We extract player market values, transfer histories, club squads, injury records, and agent portfolios from Transfermarkt. 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 Player Profiles objects from transfermarkt.com. All fields typed and schema-versioned.
"player_id": "28003", "name": "Lionel Messi", "age": 36, "position": "Right Winger", "market_value": 35000000, "current_club": "Inter Miami CF", "citizenship": "Argentina"
| # | player_id | name | full_name | date_of_birth | place_of_birth | age |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Transfer History objects from transfermarkt.com. All fields typed and schema-versioned.
"transfer_id": "3489102", "player_id": "28003", "season": "23/24", "date": "2023-07-15", "left_club": "Paris SG", "joined_club": "Inter Miami CF", "transfer_fee": "Free transfer"
| # | transfer_id | player_id | season | date | left_club | left_club_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Club Squads objects from transfermarkt.com. All fields typed and schema-versioned.
"club_id": "27", "club_name": "Bayern Munich", "league": "Bundesliga", "squad_size": 26, "average_age": 26.5, "total_market_value": 929000000, "stadium_name": "Allianz Arena"
| # | club_id | club_name | league | season | squad_size | average_age |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Match Statistics objects from transfermarkt.com. All fields typed and schema-versioned.
"match_id": "4081234", "competition": "Premier League", "date": "2024-02-10", "home_team": "Arsenal", "away_team": "Liverpool", "home_goals": 3, "away_goals": 1
| # | match_id | competition | date | home_team | home_team_id | away_team |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agent Portfolios objects from transfermarkt.com. All fields typed and schema-versioned.
"agent_id": "1234", "agency_name": "Gestifute", "country": "Portugal", "total_players": 142, "total_market_value": 1250000000, "average_market_value": 8800000, "website": "www.gestifute.com"
| # | agent_id | agency_name | legal_form | address | city | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Transfermarkt scraper captures every layer of the database: player metrics, financial histories, match logs, and agent details, with rate-limit circumvention built in.
Extract current and historical market values, charting the financial trajectory of players across their entire careers.
Capture transfer fees, loan agreements, free transfers, and sell-on clauses across all global leagues.
Analyse squad composition, average age, foreign player quotas, and aggregate market values for any club.
Extract line-ups, substitutions, goals, assists, and disciplinary records from historical and current matches.
Map player-agency relationships, agency market share, and total portfolio valuations.
Track player availability, injury types, and days missed to model durability and risk.
Scrape transfer rumours, probability percentages, and source tracking for predictive modelling.
Aggregate league standings, top scorers, and disciplinary tables across multiple tiers.
Run continuous pipelines to capture value updates and squad changes without re-scraping static historical data.
Brief in. Clean data out.
Provide leagues, clubs, or player sets. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for transfermarkt.com.
Schema validation, null-rate checks, and data type normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or warehouse on agreed cadence.
Transfermarkt employs strict rate limiting and structural complexities. Here is how we maintain stable extraction.
Transfermarkt blocks data centre IPs aggressively. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid rate limits.
Transfermarkt's DOM relies heavily on nested tables and fragmented data structures. We use precise XPath selectors to normalise this into flat, queryable records.
For massive player catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops.
Historical match data and transfer records span thousands of paginated views. Our orchestrator ensures complete traversal without missing records.
Football clubs and scouting departments track player valuations, contract expiries, and performance metrics to identify targets.
Analysts use historical transfer fees and market values to model club asset depreciation and squad equity.
Syndicates ingest match histories, injury reports, and referee statistics to train predictive models.
Agencies monitor competitor portfolios, client values, and contract end dates for acquisition strategies.
Sports publishers automate data graphics and contextual statistics for match previews and transfer deadline day coverage.
Gaming studios extract baseline squad data, player traits, and historical records to populate simulation databases.
"Transfermarkt holds the definitive financial and performance record of world football, but extracting it at scale requires navigating complex pagination and strict rate limits."
Most teams underestimate the investment required: reliable Transfermarkt scraping requires residential proxies, handling nested table structures, Cloudflare circumvention, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our transfermarkt.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 and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. 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 transfermarkt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual data, such as match statistics and transfer fees, is generally permissible. DataFlirt targets only public, non-authenticated data and respects rate limits to avoid infrastructure disruption.
We use residential ISP proxies and request timing modelled on human behaviour. We monitor for 429 response codes in real time and trigger pool rotation automatically.
We cover all major global leagues including the Premier League, La Liga, Serie A, Bundesliga, Ligue 1, MLS, and lower-tier divisions globally.
Pipelines can be configured for daily runs to capture overnight market value updates and transfer confirmations.
Yes. We extract the complete historical valuation chart for every player profile, providing a time-series view of their market worth.
Our packages start at defined league or club lists with weekly delivery. For full global database extraction, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off squad export or continuous transfer monitoring across 50 leagues, we scope, build, and operate the pipeline. Tell us what you need.
