Ben Thompson on Ben Thompson

Applying PageRank’s ideas to identify the top 10 Stratechery Articles, Daily Updates, and Exponent podcasts

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Why this was written (adapted from

Ben Thompson writes a lot. Across articles on Stratechery, Daily Updates, and his Exponent podcast with James Allworth, he has made 931 distinct pieces of content to date, not including frequent guest appearances on The Talk Show and This Week In Tech. And that is only his tech stuff!

Why We Need PageRank for Stratechery

This can make things very daunting for anyone new to Ben’s world and trying to hang on to the most important threads.

For these people Ben has self-curated yearly reviews with the top pieces of content and a short summary:

2016 | 2015 Big Five | 2015 | 2014 | 2013

However, apart from simple web traffic, Ben has never had sufficient data to produce a better curation algorithm than his own selections. As Ben says in his piece, “curation works best when there is a good amount of data, but not too much.” Ben’s own body of work has now started to be “too much” for beginners to handle.

Fortunately Ben also quotes himself a lot. For anyone familiar with Google’s PageRank this presents an immediate opportunity to create a network graph where relative importance of articles is an emergent feature.

Unfortunately, because there is not much hierarchy in these links, a typical network graph fails to produce anything useful:

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I realize I could clean this up with more work but ¯\_(ツ)_/¯

So we must employ more quantitative methods here. For this essay we simply count Ben’s own backlinks to himself and don’t go for the full iterative PageRank because we found that iteration did not add anything meaningful for our purposes (which is purely ranking source nodes, not creating a search engine of everything Ben has ever linked to). PageRank also faces a well known problem with older pages naturally having more of an opportunity to receive a higher rank, which we address by scaling the rank by the square root of time to introduce an exponential decay factor.

Ben’s Top 10 Stratechery Articles

This allows us to produce an adjusted rank for every post, which when sorted throws these up as the top 10 Ben Thompson posts:

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This is taken from my original post; the medium of Medium doesn’t allow me to post tables
  1. Aggregation Theory
  2. The Facebook Epoch
  3. Peak Google
  4. What Clayton Christensen Got Wrong
  5. Manifestos and Monopolies
  6. Popping the Publishing Bubble
  7. The Amazon Tax
  8. Facebook and the Cost of Monopoly
  9. The Great Unbundling
  10. Publishers and the Smiling Curve

No prizes for guessing that articles like Aggregation Theory and The Facebook Epoch top the list, but equally interesting that the Adj. Rank formula also throws up 2017’s Manifestos and Monopolies and Facebook and the Cost of Monopoly as important. These rankings won’t last forever; for them to hold up over time, Ben will have to continue to link to them as an anchor for his future pieces.

Plotting these over time, we can see that Ben has created a wealth of content that has remained evergreen, but great articles are particularly clustered around 2016 and on media topics like Facebook, Twitter/Snapchat, and Publishing:

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congratulations, you found the lede


I know this was a simple exercise but I was curious enough to do the initial number crunching and decided to publish it since it might be helpful to other new Ben Thompson fans.

I would like to thank Ben for allowing me to index the data needed for this project.

This work could be turned into a standing site index if there is enough interest.

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Infinite Builder working on DX @Netlify. Helping people #LearnInPublic @nycreact @eggheadio & r/reactjs. He/him 🇸🇬

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