Project description:

Maneskin reinvented rock for the XXI Century adding slightly more energy to the music, arguably more than what every other rock band has ever done in the past not departing from the tradition of the genre. More importantly, data show that the Italian band managed to place itself at the intersection between Anglo-Saxon and Italian rock, leading to a mix of the two traditions. Maneskin also has a peculiar relationship with keys. Their most used key is b minor, which shouldn’t be. Yet, if you listen to their songs, they have a melancholic aftertaste thanks to their liberal approach to

Impact reached:

The project contributed to explaining the success of Maneskin but, also, it has shown that you can do a different kind of musical journalism. Musical critique, usually, is a speculative subject: it rarely takes musicological tools to understand, in particular, pop phenomena. Here, thanks to the data provided by Spotify, I was able to broaden the perspective and get to see a bit more of the secret sauce Maneskint put in their music. I have not the ambition to change musical journalism with this single story but, at least, this kind of data allows readers to get a better understanding of rock music beyond personal tastes and with as less bias as possible. 

Techniques/technologies used:

I randomly selected rock bands from Italy and from the Anglo-Saxon world and downloaded their discography from the Spotify API. Then I performed a factorial analysis. The factorial analysis allowed me to associate energy and acousticness (as provided by Spotify’s proprietary algorithm) into a parameter I called strength. Danceability, valence (aka positive sentiment), and liveness went into what I called cheerfulness. Then I produced (on Tableau public) a scatterplot with cheerfulness and strength divided into four squares. From the top-right in an anti-clockwise sense:

– King of the Party
– Party but not too much 
– Malincholyc rock 
– Solemn Rock 

This allowed me to better describe what Maneskin does differently from other bands. Here, I provided two views: band-level (average song) and song-level. At the bottom of the chart, it is possible to select one band and compare it to Mansekin. 

In addition, I performed a quantitative analysis of keys (major/minor plus details) and an analysis of rock trends over the last 50 years. 

What was the hardest part of this project?

The hardest part of the project was to find a not-too-technical narrative. How do you explain factorial analysis to the layman? Secondly, the hardest part of the project was the first chart. There, you can select and compare band by band to see how similar or different they are to one another. I had to write some code in Tableau to program it and to use parameters to define the song/artist visualization. 

What can others learn from this project?

Other journalists can learn that data is not limited to politics, economy, crime, or the environment. Rather than a subject, data is a method of doing journalism based upon a multidisciplinary array of skills and tools and, more importantly, replicability.

This article originates from an episode of my newsletter: the code I wrote for it is the base of this project, which just needed a few more artists. I share the code from my Githb repository 

 

Project links:

www.wired.it/article/maneskin-segreti-successo-analisi-dati-grafici/

github.com/fpmassam/Small-things/blob/main/Hania%20Rani.R

danumbers.substack.com/p/why-hania-rani-and-giovanni-allevi

developer.spotify.com/discover/