# DFS Props Monitor — Player Prop Lines & Movers (`brick_joey_yto/dfs-props-monitor`) Actor

Keyless Underdog Fantasy data: 11k+ live player prop lines with odds, line-movement tracking, and watchlist alerts — scheduled DFS intel for prop bettors.

- **URL**: https://apify.com/brick_joey_yto/dfs-props-monitor.md
- **Developed by:** [William Billings](https://apify.com/brick_joey_yto) (community)
- **Categories:** Developer tools, Automation, Business
- **Features:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## DFS Props Monitor — Player Prop Lines & Movers

**The props market as a scheduled feed** — keyless Underdog Fantasy data: **11,000+ live player prop lines** with over/under odds, **line-movement tracking** (the steam signal), and player watchlists. No login — $0.001/row.

### Who it's for

- **DFS players & prop bettors** — the full prop board as data: lines, odds, and **which props moved** (steam = sharp action) before game time
- **Props tools & bots** — a structured feed for line-comparison products (the Outlier/PropsCash data class)
- **Sports analysts** — player props, matchups, and live-event lines per sport

### Modes

| Mode | What you get |
|---|---|
| **props** | All live over/under lines — player, stat, line, odds, matchup, live-event flag |
| **players** | Player directory — names, positions, sports |
| **games** | Today's games — matchups, start times, scores |
| **movers** | **Line-movement diffs** — pass your prior snapshot, get only the props that moved |
| **watch** | Props filtered to your players/sport — **schedule pre-game** |

### Example output

```json
{
  "recordType": "prop", "player": "Patrick Mahomes",
  "matchup": "KC @ BUF", "stat": "passing_yards", "lineValue": 287.5,
  "overPrice": "-115", "underPrice": "-105", "liveEvent": false
}
```

### Example inputs

```json
{ "mode": "props", "sport": "nfl", "limit": 50 }
{ "mode": "movers", "prevSnapshot": [{"player":"Mahomes","stat":"passing_yards","lineValue":285.5}] }
{ "mode": "watch", "player": "ohtani", "sport": "mlb" }
```

### Pricing

$0.001 per row — no subscription.

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# Actor input Schema

## `mode` (type: `string`):

props=live lines; players=player list; games=today's games; movers=line diffs; watch=filtered props

## `sport` (type: `string`):

Filter to a sport (nba, nfl, mlb, nhl, soccer...)

## `player` (type: `string`):

Filter to a player name (lebron, mahomes, ohtani...)

## `leagueId` (type: `integer`):

Underdog league_id filter — leave blank for all sports

## `prevSnapshot` (type: `array`):

movers: prior props rows to diff for line movement

## `limit` (type: `integer`):

Max rows per feed

## Actor input object example

```json
{
  "mode": "props",
  "limit": 100
}
```

# Actor output Schema

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("brick_joey_yto/dfs-props-monitor").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("brick_joey_yto/dfs-props-monitor").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 '{}' |
apify call brick_joey_yto/dfs-props-monitor --silent --output-dataset

```

## MCP server setup

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

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/8XEvmRxijlBI3Ghr2/builds/L8SHMuXfMBPTAzBKH/openapi.json