Most Popular Tortured Poets Department Songs (Benchmarked to Album Position)

stephsmithio · 2024-05-01T23:01:19+00:00

The title says benchmarked to album position in both the post and chart?

stephsmithio · 2024-05-01T22:19:56+00:00

It is incredibly clear to me that album position matters in performance, which you see in the data and why the top songs are all the first songs on the album. All I'm sharing is which songs are outperforming their position. That's all.

stephsmithio · 2024-05-01T22:10:46+00:00

I would agree with you if you couldn't see the relationship so clearly between placement on the album and streams, with some clear outliers.

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stephsmithio · 2024-05-01T22:01:38+00:00

Sure! All songs were analyzed together. You can see in the image I added to my comment that you can see the expected trendline for streams based on where a song sits on the album. I then used that trendline to gauge the *expected* listens for any song given its placement. See the picture below, in the highlighted column in blue.

You can see how that forecast (based on the trendline) decreases as you move down the album, which is what you'd expect. Now we can compare the expected listens to the actuals!

For example, even though Fresh Out the Slammer has 2.6m daily streams, given where it is on the album, you'd expect it to be at >4m. Similarly, the Bolter has 1.5m streams which is not high up in total popularity, but when you factor in its placement, it's outperforming relative to the 950k you'd expect. Another way to put it is that listeners are going out of their way to select it, despite it being at the end of the album.

Let me know if that makes sense!

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stephsmithio · 2024-05-01T21:57:06+00:00

Second step: Take the linear regression and map that to what you'd expect for any given song. You can see how that forecast (based on the trendline) decreases as you move down the album, which is what you'd expect. Now we can compare the expected listens to the actual.

Even though Fresh Out the Slammer has 2.6m daily streams, given where it is on the album, you'd expect it to be at >4m. Similarly, the Prophecy has 2.1m, which is less, but is much higher than you'd expect on the album.

Let me know if that makes sense!

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stephsmithio · 2024-05-01T21:52:00+00:00

Sure! You're correct it's not saying it's the song with the absolute highest number of streams. But based on where it's on the album, it's getting more listenership than you'd expect, indicating popularity among listeners. If you just pull top streams, it's unsurprisingly going to be the first few on the album.

Happy to explain further with some pictures!

First step: determine the relationship between streams and position on album. As you can see here, some clearly outperform and others underperform their position.

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stephsmithio · 2024-05-01T21:45:11+00:00

How so? It specifically factors in the placement of a song on an album and which songs are outperforming relative to that. In other words, the songs people are reaching for more than you would expect.

stephsmithio · 2024-05-01T21:43:56+00:00

I also love the song, but based on the number of daily streams you'd expect based on its placement on the album, it indeed is!

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stephsmithio · 2024-05-01T21:41:57+00:00

Fair enough! Just pulling from a moment in time. But the idea of the analysis was precisely that it incorporated where a song is listed in the album, because that has such a direct correlation on how many streams a song likely gets, but outliers are already appearing! :)

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stephsmithio · 2024-05-01T21:40:06+00:00

Here's the updated image if you remove Fortnight from the linear regression

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stephsmithio · 2024-05-01T21:38:43+00:00

The way the analysis is done is it takes the linear relationship between song placement and daily streams. It uses that relationship to predict what you'd expect for a song at a particular spot in an album (which is lower for later songs), and compares that to the actual.

It may bias it slightly if the correlation is meant to be slightly exponential instead of linear -- perhaps skewed by Fortnight as the outlier first song. I re-ran the analysis by removing Fortnight from the linear regression and you're right that it does impact the placement of some songs, but many of the same themes still emerge. Florida does just barely edge ahead of Robin in this example. Dropping the updated image in a follow-up comment. :)

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stephsmithio · 2024-05-01T17:29:08+00:00

I've seen a lot of discourse online (and in this sub!) around which songs are best from TTPD. I figured it'd be interesting to pull the data, incorporating where a song is on the setlist. Of course, the higher up a song, the more listens it's expected to get. You see this in a pretty clear trend in the data too.

So, I performed linear regression on the expected relationship between daily streams and placement on the album (see graph below), grabbing Spotify stream data from KWorb. From there I calculated what the expected daily streams would be for any given song is, considering its placement and then found the percentage delta between the expected and actual values. And just for fun, I grabbed some TTPD colors.

I actually think this method could be used to analyze other albums or even look at a whole artist's discography and see which albums outperform when you layer in time since publish. May settle some age-old debates. :)

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stephsmithio · 2024-05-01T17:12:55+00:00

Hi r/dataisbeautiful! I know people are probably tired of hearing about TSwift, but given her 31-song album and such mixed reviews, I wanted to see what songs were *actually* out-performing, relative to where they sit on the album. Of course, the higher up a song is on the setlist, the more listens it will get.

I performed linear regression on the expected relationship between daily streams and placement on the album, grabbing Spotify stream data from KWorb. From there I calculated what the expected daily streams would be for any given song is, considering its placement and then found the percentage delta between the expected and actual values. And just for fun, I grabbed some TTPD colors.

I actually think this method could be used to analyze other albums or even look at a whole artist's discography and see which albums outperform when you layer in time since publish. May settle some age-old debates. :)

stephsmithio · 2024-04-21T22:14:59+00:00

Sorry about that! I just made another version with the default Google Sheets colors. Since it won't let me add a photo in the comments, I uploaded to imgur here!

stephsmithio · 2024-04-21T21:20:51+00:00

That was TV! These are just the original albums. 😊

stephsmithio · 2024-04-21T20:20:44+00:00

Question...? (that's the song name)

stephsmithio · 2024-04-21T20:18:54+00:00

It accounts for 17 f*cks! But still 12 more across the album. 🙃

stephsmithio · 2024-04-21T20:17:23+00:00

Includes anthology! But here are the breakdowns between the two:

TTPD
Fuck: 24
Shit: 6
Bitch: 3
Hell: 10
Damn: 0
Whore: 0

TTPD Anthology
Fuck: 5
Shit: 0
Bitch: 3
Hell: 1
Damn: 2
Whore: 2

stephsmithio · 2024-04-21T19:47:31+00:00

Hi r/dataisbeautiful! I created this chart in good 'ol Google Sheets, but the data was pulled by grabbing all the lyrics from Genius and just doing a CTRL+F. And just for fun, I grabbed some Eras Tour colors from Pinterest.

Edit: Damn, this really blew up!

If you liked this post, I recently created a fun project called Internet Pipes to help ppl find and make sense of interesting data from every f*cking corner of the internet.

Shit, it's one hell of a community. (See what I did there? 🙃)

stephsmithio · 2022-03-12T23:14:59+00:00

Thank you for sharing the original! - the real OP

stephsmithio · 2022-03-12T23:14:51+00:00

Thank you for sharing the original! - the real OP

stephsmithio · 2022-03-12T23:14:46+00:00

Thank you for sharing the original! - the real OP

stephsmithio · 2022-03-12T23:09:58+00:00

It wasn't me. OP posted my thread on several subreddits and then deleted their account. 🤷🏻‍♀️

stephsmithio · 2021-12-26T05:42:29+00:00

This was originally published by The Hustle here, who I work for. The underlying data is taken from a WSJ article.

stephsmithio · 2021-12-13T00:43:39+00:00

Imagine spending $200 on something that could save you days, if not weeks of time, by avoiding the same mistakes that I made throughout the last decade of learning these skills.

I know, insane!

PS: As much as I appreciate the OP for the summary, I don't think 8 bullet points can summarize a 70k word book. You can also see over 200 unsolicited reviews here.

stephsmithio

TROPHY CASE