# Tennis Scraper - ATP & WTA Results, Scores, Draws & Rankings (`datawright/tennis-scraper`) Actor

ATP and WTA tennis data as flat rows: every match with players, seeds, countries, round, court, winner, set-by-set score with tiebreaks, games and retirements; tournament calendars and champions; and the live ATP/WTA rankings with points. Any date range or a whole season. Cheap and fast.

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

## Pricing

from $1.00 / 1,000 row of data

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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 Scraper - ATP & WTA Results, Scores, Draws & Rankings

**ATP and WTA tennis data** as clean JSON, CSV or Excel - one flat row per match, tournament or ranked player:

- 🎾 **Matches** - every singles and doubles match: players, **seeds**, countries, round, court, date, status, **winner**, **set-by-set score with tiebreaks**, sets and games won, total games, tiebreaks played, **retirements and walkovers**
- 🏆 **Tournaments** - the season calendar with dates, location, Grand Slam flag, draws and **champions**
- 📊 **Rankings** - the current ATP / WTA top 150 with **points, previous rank and movement**, plus age, plays (left/right), height and birthplace

Pull today's results, an upcoming order of play, a whole tournament or **an entire season**. Built for **tennis betting models, totals/handicap research, fantasy tennis, journalism, apps and AI agents**. No API key, no browser - fast and cheap.

### Quick start

The last few days of results on both tours:

```json
{
  "dataType": "matches"
}
```

One player's whole season (singles only):

```json
{
  "dataType": "matches",
  "season": "2026",
  "players": [
    "Alcaraz"
  ],
  "includeDoubles": false,
  "onlyCompleted": true
}
```

Every Grand Slam final of the season:

```json
{
  "dataType": "matches",
  "season": "2026",
  "grandSlamsOnly": true,
  "rounds": [
    "Final"
  ]
}
```

Current WTA rankings:

```json
{
  "dataType": "rankings",
  "tours": [
    "wta"
  ]
}
```

Tournament calendar with champions:

```json
{
  "dataType": "tournaments",
  "season": "2026",
  "tours": [
    "atp"
  ]
}
```

### Output

Match row:

```json
{
  "tour": "ATP",
  "season": 2026,
  "tournamentId": 5,
  "tournament": "Kinoshita Group Japan Open Tennis Championships",
  "tournamentStart": "2026-09-27T04:00Z",
  "tournamentEnd": "2026-10-07T03:59Z",
  "isGrandSlam": false,
  "location": "Tokyo, Japan",
  "draw": "Men's Singles",
  "isDoubles": false,
  "round": "Round 1",
  "isQualifying": false,
  "matchId": "183499",
  "date": "2026-10-01T09:30Z",
  "status": "Final",
  "completed": true,
  "court": "Kinoshita Group Show Court",
  "player1": "Alejandro Davidovich Fokina",
  "player1Id": "3212",
  "player1Country": "Spain",
  "player1Seed": null,
  "player2": "Matteo Berrettini",
  "player2Id": "2622",
  "player2Country": "Italy",
  "player2Seed": null,
  "winner": "Matteo Berrettini",
  "loser": "Alejandro Davidovich Fokina",
  "score": "1-6 7-6(1) 6-4",
  "setsPlayed": 3,
  "player1SetsWon": 1,
  "player2SetsWon": 2,
  "player1Games": 16,
  "player2Games": 14,
  "totalGames": 30,
  "tiebreaksPlayed": 1,
  "retired": false,
  "walkover": false,
  "defaulted": false,
  "notes": "Matteo Berrettini (ITA) bt Alejandro Davidovich Fokina (ESP) 1-6 7-6 (7-1) 6-4",
  "set1": "1-6",
  "set2": "7-6(1)",
  "set3": "6-4",
  "set4": null,
  "set5": null
}
```

Rankings row:

```json
{
  "tour": "ATP",
  "rankingDate": "2026-10-05T07:00Z",
  "rank": 1,
  "previousRank": 1,
  "movement": 0,
  "points": 11000,
  "playerId": "3623",
  "player": "Jannik Sinner",
  "country": "Italy",
  "age": 25,
  "dateOfBirth": "2001-08-16",
  "plays": "Right",
  "heightCm": 191,
  "birthPlace": "San Candido, Italy"
}
```

`score` is written from the winner's side; the number in brackets is the tiebreak loser's points, as on scoreboards. `set1`..`set5` hold each set, so totals and handicaps are easy to compute in a spreadsheet.

