365scores.com. All fields typed and schema-versioned.
"team": "Liverpool", "rank": 1, "points": 64, "matches_played": 28, "wins": 19, "draws": 7, "goal_difference": 39, "form_last_5": "['W', 'W', 'D', 'W', 'L']"
| # | tournament_id | season | team | rank | points | matches_played |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Team Lineups objects from 365scores.com. All fields typed and schema-versioned.
"match_id": "365-match-98274", "team": "Arsenal", "formation": "4-3-3", "manager": "Mikel Arteta", "average_age": 24.8, "starting_xi": "['Raya', 'White', 'Saliba', 'Gabriel', 'Kiwior', 'Rice', 'Odegaard', 'Havertz', 'Saka', 'Martinelli', 'Trossard']"
| # | match_id | team | formation | manager | starting_xi | substitutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Head-to-Head objects from 365scores.com. All fields typed and schema-versioned.
"team_a": "Arsenal", "team_b": "Chelsea", "total_matches": 174, "team_a_wins": 69, "team_b_wins": 54, "draws": 51, "team_a_goals": 248, "last_match_date": "2023-10-21"
| # | team_a | team_b | total_matches | team_a_wins | team_b_wins | draws |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our 365Scores scraper handles real-time data streams and complex sports schemas. We bypass rate limits, intercept WebSockets, and normalise data across different sports into a predictable format.
Extract real-time updates without polling delays by intercepting WebSocket streams directly from 365Scores servers.
Capture goals, VAR decisions, cards, and substitutions with exact minute markers and player attribution.
Aggregate historical performance metrics across multiple tournaments, including ratings and injury histories.
Parse graphical formation data into structured arrays containing starting XI, substitutes, and tactical setups.
Capture live updating standings during matchdays, tracking rank shifts and goal difference changes in real time.
Extract historical win rates, draw frequencies, and goal averages between two specific sides.
Monitor roster availability and absence records before kickoff to inform predictive models.
Capture moneyline, over/under, and handicap shifts from integrated betting partners displayed on 365Scores.
Football, basketball, tennis, cricket, and rugby data extracted into a unified, predictable schema.
Sub-second diffing for clients needing near-live latency on matchday events and scoreline changes.
Brief in. Clean data out.
Provide tournament IDs, team lists, or specific sports categories. We design the extraction schema together.
We configure Scrapy crawlers, WebSocket interceptors, proxy rotation, and session management for 365scores.com.
Schema validation, null-rate checks, and latency testing during live matchdays before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or via Webhook on agreed cadence.
Live sports platforms invest heavily in rate limiting and connection dropping. Here is how we maintain zero-latency extraction during peak traffic.
Polling HTML during a live match introduces latency and triggers rate limits. We intercept the underlying WebSocket connections and XHR streams that power the 365Scores frontend, capturing events the millisecond they are broadcast.
Football statistics look entirely different from tennis or basketball. Our pipeline maps sport-specific events into a unified, predictable schema, ensuring your downstream models do not break when switching between sports.
Certain leagues and betting odds are restricted by region. We route requests through residential proxy pools located in the target territory, ensuring complete data coverage regardless of regional broadcasting rights.
During major tournaments like the World Cup, 365Scores aggressively throttles IPs. We distribute request loads across thousands of residential IPs, rotating automatically upon detecting latency degradation or HTTP 429 errors.
Live sports data is chaotic. Referees reverse decisions and goals are disallowed. Our change-detection engine tracks state changes and emits correction events, ensuring your database reflects the final verified match state.
Quants and bookmakers ingest live events and historical head-to-head data to adjust in-play odds and train pricing models.
Fantasy operators use player statistics, lineups, and injury reports to score user teams and update player valuations.
Digital publishers populate live score widgets, match centres, and automated match reports using structured event feeds.
Trading syndicates execute automated bets on sports exchanges based on sub-second match event triggers.
Scouting departments aggregate player season statistics across obscure leagues to identify undervalued talent.
Mobile applications send push notifications for goals, cards, and full-time results based on our webhook triggers.
"365Scores aggregates millions of live data points across global sports leagues every minute, but accessing that stream programmatically requires bypassing heavy rate limits and complex WebSocket architectures."
Extracting live sports data at scale means maintaining sub-second latency while avoiding IP bans during peak matchdays. DataFlirt handles the proxy rotation, WebSocket interception, and schema normalisation across different sports so your engineering team can focus on building predictive models, not maintaining scrapers.
Everything supported by our 365scores.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.
Playwright intercepts raw JSON payloads from internal APIs and WebSocket connections, bypassing HTML parsing entirely for live events.
Kubernetes clusters auto-scale worker nodes based on the concurrent live match schedule, ensuring latency remains stable during weekend peaks.
Custom Python middleware maps disparate sport data structures into strict Pydantic models, rejecting malformed records before delivery.
Data delivered to where your team already works — no new tooling required.
About 365scores.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports statistics and match results is generally permissible. Factual data like scores and player statistics cannot be copyrighted. DataFlirt extracts only public, non-authenticated data. Clients should review 365Scores' ToS and consult legal counsel for their specific use cases.
By intercepting WebSockets and internal XHR requests, we achieve sub-second latency from the moment an event is broadcast on the 365Scores platform to the moment it hits your webhook endpoint.
We support all sports covered by 365Scores, including football, basketball, tennis, cricket, rugby, baseball, American football, and ice hockey. All data is normalised into sport-specific schemas.
Our Kubernetes clusters automatically scale worker nodes based on the volume of concurrent live matches. We maintain large residential proxy pools to distribute requests and avoid IP bans during high-traffic events like the World Cup.
Yes. We can configure backfill pipelines to scrape past seasons, historical head-to-head records, and historical player statistics across any supported tournament.
We extract the pre-match and in-play odds displayed on 365Scores from their integrated betting partners. Note that odds availability may vary based on the geo-location of the proxy used.
We use strict Pydantic models to define schemas for each sport. A football match record will contain fields for corners and yellow cards, while a basketball record will contain fields for rebounds and three-pointers. The core match metadata remains consistent.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need historical player statistics or sub-second live match events across 50 leagues — we scope, build, and operate the pipeline. Tell us what you need.
