🎯 Tennis Abstract Player Match Scraper

🚀 Pull tennis abstract player match scraper data in seconds. Get the full dataset from the source as clean tabular / spreadsheet / tabular / tabular.

🕒 Last updated: 2026-05-27 · 📊 10 fields per record · Pay-per-result pricing · Public data only

Pull Tennis Abstract player match histories: dates, tournaments, surfaces, rounds, opponents, ranks and scores for ATP/WTA players. Provide a player slug. Open as tabular records for tennis analysts, betting researchers and sports data teams.

This Actor produces a structured dataset with 10 fields per record, ready for analysts, researchers and downstream pipelines.

🎯 Target Audience	💡 Primary Use Cases
Data analysts, researchers, data teams	Market research, dataset enrichment, competitive intelligence, lead generation
BI teams, agencies, indie developers	Building dashboards, alerts, BI pull, custom apps

📋 What the Tennis Abstract Player Match Scraper does

• Fetches the public data feed in real time.
• Normalises every record into a clean schema with 10 fields.
• Outputs straight to your Apify dataset as tabular / spreadsheet / tabular / tabular.
• Supports pagination, keyword filters and per-run limits via maxItems.
• Designed for headless, repeatable runs (cron, Make, Zapier, webhooks).

💡 Why it matters: the source is public but unstructured. This Actor turns it into queryable rows you can join, filter and chart instantly.

🎬 Full Demo

🚧 Coming soon

⚙️ Input

Field	Type	Description
maxItems	integer	Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000
player	string	Tennis Abstract player slug (no spaces). Examples: NovakDjokovic, CarlosAlcaraz, IgaSwiatek, JannikSinner.

Example preview run

{
  "maxItems": 10
}

Example filtered run

{
  "maxItems": 5,
  "player": "NovakDjokovic"
}

⚠️ Good to Know: free accounts are capped at 10 items per run as a preview. Upgrade to a paid plan to unlock up to 1,000,000 items per run.

📊 Output

Each record contains these 10 fields:

Field	Type	Description
title	string	Record title.
date	string	date value from the source.
tournament	string	tournament value from the source.
surface	string	surface value from the source.
round	string	round value from the source.
rank	string	rank value from the source.
opponent	string	opponent value from the source.
score	string	score value from the source.
url	string	Source URL.
player	string	player value from the source.
scrapedAt	string	ISO 8601 timestamp of the scrape.
error	string	Error message (only present on failure records).

Sample records

[
  {
    "title": "(3) Djokovic d. Giovanni Mpetshi Perricard [FRA]",
    "date": "25-May-2026",
    "tournament": "Roland Garros",
    "surface": "Clay",
    "round": "R128",
    "rank": "4",
    "opponent": "(3) Djokovic d. Giovanni Mpetshi Perricard [FRA]",
    "score": "5-7 7-5 6-1 6-4",
    "url": "https://www.tennisabstract.com/cgi-bin/player.cgi?p=GiovanniMpetshiPerricard",
    "player": "NovakDjokovic",
    "scrapedAt": "2026-05-26T22:24:11.037Z",
    "error": null
  },
  {
    "title": "(Q) Dino Prizmic [CRO] d. (3) Djokovic",
    "date": "06-May-2026",
    "tournament": "Rome Masters",
    "surface": "Clay",
    "round": "R64",
    "rank": "4",
    "opponent": "(Q) Dino Prizmic [CRO] d. (3) Djokovic",
    "score": "2-6 6-2 6-4",
    "url": "https://www.tennisabstract.com/cgi-bin/player.cgi?p=DinoPrizmic",
    "player": "NovakDjokovic",
    "scrapedAt": "2026-05-26T22:24:11.114Z",
    "error": null
  },
  {
    "title": "(14) Jack Draper [GBR] d. (3) Djokovic",
    "date": "04-Mar-2026",
    "tournament": "Indian Wells Masters",
    "surface": "Hard",
    "round": "R16",
    "rank": "3",
    "opponent": "(14) Jack Draper [GBR] d. (3) Djokovic",
    "score": "4-6 6-4 7-6(5)",
    "url": "https://www.tennisabstract.com/cgi-bin/player.cgi?p=JackDraper",
    "player": "NovakDjokovic",
    "scrapedAt": "2026-05-26T22:24:11.143Z",
    "error": null
  }
]

✨ Why choose this Actor

⚡ Fast	Lean HTTP pipeline. No browser overhead.
🎯 Accurate	Normalised fields, deduplicated rows, real-time fetch.
💰 Pay-per-result	Pay only for the records pushed to your dataset.
🔌 Integration-ready	One-click Make / Zapier / Slack / GitHub / Google Drive.
🛠️ Maintained	Updated whenever the upstream source changes its shape.

