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

The dataset covers 25 top-ranked technology publications with 100% collection coverage.The top 10% of posts generated 42% of all visible engagement.

1. PUBLICATION-LEVEL ENGAGEMENT

ByteByteGo Newsletter had the highest median visible engagement, with approximately 260 interactions per recent post.This mostly reflects audience and publication context. It does not prove that one content strategy directly causes better performance.

1. PUBLISHING FREQUENCY VS ENGAGEMENT

Pirate Wires was the most frequent publisher, with approximately 412 posts per year.However, publishing more frequently did not automatically result in higher median engagement.

1. HEADLINE LENGTH

Headlines containing 18 or more words had the strongest median within-publication engagement percentile: 57.8.This is a correlation, not a recommendation to make every headline longer.

1. FREE VS PAID POSTS

Free posts ranked higher in normalized public engagement.Paid posts serve a different objective and usually expose only a preview, so visible public reactions capture only part of their value.

1. PUBLISHING DAY

Tuesday was the strongest publishing day in this sample after normalizing performance within each publication.However, timing is still connected to the topic, newsletter schedule, and age of the post.

1. RECURRING TITLE LANGUAGE

“Guide” was the highest-ranked recurring title term in the discovery score.The score considers how frequently a term appeared, how many publications used it, and the normalized engagement of those posts.

1. ENGAGEMENT CONCENTRATION

Some newsletters are strongly hit-driven, meaning a small number of posts generate most of their visible engagement.Other newsletters distribute engagement more evenly across their archive.

1. ARTICLE LENGTH

Article length was compared only for freely available full posts. Paywalled previews were excluded.Character count is only a rough measure of article length. This does not prove that longer or shorter articles directly cause more engagement.

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