yt-outlier

YouTube outlier analysis identifies viral patterns by computing outlier scores from recent video metrics and channel subscriber counts. This surfaces title formats and topics that significantly outperform a channel's average engagement for data-driven content ideation.

An outlier score measures video performance against a channel's baseline using recent video data and subscriber counts. It quantifies how much a specific video outperforms the channel average, helping you identify viral title formats and topics.

Niche validation involves scraping recent competitor videos, computing outlier scores, and categorizing patterns that outperform channel averages. This reveals which video formats and topics drive engagement in a specific niche before you create content.

You need yt-dlp to scrape recent YouTube video data for outlier analysis. Apify is an optional dependency that enables broader scraping capabilities when available, but yt-dlp handles the core data aggregation and filtering requirements.

Channel benchmarking compares viral patterns across channels, niches, and time windows using outlier scores. By aggregating scraped video metrics, you can identify which title formats consistently outperform averages across multiple channels in your niche.

Using yt-dlp for competitor research limits analysis to recent video data it can scrape directly. For broader historical scraping or deeper channel metrics, you need the optional Apify integration to gather comprehensive data beyond yt-dlp's standard capabilities.