# Tennis Abstract Player Match Scraper (`parseforge/tennis-abstract-scraper`) Actor

Scrapes Tennis Abstract player match histories by player slug and returns each match as a flat row with score, surface, round, opponent, and match statistics.

- **URL**: https://apify.com/parseforge/tennis-abstract-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Lead generation, Other, Automation
- **Stats:** 1,486 total users, 731 monthly users, 90.3% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $19.00 / 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?

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

[![ParseForge](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg)](https://apify.com/parseforge?fpr=vmoqkp)

### Tennis Abstract Player Match Scraper

**Scrape Tennis Abstract player match histories by player slug, up to a million records per run.** Every match comes with its score, surface, round, opponent, and match statistics. No login or API key. Export to CSV, JSON, Excel, or XML.

Tennis Abstract's official site has no public API, and manual copying of match histories is slow and error-prone. This reads the public player pages directly, filtered by player slug, and returns each match in one fixed schema.

| Who uses it | What they scrape Tennis Abstract for |
|---|---|
| Tennis analysts | Which surfaces and rounds a player performs best on |
| Sports bettors | Recent form and head-to-head records before placing a bet |
| Journalists | Match statistics for player profiles and tournament previews |
| Data scientists | Building predictive models from historical match data |

### What it does

This Actor collects Tennis Abstract player match histories by player slug and returns each match as a flat row.

- 🎾 **Player match history:** scrape every ATP or WTA match for a player slug like NovakDjokovic or IgaSwiatek.
- 📊 **Match statistics:** each row includes score, surface, round, opponent, and detailed serve and return stats.
- 🔢 **Scalable collection:** set maxItems from 1 to 1,000,000 records per run.
- 📁 **Multiple export formats:** download as CSV, JSON, Excel, or XML.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

### What you can do with Tennis Abstract data

**📈 Track player performance.**

A tennis analyst scrapes NovakDjokovic's match history to see his win rate on clay versus hard courts over the last five years.

**🎯 Build betting models.**

A sports bettor scrapes recent matches for CarlosAlcaraz and JannikSinner to compare their form before a head-to-head.

**📰 Write player profiles.**

A journalist scrapes IgaSwiatek's match history to pull statistics for a tournament preview article.

**🧪 Train predictive models.**

A data scientist scrapes thousands of matches across players to build a model that predicts match outcomes.

### Why choose this scraper

|  | What you get |
|---|---|
| **No API key** | Reads public Tennis Abstract pages directly, no registration or OAuth. |
| **Fixed schema** | Every match returns the same fields, ready for analysis. |
| **Player-focused** | Get a complete match history for any player with a valid slug. |

### How it compares

This Actor focuses on scraping a single player's match history from Tennis Abstract with a simple player slug input, while competitors may offer broader sources or additional features.

| Feature | ParseForge | Tennis Abstract Player Match Scraper & API | Tennis Abstract Player Match History Scraper | Tennis Scraper |
|---|---|---|---|---|
| Scrape by player slug | Yes | Yes | Yes | Yes |
| Match statistics (serve/return) | Yes | Yes | Yes | Yes |
| Source links | Not listed | Not listed | Yes | Not listed |
| SofaScore data | Not listed | Not listed | Not listed | Yes |
| Live matches | Not listed | Not listed | Not listed | Yes |
| Point-by-point records | Not listed | Not listed | Not listed | Yes |

### Configure the run

Drive the Actor with a Tennis Abstract player slug and a maximum record count, and it returns each match as a flat row. The Input tab lists every parameter.

A first run with the defaults:

```json
{
  "maxItems": 10
}
```

A larger pull:

```json
{
  "maxItems": 200
}
```

### Pricing

Pay-per-result: **$0.021 per result** collected. You pay only for the results written to your dataset.

| Results collected | Approximate cost |
|---|---|
| 100 results | $2.10 |
| 1,000 results | $21.00 |
| 10,000 results | $210.00 |

New Apify accounts start with $5 in free credit.