### Pricing

Pay per row delivered - see the **Pricing** tab. For scale: the 2026 ATP season had about 7,700 completed matches by mid-October including qualifying and doubles (about 3,800 main-draw singles); the WTA about 10,400. Turn off doubles and qualifying, or filter by player or tournament, to pull only what you need. Use *Max rows*, filters and the run's maximum cost to cap spending.

### FAQ

**Which events are covered?** ATP and WTA tour events, including the Grand Slams and Masters / WTA 1000s, with qualifying and doubles draws - everything ESPN publishes on its tennis scoreboards.

**Are point-by-point or serve statistics included?** No - this actor focuses on results, scores, draws and rankings. That keeps it fast and cheap.

**How fresh is it?** Live: scores update during matches and results are available as soon as a match ends.

### Disclaimer

Not affiliated with, endorsed by or connected to ESPN, the ATP, the WTA or any tournament. Provides publicly available factual tennis results. It only reads publicly accessible data and grants you no rights in it: you are responsible for making sure your use complies with the data source's terms of use and applicable laws (including copyright, database and privacy laws).

# Actor input Schema

## `dataType` (type: `string`):

Matches = one row per match (singles and doubles). Tournaments = one row per event. Rankings = one row per ranked player.

## `tours` (type: `array`):

Which tours to include.

## `season` (type: `string`):

Whole season, e.g. "2026". Leave empty and use dates instead (or neither for the last few days).

## `dateFrom` (type: `string`):

YYYY-MM-DD. Overrides season.

## `dateTo` (type: `string`):

YYYY-MM-DD, inclusive. Defaults to today.

## `daysBack` (type: `integer`):

With no season or dates: matches from the last N days (falls back to the most recent tournament days when there were none).

## `daysAhead` (type: `integer`):

Also include scheduled matches up to N days ahead (order of play).

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

Keep matches (or rankings) of these players - partial names are fine, e.g. Alcaraz, Sabalenka.

## `tournaments` (type: `array`):

Keep only tournaments whose name contains any of these, e.g. Wimbledon, Shanghai.

## `rounds` (type: `array`):

Keep only these rounds, e.g. Final, Semifinal, Quarterfinal, Round 1.

## `grandSlamsOnly` (type: `boolean`):

Australian Open, Roland Garros, Wimbledon and US Open only.

## `includeDoubles` (type: `boolean`):

Turn off for singles only.

## `includeQualifying` (type: `boolean`):

Turn off for main-draw matches only.

## `onlyCompleted` (type: `boolean`):

Drop scheduled and in-progress matches.

## `rankingsTop` (type: `integer`):

How many ranked players to return (ESPN publishes the top 150).

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

Stop after this many rows (0 = no limit).

## Actor input object example

```json
{
  "dataType": "matches",
  "tours": [
    "atp",
    "wta"
  ],
  "daysBack": 3,
  "daysAhead": 0,
  "grandSlamsOnly": false,
  "includeDoubles": true,
  "includeQualifying": true,
  "onlyCompleted": false,
  "rankingsTop": 150,
  "maxItems": 200
}
```

# 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": 200
};

// Run the Actor and wait for it to finish
const run = await client.actor("datawright/tennis-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": 200 }

# Run the Actor and wait for it to finish
run = client.actor("datawright/tennis-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": 200
}' |
apify call datawright/tennis-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datawright/tennis-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/FeAHP8YJAT5KWcHT8/builds/BhJfWwCfDmpN2C8Xq/openapi.json