“Music was better back then”: When do we stop keeping up with popular music?

After sixty years of research, it’s conventional wisdom: as people get older, they stop keeping up with popular music. Whether the demands of parenthood and careers mean devoting less time to pop culture, or just because they’ve succumbed to good old-fashioned taste freeze, music fans beyond a certain age seem to reach a point where their tastes have “matured”.

That’s why the organizers of the Super Bowl — with a median viewer age of 44 —  were smart to balance their Katy Perry-headlined halftime show with a showing by Missy Elliott.

Missy don't brag, she mostly boast
Missy don’t brag, she mostly boast

Spotify listener data offers a sliced & diced view of each user’s streams. This lets us measure when this effect begins, how quickly the effect develops, and how it’s impacted by demographic factors.

For this study, I started with individual listening data from U.S. Spotify users and combined that with Echo Nest artist popularity data, to generate a metric for the average popularity of the artists a listener streamed in 2014. With that score per user, we can compute a median across all users of a specific age and demographic profile.

What I found was that, on average…

  • … while teens’ music taste is dominated by incredibly popular music, this proportion drops steadily through peoples’ 20s, before their tastes “mature” in their early 30s.
  • … men and women listen similarly in their their teens, but after that, men’s mainstream music listening decreases much faster than it does for women.
  • … at any age, people with children (inferred from listening habits) listen to a smaller amounts of currently-popular music than the average listener of that age.

Personified, “music was better in my day” is a battle being fought between 35-year old fathers and teen girls — with single men and moms in their 20s being pulled in both directions.



Spotify creates a “Taste Profile” for every active user, an internal tool for personalization that includes us how many times a listener has streamed an artist. Separately, we can marry that up to each artist’s popularity rank from The Echo Nest (via artist “hotttnesss”).

To give you an idea of how popularity rank scales, as of January 2015:

  • Taylor Swift had a popularity rank of #1
  • Eminem had a popularity rank of about #50
  • Muse had a popularity rank of about #250
  • Alan Jackson had a popularity rank of about #500
  • Norah Jones had a popularity rank of about #1000
  • Natasha Bedingfield had a current-popularity rank of about #3000

To cut down on cross-cultural differences, I only looked at users in the U.S. Thus, to find 2014 listening history for 27-year-old males on Spotify (based on self-reported registration data), we can find the median popularity rank of the artists that each individual 27-year-old male U.S. listener streamed, and calculate the subsequent median across all such listeners.



The Coolness Spiral of Death: Currently-popular artists lie in the center of a circle, with decreasing popularity represented by each larger ring.
The Coolness Spiral of Death: Currently-popular artists lie in the center of a circle, with decreasing popularity represented by each larger ring. As users get older, they “age out” of mainstream music.

Mainstream artists are at the center of a circle, with each larger concentric ring representing artists of decreasing popularity. The average U.S. teen is very close to the center of the chart — that is, they’re almost exclusively streaming very popular music. Even in the age of media fragmentation, most young listeners start their musical journey among the Billboard 200 before branching out.

And that is exactly what happens next. As users age out of their teens and into their 20s, their path takes them out of the center of the popularity circle. Until their early 30s, mainstream music represents a smaller and smaller proportion of their streaming. And for the average listener, by their mid-30s, their tastes have matured, and they are who they’re going to be.

Two factors drive this transition away from popular music.

First, listeners discover less-familiar music genres that they didn’t hear on FM radio as early teens, from artists with a lower popularity rank. Second, listeners are returning to the music that was popular when they were coming of age — but which has since phased out of popularity.

Interestingly, this effect is much more pronounced for men than for women:

While both genders age out of popular music listening, on average this effect happens sooner and to larger degree for men than for women.
While both genders age out of popular music listening, on average this effect happens sooner and to larger degree for men than for women.

For every age bracket, women are more likely to be streaming popular artists than men are. (These days, the top of the charts skew towards female-skewing artists including female solo vocalists, which may contribute to the delta.)

However, the decline in popular music streaming is much steeper for men than for women as well. Women show a slow and steady decline in pop music listening from 13-49, while men drop precipitously starting from their teens until their early 30s, at which point they encounter the “lock-in” effect referenced in the overall user chart earlier.

The concept of taste freeze isn’t unique to men. But is certainly much stronger.



Many factors potentially explain why someone would stop following the latest popular music, and most of them are beyond our ability to measure.

However, one in particular is something we can identify — when a user starts listening to large amounts of children’s music. Or in other words, when someone has become a parent.

Spotify has an extensive library of children’s music, nursery music, etc. By identifying listeners with significant pockets of this music, we can infer which listeners are “likely parents,” then strip out those tracks and analyze the remaining music.

Does having kids accelerate the trend of aging out of music? Or do we see the opposite — i.e. that having kids in the house exposes a person to more popular music than they would otherwise listen to?

