# Tennis Match & Player Data Scraper - ATP & WTA Results (`themineworks/tennis-match-data`) Actor

Recent tour-level match results for any ATP or WTA player: opponent, score, surface, round, ranking at the time, and per-match serve/return stats (dominance ratio, ace %, double fault %, first/second serve won %). No login, no API key, no proxy.

- **URL**: https://apify.com/themineworks/tennis-match-data.md
- **Developed by:** [The Mine Works](https://apify.com/themineworks) (community)
- **Categories:** Sports, MCP servers
- **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.80 / 1,000 match scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Tennis Match & Player Data Scraper

Recent tour-level match results for any ATP or WTA player, sourced from Tennis Abstract: opponent, score, surface, round, both players' rankings at the time, and per-match serve/return stats (dominance ratio, ace %, double fault %, first/second serve won %). No login, no API key, no proxy.

### Why this exists

Official tennis data feeds (Sportradar, etc.) are enterprise contracts, not something a developer or small research team can buy per-call. This actor reads Tennis Abstract's own public per-player data directly.

### Input

- **players** (required) — PascalCase names with no space, e.g. `["NovakDjokovic", "CarlosAlcaraz", "IgaSwiatek"]`.

### Output

One row per recent match: `player`, `date`, `tournament`, `surface`, `round`, `player_rank`, `opponent_rank`, `opponent_and_result`, `won`, `score`, plus per-match serve stats (`dominance_ratio`, `ace_pct`, `double_fault_pct`, `first_serve_in_pct`, `first_serve_won_pct`, `second_serve_won_pct`).

Typically returns each player's most recent ~20 tour-level matches.

### What this does NOT cover (yet)

Tennis Abstract's per-player data actually includes 19 distinct tables (career surface splits, head-to-head records, year-end ranking history, serve-speed breakdowns, point-by-point stats, and more). This version parses only the single highest-value one — recent match results. The rest are present in the same source and are a documented, real expansion opportunity, not a limitation of the site.

### A note on seasonality

Tour-level tennis has an off-season (roughly November-December). A player with no recent tournament will simply return their last matches before the break, not an error.

### Pricing

Pay per delivered match row. You are never charged for a player name that doesn't match an existing profile — check spelling (PascalCase, no space) if you get 0 results.

# Actor input Schema

## `players` (type: `array`):

ATP/WTA player names, PascalCase with no space, e.g. \["NovakDjokovic", "CarlosAlcaraz", "IgaSwiatek"]. Returns each player's most recent tour-level matches (typically the last ~20).

## Actor input object example

```json
{
  "players": [
    "NovakDjokovic"
  ]
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "players": [
        "NovakDjokovic"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("themineworks/tennis-match-data").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "players": ["NovakDjokovic"] }

# Run the Actor and wait for it to finish
run = client.actor("themineworks/tennis-match-data").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "players": [
    "NovakDjokovic"
  ]
}' |
apify call themineworks/tennis-match-data --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,themineworks/tennis-match-data"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/b2Fpa4k83n13cbv7V/builds/GHOoYf5kkL6jWB6zM/openapi.json