# NFL Playoff Odds - All 32 Teams

**Use case:** 

Every NFL team's chance of reaching the postseason, winning its division, taking a wild card and finishing as the number one seed. The numbers come from a Monte Carlo simulation that replays every remaining game on the real schedule tens of thousands of times, so they are model output rather than a scraped page. Run it as it is, or ask the same question in plain language through the MCP server.

## Input

```json
{
  "sport": "nfl",
  "league": "eng.1",
  "metric": "playoff",
  "limit": 32,
  "holdSeconds": 0
}
```

## Output

```json
{
  "rank": {
    "label": "Rank",
    "format": "integer"
  },
  "team": {
    "label": "Team",
    "format": "string"
  },
  "abbreviation": {
    "label": "Abbreviation",
    "format": "string"
  },
  "probabilities.playoff": {
    "label": "Playoffs",
    "format": "number"
  },
  "probabilities.division": {
    "label": "Division",
    "format": "number"
  },
  "probabilities.wildCard": {
    "label": "Wild card",
    "format": "number"
  },
  "probabilities.topSeed": {
    "label": "Top seed",
    "format": "number"
  },
  "probabilities.title": {
    "label": "Title",
    "format": "number"
  },
  "probabilities.top4": {
    "label": "Top four",
    "format": "number"
  },
  "probabilities.qualify": {
    "label": "Continental qualification",
    "format": "number"
  },
  "probabilities.relegation": {
    "label": "Relegation",
    "format": "number"
  }
}
```

## About this Actor

This example demonstrates how to use [Sports Probabilities MCP - NFL, MLB & Soccer Odds for AI Agents](https://apify.com/commodus67/sports-probabilities-mcp.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/commodus67/sports-probabilities-mcp.md) to learn more, explore other use cases, and run it yourself.


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

- **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 full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/commodus67/sports-probabilities-mcp.md

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).