# NBA Team ATS & Over/Under Results 2026-27

**Use case:** 

Every NBA team game this season with the closing spread and total, plus whether the team covered (ATS) and whether the game went over or under. Includes score, opponent, home/away and team box score stats. Build ATS records, betting trend tables or models, and export to CSV, Excel or JSON.

## Input

```json
{
  "dataType": "teamGameLogs",
  "season": "2026-27",
  "daysBack": 3,
  "daysAhead": 7,
  "seasonTypes": [
    "regular",
    "playin",
    "postseason"
  ],
  "minGames": 1,
  "propMarkets": "main",
  "includeOdds": true,
  "onlyNewGames": false,
  "maxItems": 2000,
  "maxConcurrency": 6
}
```

## Output

```json
{
  "gameDateET": {
    "label": "Game date et"
  },
  "team": {
    "label": "Team"
  },
  "opponent": {
    "label": "Opponent"
  },
  "homeAway": {
    "label": "Home away"
  },
  "teamScore": {
    "label": "Team score"
  },
  "opponentScore": {
    "label": "Opponent score"
  },
  "result": {
    "label": "Result"
  },
  "spread": {
    "label": "Spread"
  },
  "total": {
    "label": "Total"
  },
  "moneyline": {
    "label": "Moneyline"
  },
  "atsResult": {
    "label": "Ats result"
  },
  "ouResult": {
    "label": "Ou result"
  }
}
```

## How it works

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

The NBA Stats Scraper - Box Scores, Game Logs, Player Props & Odds Actor is ready to run with the inputs already set for "NBA Team ATS & Over/Under Results 2026-27". [Sign up or log in](https://console.apify.com/create-task-from-example/9TafweY5tVVcGWz59) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "NBA Team ATS & Over/Under Results 2026-27" task

The NBA Stats Scraper - Box Scores, Game Logs, Player Props & Odds Actor will start running based on the input automatically. You can also change all the input fields, like what data do you want?, season, and more, to match your specific use case.

### 3. Receive the "NBA Team ATS & Over/Under Results 2026-27" 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 game date et, team, opponent, 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 "NBA Team ATS & Over/Under Results 2026-27" task via API](https://apify.com/datawright/nba-stats-scraper.md).

## FAQ

### Where do the spreads and totals come from?

The betting lines shown on ESPN's game pages (DraftKings). Each row has the spread, the total and the cover and over/under result.

### Can I get past seasons?

Yes. Change Season to 2025-26 or earlier to pull a full past season of ATS and over/under results.

### How do I keep it updated?

Schedule the task daily. Each run picks up the latest results so your ATS table stays current.

## About this Actor

This example demonstrates how to use [NBA Stats Scraper - Box Scores, Game Logs, Player Props & Odds](https://apify.com/datawright/nba-stats-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/datawright/nba-stats-scraper.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/datawright/nba-stats-scraper.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`).