⚽ TheSportsDB Scraper

Search TheSportsDB for sports teams or players by name and get clean, structured data — name, sport, league, country, stadium, formed year, social links, badge/thumbnail and description. Powered by the public TheSportsDB API, so it's fast and reliable: no browser, no login, no API key.

Built for sports data products, fan apps, dashboards and datasets. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

🔎 What is the TheSportsDB Scraper?

Give it team or player names (e.g. "Arsenal", "Lionel Messi") and it returns matching records as structured rows — switch between teams and players with one setting.

What data does it extract?

• Name (and alternate names) and sport
• League and country
• Stadium, capacity and location (teams)
• Formed year / birth date and position (players)
• Social links (website, Facebook, Twitter, Instagram)
• Badge / thumbnail image
• Description

⬇️ Input

Field	Type	Description
searchTerms	array	Team or player names, e.g. Arsenal.
searchType	string	team or player.

Example input

{
  "searchTerms": ["Arsenal", "Real Madrid"],
  "searchType": "team"
}

⬆️ Output

One record per team:

{
  "type": "team",
  "id": "133604",
  "name": "Arsenal",
  "sport": "Soccer",
  "league": "English Premier League",
  "country": "England",
  "formed_year": "1886",
  "stadium": "Emirates Stadium",
  "stadium_capacity": "60272",
  "website": "www.arsenal.com",
  "twitter": "twitter.com/arsenal",
  "badge": "https://.../badge.png",
  "description": "Arsenal Football Club is a professional football club...",
  "query": "Arsenal"
}

💡 Use cases

• ⚽ Sports apps — enrich teams and players with metadata and images.
• 📊 Dashboards — build league and team datasets.
• 🗂️ Catalogs — assemble a structured sports database.
• 🤖 LLM / app pipelines — feed structured sports data into your tools.

📈 Why scrape TheSportsDB?

Sports data is valuable when you need quick, structured enrichment without building a full sports database yourself. TheSportsDB provides team and player metadata across many sports, including names, leagues, countries, stadiums, images and social links. That is enough for many fan apps, internal dashboards, content sites, newsletters, trivia products and LLM tools.

Instead of manually copying team badges, stadium names or player summaries, you can pass a list of names and receive normalized rows. This is useful for prototyping sports products, cleaning messy spreadsheets, enriching a CMS, preparing seed data for an app, or adding context to match schedules and sports-news feeds.

🔁 Automation workflow

Use the actor as an enrichment step. First collect team names from fixtures, news articles, league tables or your own database. Then run this scraper with searchType set to team and merge the returned badge, country, league, stadium and social fields into your system. For player-focused workflows, switch to player and enrich rosters, fantasy tools or editorial databases.

For scheduled workflows, refresh the same list weekly or monthly. Team metadata does not change every hour, but badges, descriptions, websites and social handles can change over time. A scheduled run keeps your application data current without expensive API development.

✅ Data quality notes

The actor calls TheSportsDB's public API and normalizes the response into stable fields. Some searches can return multiple matches, especially for common player names or clubs with similar names. Use exact names where possible and keep the query field so you can trace each output row back to the input that produced it. If a field is not available from the source, it is returned as null instead of being guessed.

❓ FAQ

Do I need an API key or login? No — it uses the public TheSportsDB API.

Teams and players? Yes — set searchType.

Which sports? Many — soccer, basketball, American football, motorsport and more.

Do I get images? Yes — team badges and player thumbnails.

Are social links included? Yes — website and social profiles for teams.

How does pricing work? Pay per result returned. No subscription.

Is it legal? It uses the public TheSportsDB API. Use responsibly and within their terms.

⚙️ How it works

The scraper calls the TheSportsDB API directly and returns clean rows — no browser and no key. It looks up each team or player by name and normalizes the response into consistent fields, so you get a tidy table instead of raw JSON. Runs are fast and dependable, which is why the actor keeps passing its daily health check. The same input shape works for one lookup or a long list of names.

👥 Who uses sports data?

Sports data is valuable to app developers, media teams, analysts and fan communities. A developer enriches a fantasy app with team metadata; a media site pulls badges and descriptions; an analyst builds a league dataset; a bot feeds structured data into a chat experience. Because every record is plain JSON with consistent fields, it drops straight into a spreadsheet, database, BI tool or LLM pipeline with no custom parsing.

📤 Export, schedule & integrate

Every run is saved to a dataset you can export to JSON, CSV, Excel, XML or RSS, or pull through the Apify API. Wire it into Make, Zapier, n8n, Google Sheets, Slack or your own database, run it on a schedule to keep your data fresh, and call it from AI agents through the Apify MCP server.

💡 Tips for best results

• Use exact team names for the best matches.
• Switch searchType to player to enrich rosters.
• Schedule runs to refresh badges and descriptions.
• Combine many names in one run to build a dataset fast.

❓ More FAQ

How fresh is the data? It is fetched live on each run.

Can I run it automatically? Yes — use Apify Schedules (cron).

Which export formats? JSON, CSV, Excel, XML and RSS, plus the Apify API.

Can AI agents use it? Yes — via the Apify API and MCP server.

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