# Tennis player database: profiles with rank and Elo per surface

**Use case:** 

One row per player: full name, birth date, height, playing hand and backhand, country, photo, ATP or WTA rank and points, and Elo overall, at its peak and on hard, clay and grass. The example reads ten of the top men and women; replace them with any players' names or Flashscore URLs.

## Input

```json
{
  "mode": "playerProfiles",
  "sackmannFile": "matches",
  "closingOdds": false,
  "pointByPointFormat": "games",
  "dateFrom": "today",
  "matchIds": [],
  "playerUrls": [
    "Jannik Sinner",
    "Carlos Alcaraz",
    "Novak Djokovic",
    "Alexander Zverev",
    "Taylor Fritz",
    "Aryna Sabalenka",
    "Iga Swiatek",
    "Gauff",
    "Elena Rybakina",
    "Jessica Pegula"
  ],
  "rankingLists": [
    "atp",
    "wta"
  ],
  "rankingFormat": "standard",
  "tournamentUrls": [],
  "years": [],
  "statsSplitBy": "season",
  "eloTour": "atp",
  "eloSurface": "all",
  "tours": [],
  "matchType": "singles",
  "status": "all",
  "tournaments": [],
  "players": [],
  "surfaces": [],
  "includeOdds": true,
  "oddsCountry": "GB",
  "oddsMarkets": "match-winner",
  "includeH2H": true,
  "includeStatistics": true,
  "includePointByPoint": false,
  "includeRankings": true,
  "sortBy": "tour",
  "maxItems": 100
}
```

## Output

```json
{
  "name": {
    "label": "Player",
    "format": "text"
  },
  "fullName": {
    "label": "Full name",
    "format": "text"
  },
  "country": {
    "label": "Country",
    "format": "text"
  },
  "birthDate": {
    "label": "Born",
    "format": "date"
  },
  "rank": {
    "label": "Rank",
    "format": "number"
  },
  "rankingPoints": {
    "label": "Points",
    "format": "number"
  },
  "elo": {
    "label": "Elo",
    "format": "number"
  },
  "eloHard": {
    "label": "Elo hard",
    "format": "number"
  },
  "eloClay": {
    "label": "Elo clay",
    "format": "number"
  },
  "eloGrass": {
    "label": "Elo grass",
    "format": "number"
  },
  "eloPeak": {
    "label": "Elo peak",
    "format": "number"
  },
  "matchesInDatabase": {
    "label": "Matches",
    "format": "number"
  },
  "photoUrl": {
    "label": "Photo",
    "format": "image"
  },
  "url": {
    "label": "Page",
    "format": "link"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Flashscore Tennis Scraper - Live Scores, Odds, H2H & History Actor is ready to run with the inputs already set for "Tennis player database: profiles with rank and Elo per surface". [Sign up or log in](https://console.apify.com/create-task-from-example/7y9DExxFrOq9iBdlo) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Tennis player database: profiles with rank and Elo per surface" task

The Flashscore Tennis Scraper - Live Scores, Odds, H2H & History Actor will start running based on the input automatically. You can also change all the input fields, like mode, sackmann file, and more, to match your specific use case.

### 3. Receive the "Tennis player database: profiles with rank and Elo per surface" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as player, full name, country, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Tennis player database: profiles with rank and Elo per surface" task via API](https://apify.com/crawlplant/flashscore-tennis.md).

## About this Actor

This example demonstrates how to use [Flashscore Tennis Scraper - Live Scores, Odds, H2H & History](https://apify.com/crawlplant/flashscore-tennis.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/crawlplant/flashscore-tennis.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

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 full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/crawlplant/flashscore-tennis.md

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`).