We extract match results, league tables, form guides, goal timing stats, and H2H records from Soccerstats. 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 Match Results objects from soccerstats.com. All fields typed and schema-versioned.
"match_id": "eng_pr_2023_114", "date": "2023-10-21", "home_team": "Arsenal", "away_team": "Chelsea", "full_time_home_goals": 2, "full_time_away_goals": 2, "half_time_home_goals": 0, "half_time_away_goals": 1
| # | match_id | date | league | home_team | away_team | full_time_home_goals |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for League Tables objects from soccerstats.com. All fields typed and schema-versioned.
"rank": 1, "team_name": "Manchester City", "matches_played": 38, "wins": 28, "draws": 7, "losses": 3, "goal_difference": 62, "points": 91, "form_last_6": "WWWWWW"
| # | league_id | season | rank | team_name | matches_played | wins |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Goal Timing objects from soccerstats.com. All fields typed and schema-versioned.
"team_name": "Liverpool", "total_goals_scored": 84, "goals_0_15": 12, "goals_16_30": 14, "goals_76_90": 22, "late_goals_percentage": 26.2
| # | team_name | league | total_goals_scored | goals_0_15 | goals_16_30 | goals_31_45 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Head-to-Head objects from soccerstats.com. All fields typed and schema-versioned.
"team_a": "Real Madrid", "team_b": "Barcelona", "total_matches": 254, "team_a_wins": 103, "draws": 52, "team_b_wins": 99, "last_meeting_date": "2023-10-28", "last_meeting_result": "1-2"
| # | team_a | team_b | total_matches | team_a_wins | draws | team_b_wins |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Over/Under Stats objects from soccerstats.com. All fields typed and schema-versioned.
"team_name": "Bayern Munich", "matches_played": 34, "over_1_5_pct": 94.1, "over_2_5_pct": 82.4, "over_3_5_pct": 58.8, "btts_pct": 61.8, "clean_sheet_pct": 32.4
| # | team_name | matches_played | over_0_5_pct | over_1_5_pct | over_2_5_pct | over_3_5_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Soccerstats contains a wealth of data trapped in legacy HTML table structures. We handle the complex DOM traversal, team name normalisation, and historical archiving.
Extract overall, home, and away league tables, alongside rolling 6-match and 8-match form guides for every team.
Capture full-time and half-time scores, match dates, and venue details across thousands of historical fixtures.
Extract 15-minute interval breakdowns for goals scored and conceded, enabling deep in-play probability modelling.
Pull percentage frequencies for Over 1.5, 2.5, and 3.5 goals, plus Both Teams To Score (BTTS) statistics.
Scrape historical matchups between specific teams, including aggregate goals, win distributions, and recent meeting results.
Isolate team performance metrics based on venue, capturing the statistical impact of home advantage.
Extract cards per game, fouls awarded, and penalty frequencies broken down by individual match officials.
Our parsers navigate deeply nested, classless table structures to extract reliable data without schema breakage.
Configure daily or weekly pipelines to capture weekend fixture results and updated league standings automatically.
Brief in. Clean data out.
Provide the target leagues, seasons, and statistical categories. We design the relational extraction schema.
We configure Scrapy crawlers with custom lxml parsers to navigate the nested table structures of soccerstats.com.
Schema validation, team name normalisation checks, and data type enforcement before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Soccerstats is a data goldmine built on older web technologies. Here is how we extract clean data from complex DOMs.
Soccerstats relies heavily on nested HTML tables without semantic class names or IDs. We use custom XPath and lxml parsers that rely on structural hierarchy rather than fragile CSS selectors, ensuring stable extraction.
Team names often vary between pages (e.g., 'Man Utd' vs 'Manchester United'). Our pipeline includes a normalisation layer that maps all variations to a canonical UUID, ensuring clean joins in your database.
Navigation relies on complex URL query parameters rather than RESTful paths. We map the entire parameter space for target leagues and seasons, ensuring complete coverage of historical archives without missing fixtures.
To prevent IP bans and server strain, we manage request concurrency and implement exponential backoff. We route requests through distributed IP pools to maintain throughput while respecting target infrastructure.
Instead of re-scraping entire historical seasons every week, we compute hashes of current season pages and only extract new match results and updated table rows, reducing pipeline runtime and downstream load.
Quantitative syndicates use historical match results and goal timing data to train Poisson distribution models for match outcomes.
Sportsbooks ingest Over/Under and BTTS frequencies to validate their opening lines and identify pricing anomalies.
Platform providers use form guides and fixture difficulty metrics to power player recommendation engines.
Publishers populate pre-match preview articles with automated H2H statistics and team form summaries.
Club analysts benchmark their team's late-goal concession rates against league averages to identify tactical weaknesses.
In-play traders use 15-minute goal interval statistics to model liquidity entry points on exchange platforms.
"Soccerstats holds decades of structured football history, but its legacy HTML table structure makes automated extraction a nightmare for unspecialised crawlers."
Extracting data from Soccerstats requires parsing deeply nested legacy HTML tables, handling inconsistent team naming conventions across seasons, and managing rate limits. DataFlirt normalises this chaos into clean, relational datasets so your quants can focus on modelling rather than DOM traversal.
Everything supported by our soccerstats.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 and request scheduling, while lxml processes complex XPath queries against legacy HTML structures with high performance.
We maintain pools of datacenter and residential IPs to distribute request load, preventing rate limits while extracting large historical archives.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for weekend fixture updates. All state and normalised team mappings are stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About soccerstats.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available statistical data is generally permissible. DataFlirt extracts factual, non-copyrightable sports statistics. We do not extract personal data or bypass authentication. Clients should review target site Terms of Service and consult legal counsel for specific commercial use cases.
We use custom lxml parsers and structural XPath queries rather than relying on CSS classes. Our engineering team maps the nested table hierarchy for each specific page type (league table, form guide, H2H) to ensure robust extraction.
We can extract data for any league available on the platform, including major European leagues (Premier League, La Liga, Serie A, Bundesliga, Ligue 1), lower divisions, and international tournaments.
Soccerstats is typically updated shortly after matches conclude. We schedule our pipelines to run at defined intervals (e.g., daily or post-weekend) to capture the latest results and updated tables.
No. Soccerstats is best suited for pre-match analysis, historical research, and post-match statistics. We do not offer sub-second live match event scraping from this source.
Yes. We can crawl the historical archives to extract league tables, match results, and team statistics spanning multiple decades, depending on league availability on the site.
Our pipeline includes a normalisation layer. We maintain a mapping database that standardises team name variations into a single canonical identifier, ensuring your historical joins work correctly.
Engagements typically start with a defined set of leagues and seasons for historical extraction, followed by a recurring weekly pipeline for current season updates. Contact us with your league list for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full historical archive of 20 leagues or a weekly update of form guides and goal stats, we build and operate the pipeline. Tell us what you need.
