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How the Outlier Score works — the math, the limits, the wins

MC
Maya Chen
May 8, 2026 · 8,247 reads
[ HERO · PLAYBOOK ]gpt-image-2 prompt: "editorial photo for blog post about how the outlier score works — the math, the limits, the wins — dark moody background, neon lime accent, GenZ aesthetic, 16:9"

Every score is views ÷ creator median × 100. We explain why this beats raw view counts, the edge cases, and how to read it.

The math

Outlier Score = (video views / creator's 30-day median views) × 100.

That's it. A score of 100 means the video performed exactly at the creator's recent average. 500 means 5× over. 1000+ is a genuine breakout.

Why this beats raw view counts

Raw view counts measure audience size, not signal. A creator with 2M followers averaging 800K views per post who hits 1M with a new format isn't doing anything special — that's noise. A creator with 8K followers averaging 2K views who lands a 60K-view post is doing 30× better by signal-to-baseline.

The Outlier Score asks: did this format work, given the audience this creator has built? That's the question content strategists actually care about.

The limits

Three things to know:

  • Cold-start creators (< 5 videos in 30d) get score = 0. We don't have a stable baseline yet.
  • Median, not mean — one viral hit doesn't poison the baseline. The 6th-best video in 30 days is the reference point.
  • Posted-at sensitivity — a video that's only been live 2 hours is still rolling up. We re-score on every scrape cycle.

How to read it in practice

A 200+ score on a creator over 100K followers is a real format signal.
A 600+ score on a creator under 10K followers is a great hook with limited reach.
A 1000+ score on a verified account is a "stop everything, study this" moment.

Stack the score next to follower count and 7d engagement split (likes/comments/saves) and you've got a much sharper read than what the native discover tab gives you.

Pull this data yourself.

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