### Free users

Free-plan runs return up to 10 results as a preview. [Upgrade your Apify plan](https://console.apify.com/sign-up?fpr=vmoqkp) to collect up to 1,000,000 results per run.

### Run it

1. [Create a free Apify account with $5 in credit](https://console.apify.com/sign-up?fpr=vmoqkp).
2. Open the [Tennis Abstract Player Match Scraper](https://apify.com/parseforge/tennis-abstract-scraper?fpr=vmoqkp).
3. Set your inputs and any filters, then click **Start**.
4. Export the results as CSV, Excel, JSON, or XML from the **Dataset** tab.

Run it programmatically through the [Apify API](https://docs.apify.com/api/v2) (`run-sync-get-dataset-items`) or the [ApifyClient](https://docs.apify.com/api/client/js) for JavaScript and Python.

### Use with AI agents (MCP)

Give an AI agent live access to Tennis Abstract through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?tools=parseforge/tennis-abstract-scraper"
```

Then prompt it in plain language to run the scraper and read back the results.

### Troubleshooting

**Why am I getting no results?**

Check that the player slug is correct and matches the Tennis Abstract URL. Also ensure the player has matches listed on Tennis Abstract.

**Why is the run slow?**

Tennis Abstract pages can be slow to load. Reduce maxItems or run during off-peak hours.

**Why are some match statistics missing?**

Tennis Abstract may not have complete statistics for older or lower-level matches. The Actor returns whatever is available on the page.

**Why did the run fail with an error?**

Check that the player slug contains no spaces and is a valid Tennis Abstract player. If the problem persists, try again later as the site may be temporarily unavailable.

### FAQ

| Question | Answer |
|---|---|
| What is a Tennis Abstract player slug? | It is the player's name without spaces, as used in Tennis Abstract URLs. Examples: NovakDjokovic, CarlosAlcaraz, IgaSwiatek, JannikSinner. |
| Does this work for both ATP and WTA players? | Yes, you can scrape match histories for any player listed on Tennis Abstract, including ATP and WTA players. |
| What match statistics are included? | Each match row includes score, surface, round, opponent, and detailed serve and return statistics as provided by Tennis Abstract. |
| How many matches can I scrape in one run? | You can set maxItems from 1 to 1,000,000 records per run. |
| Do I need an API key or login? | No, this Actor reads public Tennis Abstract pages directly, no registration or OAuth required. |
| What export formats are supported? | You can export the scraped data as CSV, JSON, Excel, or XML. |
| Can I scrape multiple players at once? | This Actor scrapes one player slug per run. To scrape multiple players, run the Actor multiple times with different player inputs. |
| How do I find a player's slug? | Go to the player's Tennis Abstract page and copy the last part of the URL. For example, https://www.tennisabstract.com/cgi-bin/player.cgi?p=NovakDjokovic has slug NovakDjokovic. |
| Is this Actor affiliated with Tennis Abstract? | No, this is an independent scraper that reads publicly available data from Tennis Abstract. |
| What if a player has no matches? | The Actor will return an empty dataset. Check that the player slug is correct and that the player has matches listed on Tennis Abstract. |

### Related actors

Browse the full [ParseForge collection](https://apify.com/parseforge?fpr=vmoqkp) for more scrapers.

🆘 **Need help?** Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ **Disclaimer.** This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Tennis Abstract. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

# Actor input Schema

## `maxItems` (type: `integer`):

How many records to collect per run.

## `player` (type: `string`):

Tennis Abstract player slug (no spaces). Examples: NovakDjokovic, CarlosAlcaraz, IgaSwiatek, JannikSinner.

## Actor input object example

```json
{
  "maxItems": 10,
  "player": "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 = {
    "maxItems": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/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 = { "maxItems": 10 }

# Run the Actor and wait for it to finish
run = client.actor("parseforge/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 '{
  "maxItems": 10
}' |
apify call parseforge/tennis-abstract-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,parseforge/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/3lbL09RP0RxrnEdFU/builds/PBGS3UHK1KYDnucZJ/openapi.json