# Challenger tennis odds dataset: ATP Challenger 2025 results

**Use case:** 

ATP Challenger singles of 2025 with the result and the bookmaker odds, opening and closing, where priced, plus the fair win probability and Elo. A market the major odds files skip; the example returns 500 matches, set Max items higher for more.

## Input

```json
{
  "years": [
    2025
  ],
  "tours": [
    "challenger-men"
  ],
  "dateFrom": "-60",
  "dateTo": "today",
  "oddsCountry": "GB",
  "maxItems": 500
}
```

## Output

```json
{
  "tourney_date": {
    "label": "Date",
    "format": "text"
  },
  "tourney_name": {
    "label": "Tournament",
    "format": "text"
  },
  "surface": {
    "label": "Surface",
    "format": "text"
  },
  "round": {
    "label": "Round",
    "format": "text"
  },
  "winner_name": {
    "label": "Winner",
    "format": "text"
  },
  "winner_rank": {
    "label": "Winner rank",
    "format": "number"
  },
  "loser_name": {
    "label": "Loser",
    "format": "text"
  },
  "loser_rank": {
    "label": "Loser rank",
    "format": "number"
  },
  "score": {
    "label": "Score",
    "format": "text"
  },
  "winner_elo": {
    "label": "Winner Elo",
    "format": "number"
  },
  "loser_elo": {
    "label": "Loser Elo",
    "format": "number"
  },
  "B365W": {
    "label": "B365 winner",
    "format": "number"
  },
  "B365L": {
    "label": "B365 loser",
    "format": "number"
  },
  "MaxW": {
    "label": "Max winner",
    "format": "number"
  },
  "MaxL": {
    "label": "Max loser",
    "format": "number"
  },
  "AvgW": {
    "label": "Avg winner",
    "format": "number"
  },
  "AvgL": {
    "label": "Avg loser",
    "format": "number"
  },
  "OpenAvgW": {
    "label": "Opening avg winner",
    "format": "number"
  },
  "OpenAvgL": {
    "label": "Opening avg loser",
    "format": "number"
  },
  "ProbW": {
    "label": "Fair prob. winner",
    "format": "number"
  },
  "ProbL": {
    "label": "Fair prob. loser",
    "format": "number"
  },
  "BookmakerCount": {
    "label": "Bookmakers",
    "format": "number"
  },
  "OddsCountry": {
    "label": "Odds country",
    "format": "text"
  }
}
```

## How it works

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

The Tennis Odds Dataset - ATP & WTA Closing Odds, Results & Elo Actor is ready to run with the inputs already set for "Challenger tennis odds dataset: ATP Challenger 2025 results". [Sign up or log in](https://console.apify.com/create-task-from-example/1mR2ryhJT8vGm4Cbk) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Challenger tennis odds dataset: ATP Challenger 2025 results" task

The Tennis Odds Dataset - ATP & WTA Closing Odds, Results & Elo Actor will start running based on the input automatically. You can also change all the input fields, like years, tours, and more, to match your specific use case.

### 3. Receive the "Challenger tennis odds dataset: ATP Challenger 2025 results" 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 date, tournament, surface, 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 "Challenger tennis odds dataset: ATP Challenger 2025 results" task via API](https://apify.com/crawlplant/tennis-odds-dataset.md).

## About this Actor

This example demonstrates how to use [Tennis Odds Dataset - ATP & WTA Closing Odds, Results & Elo](https://apify.com/crawlplant/tennis-odds-dataset.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/crawlplant/tennis-odds-dataset.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-odds-dataset.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`).