# Tennis point-by-point CSV, Sackmann format: last 7 days

**Use case:** 

Finished ATP and WTA matches of the last 7 days in the columns of Jeff Sackmann's tennis_pointbypoint files: pbp_id, both servers, the whole match as one S/R string, score and minutes, each string checked against the final score. The example returns up to 300 matches; set Max items higher for more.

## Input

```json
{
  "mode": "matches",
  "dateFrom": "-7",
  "dateTo": "yesterday",
  "matchIds": [],
  "tournamentUrls": [],
  "years": [],
  "playerUrls": [],
  "tours": [
    "atp",
    "wta"
  ],
  "matchType": "singles",
  "status": "finished",
  "tournaments": [],
  "players": [],
  "includeStatistics": true,
  "pointByPointFormat": "sackmann",
  "maxItems": 300
}
```

## Output

```json
{
  "pbp_id": {
    "label": "Match",
    "format": "text"
  },
  "date": {
    "label": "Date",
    "format": "text"
  },
  "tny_name": {
    "label": "Tournament",
    "format": "text"
  },
  "tour": {
    "label": "Tour",
    "format": "text"
  },
  "draw": {
    "label": "Draw",
    "format": "text"
  },
  "server1": {
    "label": "Server 1",
    "format": "text"
  },
  "server2": {
    "label": "Server 2",
    "format": "text"
  },
  "winner": {
    "label": "Winner (1 or 2)",
    "format": "number"
  },
  "pbp": {
    "label": "Points",
    "format": "text"
  },
  "score": {
    "label": "Score",
    "format": "text"
  },
  "wh_minutes": {
    "label": "Minutes",
    "format": "number"
  }
}
```

## How it works

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

The Tennis Point-by-Point Data - Every Game & Point Actor is ready to run with the inputs already set for "Tennis point-by-point CSV, Sackmann format: last 7 days". [Sign up or log in](https://console.apify.com/create-task-from-example/yTdrb95B5llz3WO5U) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Tennis point-by-point CSV, Sackmann format: last 7 days" task

The Tennis Point-by-Point Data - Every Game & Point Actor will start running based on the input automatically. You can also change all the input fields, like mode, from day (utc), and more, to match your specific use case.

### 3. Receive the "Tennis point-by-point CSV, Sackmann format: last 7 days" 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 match, date, tournament, 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 point-by-point CSV, Sackmann format: last 7 days" task via API](https://apify.com/crawlplant/tennis-point-by-point.md).

## About this Actor

This example demonstrates how to use [Tennis Point-by-Point Data - Every Game & Point](https://apify.com/crawlplant/tennis-point-by-point.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/crawlplant/tennis-point-by-point.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/tennis-point-by-point.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`).