Tennis Abstract Player Match Scraper

Export recent public Tennis Abstract match results for one player into structured JSON, CSV, Excel, or an API workflow. Get a clean match-level dataset with opponents, scores, tournament context, rankings, and the statistics displayed for each public result.

What you get

Every exported row is one recent player match. Use it to:

• Track a player’s latest opponents, scores, and results.
• Build a surface-aware tennis player match history dataset.
• Compare tournament, round, ranking, and tennis match statistics trends.
• Feed match history into spreadsheets, dashboards, or analytical agents.

Input recipes

Start with a small request:

{
  "playerSlug": "NovakDjokovic",
  "maxItems": 20
}

Use the player identifier from the public Tennis Abstract player URL. For example, the slug in a player URL ending in ?p=IgaSwiatek is IgaSwiatek.

Output example

{
  "playerSlug": "NovakDjokovic",
  "playerName": "Novak Djokovic",
  "date": "2025-01-24",
  "tournament": "Australian Open",
  "surface": "Hard",
  "round": "SF",
  "opponent": "Alexander Zverev",
  "result": "W",
  "score": "7-6(5) 6-3",
  "sourceUrl": "http://www.tennisabstract.com/cgi-bin/player-classic.cgi?p=NovakDjokovic"
}

Statistics and rankings are included when displayed by the source for that match; unavailable source values are null rather than guessed.

Input settings

Who is it for?

This Actor is useful for tennis analysts, sports researchers, journalists, fantasy and scouting workflows, and developers building player-history datasets. It is designed for public recent-match data for a specific player, not live scoring or private account data.

Tips

• Copy the exact player slug from the public player URL to avoid targeting a similarly named player.
• Begin with a small maxItems value when testing a new workflow.
• Use sourceUrl to retain a direct public reference alongside every exported row.
• Export to CSV or Excel for spreadsheets, or consume the default dataset from your application.

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/tennis-abstract-scraper').call({
  playerSlug: 'NovakDjokovic',
  maxItems: 20,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("fetch_cat/tennis-abstract-scraper").call(
    run_input={"playerSlug": "NovakDjokovic", "maxItems": 20}
)
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

cURL

curl -X POST \
  'https://api.apify.com/v2/acts/fetch_cat~tennis-abstract-scraper/runs?token=<APIFY_TOKEN>' \
  -H 'content-type: application/json' \
  -d '{"playerSlug":"NovakDjokovic","maxItems":20}'

MCP and AI agents

Use this Actor from Apify MCP by enabling https://mcp.apify.com?tools=fetch_cat/tennis-abstract-scraper.

For Claude Code, add the MCP server:

claude mcp add apify -- npx -y @apify/mcp-server --tools=fetch_cat/tennis-abstract-scraper

Or add this server configuration:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": ["-y", "@apify/mcp-server", "--tools=fetch_cat/tennis-abstract-scraper"],
      "env": { "APIFY_TOKEN": "<APIFY_TOKEN>" }
    }
  }
}

Example prompts: “Export the latest 20 matches for NovakDjokovic” or “Return IgaSwiatek’s recent hard-court matches as a table.”

Support

Open an issue on the Actor page with the player slug and a non-sensitive example. Include the expected public player page so support can reproduce the request.