# Tennis Abstract Scraper - ATP/WTA Match Logs (`thirdwatch/tennis-abstract-scraper`) Actor

Scrape Tennis Abstract player match histories: date, tournament, surface, round, opponent, score, rankings, and serve/return statistics per match. Built for research and data pipelines. No login, cookies, or API keys — clean JSON output and pay-per-result pricing.

- **URL**: https://apify.com/thirdwatch/tennis-abstract-scraper.md
- **Developed by:** [Thirdwatch](https://apify.com/thirdwatch) (community)
- **Categories:** Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 results

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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Tennis Abstract Scraper — ATP/WTA Match Logs

### What you get

> **Thirdwatch portfolio:** 8,800+ users across 140+ public Actors, 2M+ records delivered, >95% run success. [Explore all Thirdwatch Actors](https://apify.com/thirdwatch).

Scrape Tennis Abstract player match histories — date, tournament, surface, round, opponent, score, rankings, and detailed serve/return statistics for every match. Reads the site's per-player match-log data directly.

Use it for tennis analytics, Elo research, betting models, and player-form tracking.

### Why this Actor

- **Full match logs:** every recorded match per player, not just recent form.
- **Deep stats:** serve/return points, aces, double faults, break points — the per-match stat columns Tennis Abstract is known for.
- **ATP + WTA:** pass any player name; slug resolution is automatic.
- **Context fields:** tournament, surface, round, player rank, and opponent rank on each row.

### Quick start

```json
{
  "players": ["Novak Djokovic", "Carlos Alcaraz"],
  "maxMatches": 50
}
```

### Input

| Field | Required | Description |
|---|---:|---|
| `players` | Yes | Player names or slugs (e.g. `Novak Djokovic` or `NovakDjokovic`). |
| `maxMatches` | No | Max match rows per player. Default: 50. |

### Output

| Field | Description |
|---|---|
| `player`, `playerSlug` | Player identity. |
| `date`, `tournament`, `surface`, `round` | Match context. |
| `opponent`, `score` | Opponent name and match score. |
| rank / opponent-rank columns | Rankings at match time. |
| serve/return stat columns | Per-match stats as published (aces, DF, serve %, break points, etc.). |

Column names follow Tennis Abstract's own headers; a few uncommon columns may appear as `colN` on unusual tables.

### Notes & limitations

- Coverage matches Tennis Abstract's own archive — exhibitions and some Challengers may be absent.
- Stats are per published match logs; rows for very old matches can be sparse.

### Use cases

- Researchers building datasets
- Developers feeding downstream pipelines
- Teams monitoring changes over scheduled runs

Last verified: 2026-09

More scrapers at [thirdwatch.dev](https://thirdwatch.dev).

# Actor input Schema

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

Player names (e.g. "Novak Djokovic") or tennisabstract.com player.cgi?p= URLs.

## `maxMatches` (type: `integer`):

Max matches per player

## Actor input object example

```json
{
  "players": [
    "Novak Djokovic"
  ],
  "maxMatches": 50
}
```

# 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": [
        "Novak Djokovic"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("thirdwatch/tennis-abstract-scraper").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": ["Novak Djokovic"] }

# Run the Actor and wait for it to finish
run = client.actor("thirdwatch/tennis-abstract-scraper").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": [
    "Novak Djokovic"
  ]
}' |
apify call thirdwatch/tennis-abstract-scraper --silent --output-dataset

```

## MCP server setup

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

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/zsn1i7LOwIvZMVqgv/builds/4uuChqojxdyuVZShD/openapi.json