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Sports data extraction

Results, standings and stats, delivered as clean data.

We collect results and standings, fixtures, player stats and match details from public league and sports websites.

Built around your leagues and sources, delivered on your schedule.

Get a free sample from your own sources within 24 hours and see the data before you commit.

100% job success10+ years of experience20k+ course studentsWork directly with the founder
standings.csvsample data
Illustrative sample of a scraped league table. Select a team to see its position history.
teamplayedpointsformpositionstatus
3470WWWDW2▲ 3
3479WDWWW1unchanged
3458LLDWL5▼ 2
3441DLWLD12new
3424LLLDL18removed
Illustrative data. Select a team to see its position history.

Trusted by 500+ companies, startups and individuals.

Rock the Rankings
Iceland Travel
Blue Orange
myrunway
boxtribe
Kore Real Estate Group
42matters
Udemy
nextflat
Crunch
Swell Inc.
Sunday
Wildcat Capital Management
XCStats
Distribusion
catchhotels
QAPA
Product Folks
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Quotient
Saw.com
Markmi
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Tensile Capital Management
Car Geeks Collision
ParcelNinja
Packt
Pioneer Interests
Syndigo
Luxury Family Rentals
flowingbee
Moshbit
Velocity by design
Auditi
mxney
What you get

Every field you need from a sports source.

These are the usual building blocks. We add or drop fields to fit your project, and the free sample shows exactly what comes back.

Match

What was played, and when.

  • match_id
  • league
  • season
  • round
  • match_date
  • kickoff_time
  • home_team
  • away_team
  • venue

Result and events

How it ended, and what happened.

  • status
  • score
  • half_time_score
  • winner
  • goals
  • cards
  • substitutions
  • attendance
  • referee

Standings

Where each team stands.

  • position
  • played
  • won
  • drawn
  • lost
  • goals_for
  • goals_against
  • points
  • form

Players and teams

Who plays, and how they perform.

  • player_name
  • team
  • position_played
  • appearances
  • goals
  • assists
  • minutes
  • injuries
  • squad_value

Odds and markets

What the market expects, where published.

  • home_odds
  • draw_odds
  • away_odds
  • odds_source
  • odds_history
  • market_type
  • last_updated

History and source

How each record changes over time.

  • first_seen
  • last_seen
  • position_changes
  • status_changes
  • source_urls
  • sources_count
  • last_checked
  • duplicate_of

On requestFixtures and schedules, live scores, transfer and contract news, player market values, team and player profiles, and past seasons, where they are published.

Who uses it

How teams put sports data to work.

Media and fan apps

Fill your scores, tables and fixtures pages with data from many sources, matched to one record per match and kept current.

Typical refresh: hourly to daily

Analytics and fantasy platforms

Feed models and fantasy games with clean results, player stats and injuries, with the history behind every number.

Typical refresh: daily

Clubs, agents and scouts

Follow players, squads and market values across leagues, and spot changes in form, minutes or contract status early.

Typical refresh: weekly

Researchers and data journalists

Build clean multi-season datasets of results and tables for studies and stories, from one consistent history.

Typical refresh: weekly to monthly
Why it is hard

Why sports data is harder than it looks.

Sports data moves fast and every source formats it differently. These are the five problems we plan for, and how we handle each.

The problem

Names are spelled differently.

One site writes "FC Eastfield", another "Eastfield" or "Eastfield FC", and players appear with and without accents, initials or nicknames.

How we handle it

We match teams, players and competitions to one consistent ID and keep the original spelling, so records from different sources join cleanly.

The problem

Scores and tables keep changing.

Scores update minute by minute, results get corrected, and tables move after every match, so a single snapshot is soon out of date.

How we handle it

Scheduled re-checks store every change with a timestamp, so you get position history, corrected results and the date each record was last confirmed.

The problem

Seasons and competitions differ.

Leagues use different formats, rounds, tie-breaks and calendars, and cups and playoffs do not fit a simple table.

How we handle it

We model competitions, rounds and seasons explicitly, so each row says which competition, stage and season it belongs to.

The problem

Stats are defined differently.

One source counts a shot, an assist or a minute differently from another, and some publish estimates instead of official numbers.

How we handle it

We keep each statistic as published, note the source, and flag where sources disagree.

The problem

Sites block and change.

Sports sites use bot protection and redesign their pages, and scrapers quietly return half the rows.

How we handle it

Every pipeline is monitored with row-count and field checks. When a site changes or blocks us, we fix it. Maintenance is part of the service.

How it works

From your list of leagues to a live data feed.

Tell us what you need

Share the leagues or sports sites, the seasons, and the fields. A short call or a message is enough.

See a free sample

Within 24 hours of the call, depending on the sources, you get a sample from your own sources. You see the fields, the quality and the refresh rate before you commit.

Pick the schedule

Hourly, daily, weekly or monthly. We scope the price once you have seen the sample.

Receive and rely on it

Data arrives as CSV, JSON, Excel, Google Sheets, Airtable, in your database, through an API or in cloud storage. We monitor it and fix what breaks.

Common questions

Straight answers.

What teams ask before they start.

What sports data can you collect?

Fixtures, results, standings, player and team stats, injuries and transfer news, and match details such as venue, attendance and officials. On request we also collect player market values and odds where they are published. Tell us the fields you need and the free sample shows what is possible.

Can you track changes and corrections?

Yes. We re-check your sources on a schedule from hourly to monthly and store every change. You get position history, corrected results and last-confirmed dates, not just a snapshot. A team that disappears from a table is marked removed. If it shows up in another division we can usually tell it was promoted or relegated.

How do you handle different team and player names?

We match teams, players and competitions to one consistent ID across sources and keep the original spelling. Anything we cannot match with confidence is marked, not guessed.

Which leagues, sports and countries can you cover?

Football, basketball, baseball, hockey, tennis and more, from league websites, sports portals and stats sites, in any country and language. If a source is not practical, for example because of heavy blocking, we tell you upfront, before you commit to anything.

Is scraping sports websites legal?

We collect publicly available data only, such as the results, tables and stats any visitor can see. Rules differ by country and by site, a site's terms may restrict automated access, some data is licensed by leagues, and player and staff names are personal data under laws like GDPR. We flag risky sources and fields before we build. We are not lawyers, so for sensitive uses we recommend checking with your own counsel.

How do I receive the data?

CSV, JSON, Excel, Google Sheets, Airtable, your own database, an API or cloud storage, on a schedule you choose, ready to feed your app, model or dashboard.

How much does it cost, and how fast can we start?

Pricing depends on sources, volume and frequency. We scope it after a free discovery call and sample. Most samples are ready within 24 hours of the call, depending on the sources.

Your free sports data sample, built on your actual sources.

Tell us the leagues, sports sites, seasons and fields you need. We reply with a plan and a sample.

Prefer to talk first? Book a call with the founder

Lazar Telebak, founder of REData Analytics

Lazar Telebak

Founder, REData Analytics

You work directly with the person who builds your data feed. In 10+ years he has worked with 500+ startups, agencies and companies, from one-time extractions to long-running systems processing tens of millions of records.

Founder profile on LinkedIn ↗