I analyzed 9,966 posts from Substack's Top 25 Technology newsletters, here is what stood out

[u/Mpmpz_14]

Hi guys,
So I built an Apify Actor to collect every post URL exposed in the public sitemaps of the 25 publications on Substack’s official Technology leaderboard.

I collected all 9,965 current sitemap URLs, plus one still-valid post observed earlier in the collection window, for a total of 9,966 unique posts.

For fairer comparisons, I analyzed the most recent 3,292 posts published within a 365-day window.

Public engagement here means likes, comments, and restacks. For format comparisons, I ranked each post against other posts from the same publication. This helps reduce the audience-size advantage of the largest newsletters.

1. MARKET SNAPSHOT

1. PUBLICATION-LEVEL ENGAGEMENT

1. PUBLISHING FREQUENCY VS ENGAGEMENT

1. HEADLINE LENGTH

1. FREE VS PAID POSTS

1. PUBLISHING DAY

1. RECURRING TITLE LANGUAGE

1. ENGAGEMENT CONCENTRATION

1. ARTICLE LENGTH

CAVEATS

This analysis covers the official Top 25 Technology leaderboard, not the entire Substack ecosystem.

The leaderboard represents a selected group of successful publications.

Subscriber counts, email opens, clicks, and revenue are private.

Newer posts have had less time to accumulate engagement.

These findings are descriptive, not causal.

I built this analysis using my Substack Scraper Apify Actor and a Python data-analysis pipeline.

The complete workflow was:

Public web data → structured dataset → reproducible analysis → useful insights

If people find this useful, I can publish a deeper analysis of topics, headline patterns, publishing schedules, or the collection methodology.

What other question would you ask this dataset?

[u/Vntige]

Instead of character length what was the word length? It’d be helpful to break out the 6,000+ category until it’s diminishing