mfdaniels

Yea, there's lots of other charts, sources, notes that I could have included in the piece. The challenge is that this is not an academic study – it's Internet catnip. All the things that make studies too dense and boring to read (i.e., assumptions, abstractions and simplifications) are purposefully excluded.

I know that this undermines the credibility of the article, but I'm optimized for readability and storytelling, not to build a full-proof argument for timelessness. There's a million rabbit-holes that I could have gone down to make a much more solid case, but I decided to present the data and let the reader draw conclusions (kinda like I did with the hip hop/vocab piece: http://poly-graph.com/vocabulary.html).

I also realize that one could argue that this is a terrible way to approach a writing/data-analysis project. Assumptions and simplifications are important to highlight. But I weighed the options and decided to focus on accessibility.

Happy to discuss the pros/cons of this further :)

I should!

Sorry that there's so comments section.

The data is pretty clear in terms of source...Spotify in 2014...Billboard data via Whitburn.

The data was directly from one of Spotify's data partners.

Yea Spotify isn't a perfect indicator. This is the best proxy for present-day popularity that I can think of. I could have create an index that abstracted several data sources, but that would have killed the readability of the article.

Ah. Didn't realize that. Billboard data, generally, is terribly inaccurate in terms of a measure of cultural popularity...back in the day, they used to call radio stations to ask what their top 10 were. But it's the best we've got in terms of data.

Yup. I emailed Dorothy at the beginning of the project to get the Whitburn database.

Yea, Beatles, T Swift, Ac/dc are all not on Spotify. I preface that in the article.

It's all of Spotify. So while it's international data, it certainly skews toward western music.

I actually think that "longevity" is a better term. But I only realized that after I wrote the article. Here's another version of the article that never really was released, which focused on longevity over timeless: http://poly-graph.co/timeless/nodiggity.html

We only have two data points in this work: today and release date. So longer trends like the one you pointed out might be lost in time.

Honestly, I expect recency to be a large factor in Spotify plays. That is, we expect that the average hit from the 70s to have fewer plays than a hit from the 80s, and so on.

Regardless of the age of the listener, I expect newly released songs to have higher playcounts (in fact, I plotted this curve, but it was too high-brow for the Internet and the audience for which I was writing).

That said, if I managed to cut the data by age-bucket, I do think that the results would shift toward the music with which you grew up.

I made everything. Thanks!

I used D3 to create the charts, as well as some additional frameworks (Jquery, Waypoints).

Yea, the plan was to cut the data by age buckets that Spotify has (<18, 19-25, etc.). This, unfortunately, would have quintupled the size of the data pull, which was already close to a million requests.

The last point, Billboard vs. Spotify, is in the second to last chart. Check it out :)

Agree! In fact, I'd argue that a v2 of this project, where I look at the features of songs with high longevity (e.g., No Diggity) were driven by samples/covers/Glee/etc.

Yea, the plan was to remove counts via radio and playlists, but it would have doubled the amount of work from a data call/cleaning perspective. I'd bet that the results are still directionally accurate :)

author here, if there's any questions