We extract live match events, Sofascore statistical ratings, attack momentum graphs, lineups, and historical player data. Delivered as clean JSON, Parquet, or via Webhook to your infrastructure 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 sofascore.com. All fields typed and schema-versioned.
"match_id": "11352481", "home_team": "Arsenal", "away_team": "Liverpool", "status": "inprogress", "current_minute": 67, "home_score": 1, "away_score": 1, "event_type": "goal", "player_name": "Bukayo Saka"
| # | match_id | tournament_name | home_team | away_team | status | current_minute |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Player Statistics objects from sofascore.com. All fields typed and schema-versioned.
"player_id": "83415", "player_name": "Martin Ødegaard", "sofascore_rating": 8.2, "minutes_played": 90, "goals": 0, "assists": 1, "accurate_passes": 45, "pass_completion_pct": 88.2
| # | player_id | player_name | team_name | position | sofascore_rating | minutes_played |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Team Standings objects from sofascore.com. All fields typed and schema-versioned.
"tournament_name": "Premier League", "season": "23/24", "rank": 1, "team_name": "Manchester City", "matches_played": 38, "points": 91, "goal_difference": 62, "form_last_5": "['W', 'W', 'W', 'W', 'W']"
| # | tournament_id | tournament_name | season | rank | team_name | matches_played |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Match Lineups objects from sofascore.com. All fields typed and schema-versioned.
"match_id": "11352481", "team_name": "Arsenal", "formation": "4-3-3", "manager_name": "Mikel Arteta", "starting_xi_names": "['Raya', 'White', 'Saliba', 'Gabriel', 'Zinchenko', 'Rice', 'Ødegaard', 'Havertz', 'Saka', 'Martinelli', 'Jesus']", "average_age": 25.4
| # | match_id | team_name | formation | manager_name | starting_xi_ids | starting_xi_names |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Odds & Predictions objects from sofascore.com. All fields typed and schema-versioned.
"match_id": "11352481", "provider_name": "bet365", "market_type": "1X2", "home_odds": 2.45, "draw_odds": 3.4, "away_odds": 2.8, "dropping_odds_flag": true, "community_votes_home": 4512
| # | match_id | provider_name | market_type | home_odds | draw_odds | away_odds |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Sofascore aggregates massive volumes of live and historical sports statistics. We extract the underlying JSON payloads and WebSocket streams to deliver structured data at millisecond latency.
Extract goals, cards, substitutions, and VAR decisions in real-time. Delivered via webhooks for instant downstream processing.
Capture the proprietary 10-point player ratings updated live during matches, alongside the 300+ stats used to calculate them.
Parse the visual Attack Momentum graphs into structured time-series data to quantify match dominance per minute.
Extract spatial coordinate data for player heatmaps and shot locations, mapped to standard pitch dimensions.
Track pre-match and in-play odds fluctuations across major bookmakers. Identify market shifts and dropping odds indicators.
Aggregate historical match data between teams, including past results, average goals, and streak statistics.
Extract starting XIs, bench players, formations, and manager data as soon as they are announced pre-match.
Track player transfer values, contract durations, and historical transfer fees across all major leagues.
Extract data across football, tennis, basketball, ice hockey, cricket, and 15+ other sports supported by Sofascore.
Brief in. Clean data out.
Specify sports, leagues, match IDs, or player profiles. We design the extraction schema for historical or live data.
We configure interceptors for Sofascore's internal APIs and WebSockets, handling token rotation and rate limits.
Schema validation, null-rate checks, and latency testing to ensure live events arrive within SLA.
JSON / Parquet pushed to your S3 bucket, or real-time Webhook POSTs for live match events.
Live sports data requires low-latency infrastructure. Here is how we maintain stable pipelines against dynamic rate limits and geographic blocks.
Instead of parsing HTML, we intercept the raw JSON payloads from Sofascore's internal APIs. This guarantees exact data types, zero parsing errors, and significantly lower latency for historical data.
Live matches rely on WebSocket connections. We maintain persistent WebSocket streams across thousands of concurrent matches, parsing binary or compressed frames into structured JSON events instantly.
Sofascore aggressively limits requests per IP. We distribute API calls across a massive pool of residential and mobile proxies, rotating headers and session tokens to prevent IP bans and HTTP 429 errors.
Certain odds providers and streaming links are geo-restricted. We route requests through region-specific proxy nodes to capture localised odds and ensure complete data coverage regardless of origin.
Sofascore's JSON structures vary wildly between football, tennis, and basketball. We normalise these disparate payloads into a consistent, predictable schema for your data warehouse.
Quantitative syndicates ingest historical player stats, attack momentum, and dropping odds to train predictive pricing models.
DFS operators use Sofascore ratings and live event data to score players and settle contests in real-time.
Publishers automate match reports, player comparison graphics, and statistical deep-dives using structured historical data.
Coaching staff and scouts analyse heatmaps, pass completion rates, and H2H data for tactical preparation.
Affiliate sites track pre-match and live odds across multiple bookmakers to highlight arbitrage opportunities.
Mobile applications integrate live scores, standings, and transfer rumours to keep users engaged during and between matches.
"Sofascore aggregates the most granular statistical ratings and live momentum data in sports — but accessing it programmatically requires reverse-engineering their internal streams."
Building a reliable sports data pipeline is a latency game. Extracting live events via WebSockets while managing token rotation and proxy bans requires dedicated infrastructure. DataFlirt handles the extraction complexity, delivering clean JSON via webhooks so your models receive data the millisecond a goal is scored.
Everything supported by our sofascore.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.
Pipelines utilise Python's asyncio to maintain thousands of concurrent WebSocket connections, ensuring low-latency processing of live match events.
Requests are routed through residential IPs in specific geographic zones to bypass regional blocks on odds providers and ensure consistent API access.
Live events are pushed via AWS Lambda-backed webhooks, while historical batch runs are processed on Kubernetes clusters and written directly to Parquet on S3.
Data delivered to where your team already works — no new tooling required.
About sofascore.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports statistics is generally permissible. DataFlirt extracts only public match data, ratings, and odds. We do not bypass authentication walls or extract personal user data. Clients should review terms of service and consult legal counsel regarding commercial use of sports data.
By intercepting WebSocket streams, live events (goals, cards, odds changes) are captured and pushed via webhook within 50-150 milliseconds of appearing on the Sofascore platform.
Yes. We can iterate through tournament archives to extract historical match results, lineups, player statistics, and closing odds for past seasons.
Yes. We extract the underlying numerical arrays that generate the visual Attack Momentum graphs, providing you with a minute-by-minute integer value representing team dominance.
We distribute requests across a large pool of residential proxies and rotate session tokens dynamically. This prevents HTTP 429 Too Many Requests errors and ensures continuous data extraction.
We can extract data for any sport covered by Sofascore, including football, tennis, basketball, ice hockey, volleyball, handball, esports, and cricket.
Yes. For historical bulk extractions, we deliver highly compressed Parquet files directly to your AWS S3 bucket, Google Cloud Storage, or Snowflake stage.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical database of player statistics or a live webhook feed for in-play betting models — we build and operate the infrastructure. Tell us your requirements.
