# Sleeper Player Props Scraper — Lines, Multipliers & Hit Rates (`hipersoft/sleeper-props-scraper`) Actor

Scrape live Sleeper player props: player, team, stat, line, over/under payout multipliers, pick popularity, recent game results, average and over hit rate. NFL, NBA, MLB, NHL, college football, WNBA, soccer, tennis, MMA and esports.

- **URL**: https://apify.com/hipersoft/sleeper-props-scraper.md
- **Developed by:** [hiper soft](https://apify.com/hipersoft) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.00015 / prop line

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

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 examples already wired to this Actor's own input schema, see the [API](#api) section below.

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`).

# README

## Sleeper Player Props Scraper: Lines, Payout Multipliers & Hit Rates

**Sleeper Player Props Scraper** collects **live player prop lines from Sleeper** across the **NFL, NBA, MLB, NHL, college football and basketball, WNBA, soccer, tennis, MMA and esports**. For every prop you get the **player, team, position, stat, line, over and under payout multipliers, pick popularity**, plus the **player's recent game results for that stat, recent average and over hit rate**. Export to **CSV, Excel or JSON** for DFS research, line shopping and betting models.

### What does it do?

- 🏈 **All Sleeper sports** in one run, or filter by sport.
- 📏 **Lines and payouts**: the line and the over and under payout multipliers.
- 🔥 **Popularity**: how often a pick is taken, where available.
- 📈 **Recent performance**: the last games' results for the stat, with average and over hit rate, so you can spot value fast.
- 🔎 **Filters**: stat type (points, passing_yards, shots…) and player name.

### What data can you scrape?

| Field | Description |
|---|---|
| `sport` | Sport or league |
| `player`, `playerId`, `team`, `position` | Player |
| `stat` | Stat type (points, rebounds, receiving_yards, shots…) |
| `line` | Line value |
| `overMultiplier`, `underMultiplier` | Payout multipliers |
| `lineType` | Normal, boosted, etc. |
| `popularity`, `picks`, `overPicks`, `underPicks` | Pick popularity, where available |
| `recentPerformance` | Recent games: date, opponent, value |
| `recentAverage`, `recentHitRateOver` | Recent average and % of games over the line |
| `gameId`, `gameStatus`, `season`, `seasonType` | Game context |
| `updatedAt` | Last line update |

### Use cases

- **DFS and pick'em research**: find props where recent form beats the line.
- **Line comparison**: compare Sleeper lines with sportsbooks and other pick'em apps.
- **Betting models**: payout multipliers and recent stats as model features.
- **Content and alerts**: publish daily top props and boosted lines.

### How to use it

1. Click **Try for free** to open the scraper in Apify Console.
2. Pick **sports** and optionally **stat types** or a **player name**.
3. Click **Start** and download the props as **CSV, Excel or JSON**.

### Input example

```json
{
  "sports": ["nfl", "nhl"],
  "statTypes": ["receiving_yards", "shots"],
  "includePlayerNames": true,
  "includeRecentPerformance": true
}
```

### Output example

```json
{
  "sport": "nhl",
  "player": "Blake Coleman",
  "team": "MIN",
  "position": "LW",
  "stat": "assists",
  "line": 0.5,
  "overMultiplier": 3.23,
  "underMultiplier": 1.19,
  "gameStatus": "pre_game",
  "recentPerformance": [{ "date": "2026-10-01", "opponent": "NSH", "value": 0 }],
  "recentAverage": 0,
  "recentHitRateOver": 0,
  "updatedAt": "2026-10-06T07:43:01.563Z"
}
```

### How much does it cost?

The scraper uses **pay-per-line** pricing: a tiny fixed price per prop line, with automatic discounts on higher Apify plans. A full snapshot of all live lines usually costs well under a dollar. See the **Pricing** tab for the exact price.

### More betting and sports data

- [Action Network Odds Scraper](https://apify.com/hipersoft/actionnetwork-odds-scraper): sportsbook lines and public betting splits.
- [TennisExplorer Scraper](https://apify.com/hipersoft/tennisexplorer-scraper): tennis results and odds.

### FAQ

**How often do lines change?**
Constantly. Schedule the scraper (e.g. every 15 minutes) to track line movement.

**Are player names included?**
Yes, with **Include player names** on.

**What formats can I export?**
CSV, Excel, JSON, XML, HTML and RSS, or read the results through the Apify API.

**Can I integrate it with other apps?**
The scraper can be connected with almost any cloud service or web app thanks to [integrations on the Apify platform](https://apify.com/integrations). It works with [Make](https://apify.com/integrations/make), [Zapier](https://apify.com/integrations/zapier), [Slack](https://docs.apify.com/platform/integrations/slack), [Airbyte](https://docs.apify.com/platform/integrations/airbyte), [GitHub](https://docs.apify.com/platform/integrations/github), [Google Drive](https://docs.apify.com/platform/integrations/drive) and [many more](https://apify.com/integrations), plus the [Apify API](https://docs.apify.com/api/v2), JS/Python clients and MCP. Or use [webhooks](https://docs.apify.com/platform/integrations/webhooks) to trigger an action whenever a run finishes.

**Is it legal?**
The scraper collects publicly displayed prop lines. You are responsible for complying with Sleeper's terms and your local gaming laws.

### Related Actors

- [Action Network Odds Scraper](https://apify.com/hipersoft/actionnetwork-odds-scraper)

### Notes

Original clean-room implementation. Returns only public data. Not affiliated with Sleeper.

# Actor input Schema

## `sports` (type: `array`):

Only props for these sports. Leave empty for all.

## `statTypes` (type: `array`):

Only these stats, e.g. points, rebounds, passing_yards, receiving_yards, anytime_touchdowns, shots, hits, strikeouts. Leave empty for all.

## `playerNameContains` (type: `string`):

Only props for players whose name contains this text.

## `includePlayerNames` (type: `boolean`):

Resolve player IDs to full names.

## `includeRecentPerformance` (type: `boolean`):

Add the player's recent game results for the stat, the recent average and how often they went over the line.

## `maxItems` (type: `integer`):

Stop after this many lines. 0 means all live lines.

## `proxyConfiguration` (type: `object`):

Not required for normal use.

## Actor input object example

```json
{
  "sports": [
    "nfl",
    "nba"
  ],
  "includePlayerNames": true,
  "includeRecentPerformance": true,
  "maxItems": 0,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

The results as dataset items.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "sports": [
        "nfl",
        "nba"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("hipersoft/sleeper-props-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "sports": [
        "nfl",
        "nba",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("hipersoft/sleeper-props-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "sports": [
    "nfl",
    "nba"
  ]
}' |
apify call hipersoft/sleeper-props-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,hipersoft/sleeper-props-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ovRMjdssOI6aVYnT1/builds/a0pfrToXhdJOPWiGt/openapi.json