📈 How it compares to alternatives

Approach	Speed	Cost	Maintenance
Manual pull	🐌 Slow	💸 Wasted hours	🛑 Breaks often
Custom script	⚡ Fast	💻 Dev hours	🛠 You maintain it
This Actor	⚡ Fast	💰 Pay per row	✅ We maintain it

🚀 How to use

1. Create a free account w/ $5 credit
2. Open the Actor page on Apify Console.
3. Fill in the input form (or paste a tabular input).
4. Click Start and watch the dataset populate live.
5. Pull as tabular / spreadsheet / tabular / tabular, or pipe to your stack.

💼 Business use cases

📈 Market research

Pull a fresh slice of the source whenever you need it. No more stale manual pull.

🎯 Lead generation

Combine with enrichment Actors to turn raw records into outbound-ready lists.

🧪 Data science

Use the dataset as a clean input for notebooks, ML pipelines or dashboards.

📊 Competitive intelligence

Track changes over time by scheduling daily / weekly runs and diffing snapshots.

🔌 Automating Tennis Abstract Player Match Scraper

Use Apify's integrations to wire the dataset into your stack:

• Make - trigger a scenario when a run finishes
• Zapier - fan out new rows to Sheets, Airtable, Notion
• Slack - get a notification with the dataset link
• Airbyte - sync results into your data warehouse
• GitHub Actions - schedule runs from your CI
• Google Drive - auto-upload the tabular/tabular

🌟 Beyond business use cases

🔬 Research

Use the dataset for academic studies, longitudinal analyses or sector reports.

🎓 Personal projects

Bootstrap side projects, dashboards, niche search engines, alerts.

🤝 Non-profit

Investigations, policy work, transparency tools - public data put to public use.

🧪 Experimentation

Prototype quickly; the Actor is the boring plumbing so you can focus on the idea.

🤖 Ask an AI assistant about this scraper

• Open in ChatGPT
• Open in Claude
• Open in Perplexity
• Open in Copilot

Paste this link and ask "summarise this Actor and suggest 5 use cases": https://apify.com/parseforge/tennis-abstract-scraper

❓ Frequently Asked Questions

🟢 Is the data live? Yes. Every run hits the source and returns fresh data.

🟢 Do I need an API key? No. Everything happens through your Apify account.

🟢 What output shape are supported? tabular records, RSS - Apify dataset standard.

🟢 Can I schedule it? Yes, with Apify Schedules or external cron via Make/Zapier.

🟢 What's the cost? Pay-per-result - see the pricing tab on the Actor page.

🟢 Is this affiliated with Tennis Abstract Player Match? No. Independent scraper. Public data only.

🟢 Do you store the data? Your Apify dataset, your data. We don't hold copies.

🟢 What if the upstream changes shape? Open a ticket and we'll patch the Actor.

🟢 Can I filter results? Yes, see the input section above for filters.

🟢 Will it work for 10-field pull? Yes - full schema in every row.

🟢 Can I integrate it with Make/Zapier? Yes - see the automation section.

🔌 Integrate with any app

Apify exposes a REST API + webhooks. Plug the dataset into Make, Zapier, n8n, Airbyte, Slack, Google Sheets, Airtable, Notion, Postgres, BigQuery, Snowflake, GitHub Actions, Discord - anywhere your team already works.

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💡 Pro Tip: browse the complete ParseForge collection for more public-data Actors.

🆘 Need Help? Open our contact form

⚠️ Disclaimer: independent tool, not affiliated with Tennis Abstract Player Match. Only publicly available data is collected.