# YouTube Outlier Finder (`vanity_arias/youtube-outlier-finder`) Actor Find a channel's outlier videos — recent uploads scored against the channel's own median views. See instantly which videos overperformed (2x, 10x, 50x baseline) for content research, title & thumbnail studies, and trend hunting. No API key, no quota. - **URL**: https://apify.com/vanity\_arias/youtube-outlier-finder.md - **Developed by:** [Nvikelo Nyathi](https://apify.com/vanity_arias) (community) - **Categories:** Social media, Marketing, Videos - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. 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). # README ## YouTube Outlier Finder Point it at any YouTube channel, get back the channel's recent videos **scored against its own median views** — so you can see at a glance which videos overperformed (2x, 10x, 50x the channel's normal) and which flopped. Outliers are the single strongest signal in content research: a video that does 10x its channel's baseline earned that reach through **packaging** (idea + title + thumbnail), not subscriber count. That's the thing worth studying and adapting. ### Use cases - **Content research** — find what's actually working in your niche right now, normalized for channel size. - **Title & thumbnail studies** — build swipe files from proven overperformers, not lucky big channels. - **Competitor analysis** — see which of a competitor's bets paid off and which flopped. - **Small-channel scouting** — a 5k-sub channel with a 40x outlier is a trend alarm no view-sorted list will show you. ### Input ```json { "channel": "@MrBeast", "maxVideos": 30, "minMultiplier": 0 } ```` | Field | Type | Notes | |-------|------|-------| | `channel` | string (required) | Handle (`@MrBeast`), channel ID (`UC…`), or any channel URL. | | `maxVideos` | integer | Recent videos to analyze, up to ~30 (one page of the Videos tab). Default 30. | | `minMultiplier` | integer | Only return videos at/above this multiplier. `0` (default) returns every analyzed video with its score. | | `proxyConfiguration` | object | Preconfigured — residential proxy is used automatically. | ### Output Dataset items sorted by multiplier, highest first: ```json { "videoId": "iYlODtkyw_I", "url": "https://www.youtube.com/watch?v=iYlODtkyw_I", "title": "Survive 30 Days Chained To A Stranger, Win $250,000", "views": 50000000, "publishedText": "4 days ago", "channelBaseline": 142000000, "multiplier": 0.35 } ``` - `channelBaseline` — the channel's median views across the analyzed videos. - `multiplier` — this video's views ÷ baseline. **≥2 is an outlier; ≥10 is a monster.** ### How it works Fetches the channel's **Videos** tab through Apify residential proxy and parses the embedded page data — no browser, no YouTube API key, no quota. View counts are YouTube's compact numbers ("1.2M"), which is plenty of precision for multiplier math. ### Limitations (honest ones) - Analyzes the **~30 most recent longform uploads** (one page, no scroll). Shorts live on a separate tab and are not included. - Baseline is the median of those same recent videos — for channels that recently blew up, the baseline reflects the new normal, not their history. - One run = one channel. Loop over channels to scan a niche. # Actor input Schema ## `channel` (type: `string`): YouTube channel — handle (`@MrBeast`), channel ID (`UC…`), or any channel URL. ## `maxVideos` (type: `integer`): How many recent videos to analyze (up to ~30 — one page of the Videos tab). ## `minMultiplier` (type: `integer`): Only return videos at or above this views-vs-baseline multiplier. 0 = return every analyzed video with its score. ## `proxyConfiguration` (type: `object`): Apify Proxy — residential is used automatically (YouTube blocks datacenter IPs). You normally don't need to touch this. ## Actor input object example ```json { "channel": "@MrBeast", "maxVideos": 30, "minMultiplier": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "channel": "@MrBeast" }; // Run the Actor and wait for it to finish const run = await client.actor("vanity_arias/youtube-outlier-finder").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "channel": "@MrBeast" } # Run the Actor and wait for it to finish run = client.actor("vanity_arias/youtube-outlier-finder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "channel": "@MrBeast" }' | apify call vanity_arias/youtube-outlier-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vanity_arias/youtube-outlier-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "YouTube Outlier Finder", "description": "Find a channel's outlier videos — recent uploads scored against the channel's own median views. See instantly which videos overperformed (2x, 10x, 50x baseline) for content research, title & thumbnail studies, and trend hunting. No API key, no quota.", "version": "0.1", "x-build-id": "O7A8vqVMcBWgdljL1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vanity_arias~youtube-outlier-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vanity_arias-youtube-outlier-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/vanity_arias~youtube-outlier-finder/runs": { "post": { "operationId": "runs-sync-vanity_arias-youtube-outlier-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/vanity_arias~youtube-outlier-finder/run-sync": { "post": { "operationId": "run-sync-vanity_arias-youtube-outlier-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "channel" ], "properties": { "channel": { "title": "Channel", "type": "string", "description": "YouTube channel — handle (`@MrBeast`), channel ID (`UC…`), or any channel URL." }, "maxVideos": { "title": "Max videos", "minimum": 3, "maximum": 30, "type": "integer", "description": "How many recent videos to analyze (up to ~30 — one page of the Videos tab).", "default": 30 }, "minMultiplier": { "title": "Min multiplier", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return videos at or above this views-vs-baseline multiplier. 0 = return every analyzed video with its score.", "default": 0 }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Apify Proxy — residential is used automatically (YouTube blocks datacenter IPs). You normally don't need to touch this.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```