The “musical tax” of having children: becoming a parent has an equivalent impact on your “music relevancy” as aging about 4 years.
The “musical tax” of having children: becoming a parent has an equivalent impact on your “music relevancy” as aging about 4 years.

In fact, it’s the latter: Even when we account for potential account sharing, users at every age with kids listen to smaller amounts of popular music than the average listener. Put another way, becoming a parent has an equivalent impact on your “music relevancy” as aging about 4 years.

Interestingly, when it comes to parents, we don’t see the same steadily-increasing gap we saw when comparing men and women by age. Instead, this “musical tax” is roughly the same at every age. This makes sense; having a child is a “binary” event. Once it happens, a lot of other things go out the window.

All this is to say that yes, conventional wisdom is “wisdom” for a reason. So if you’re getting older and can’t find yourself staying as relevant as you used to, have no fear — just wait for your kids to become teenagers, and you’ll get exposed to all the popular music of the day once again!

(Though I guess if we’ve learned anything today, it’s that you’ll end up trying to get them to listen to your Built To Spill albums anyway.)

Methodology notes

  • For this analysis, I wanted to isolate music taste down to pure music-oriented discovery, not music streamed because of an interest in some other media or some other activity. To that end, I eliminated any Taste Profile activity for artists whose genre indicated another media originally (“cabaret”,”soundtrack”,  “movie tunes” “show tunes”, “hollywood”, “broadway”), as well as music clearly tied to another activity ( “sleep”, “environmental”, “relaxative”,  “meditation”).
  • To identify likely parents, I first identified listeners with notable 2014 listening for genres including “children’s music”, “nursery” , “children’s christmas”, “musica para ninos”, “musique pour enfants”, “viral pop” or ” antiviral pop”, then subsequently removed that music when calculating the user’s median artist popularity rank.
  • To control for characteristics across cultures, this analysis looked only at U.S. listeners.
  • As has been pointed out in previous analyses, registration data by birth year has a particular problem in overrepresentation at years that end with 0 (1990, 1980, etc). For those years, I took the average value of people who self report as one year younger and one year older. Matching birth year to age can also be problematic depending on month of birth, so for the graph’s sake I’ve displayed three-period moving averages per year.

Why your lousy fantasy football season isn’t your fault

“Just win, baby.” – Al Davis, Owner & GM, Oakland Raiders

Maybe for you, the realization that your fantasy football season was circling the drain came slowly. A few bad breaks. A concussion that took your star QB out for a game; an opponent that picked up a free agent running back the same week that he broke loose for 200 yards.

Or maybe it came suddenly. Maybe you scored 125 points in Week 13, but missed the playoffs because your opponent scored 127, leaving you wondering why you even bother with this stupid game.

However it happened, the important thing is: it wasn’t your fault.

But nobody understands that! Yes, everyone whines about their lousy fantasy season. But the key difference is that most of those poor fools didn’t know what they were doing. Whereas your complaints are justified. Your disaster wasn’t poor playing but poor luck. So your pain is exquisitely unique.

The question at hand is – how do we prove it?

Continue reading “Why your lousy fantasy football season isn’t your fault”

How machines can help us discover overlooked films

Feeling like I’d burned through my standard sources for movie recommendations, I recently decided to turn to box office failures. I was seeking out an automated way to explore the world of such movies and find “overlooked” films that are actually very good, but were ignored in theaters and dismissed by critics.

Using Nathan Rabin’s popular “My Year of Flops” series on The AV Club and follow-up book as a starting point, I designed an algorithm to predict whether a box office failure is actually a film worth seeing. The algorithm examines multiple aspects of a movie’s cultural response to make its prediction – such as applying sentiment analysis to capture the tone of reviews, and understanding whether critics and audiences responded differently to a movie. The output is a list of 100+ movies released over the past decade with high likelihood of being quality, “overlooked” films.

Here’s how it works…


In 1994, Forrest Gump made over $300M at the domestic box office, won six Oscars, and spawned a murderer’s row of pop culture references.

The Shawshank Redemption also came out that year. It had a confusing name, won exactly zero Oscars, and made only $16M in its initial run – an amount outdistanced by House Party 3, Kid ‘n Play’s capstone installment in their “living-situation-oriented festival” trilogy.

Yet flip on TNT on a random Saturday night, and you’re more likely to be greeted by Andy and Red than by Forrest and Jenny.

Because it flopped in theaters, people had to discover Shawshank organically on video. And not only did its reputation grow, but fans felt a sense of personal ownership and evangelism. Nearly everyone I know who’s seen the movie first watched it because of a recommendation, and fiercely loyal IMDb users have even rated it the best movie of all time.

One of the earliest Amazon.com customer reviews for The Shawshank Redemption.
One of the earliest Amazon.com customer reviews for The Shawshank Redemption.

Continue reading “How machines can help us discover overlooked films”