Let’s face it, Spotify is not very generous when it comes down to publishing personal statistics. They put a lot of effort in the yearly report (which is definitely awesome), but if you want something more, you should visit Spotify Statistics. Spotify Statistics is a third-party web-app which shows your most-listened artists and tracks. Furthermore it provides extensive details about all your playlists.
Not convinced yet? Well, it is free, and it is as simple as just logging in with your Spotify account!
It shows a list of your most-listened artists and tracks during either the last 4 weeks, 6 months or from the beginning. Making it a great way to revisit your memories you have from the artists you used to play non-stop years back.
On top of that it also shows all kinds of interesting facts about your playlists too! From quicks facts like average tempo, oldest / newest song and average track length; to a distribution of artists who populate the playlist, and more.
Seems like my workout playlist, mainly used while I pump out some reps,
is also great for dancing!
Let’s get technical now
The data used in Spotify Statistics is solely and directly derived from the Spotify API. The API allows for some impressive data to be retrieved. It contains data about most listened music, but more interestingly, it also allows access to automated audio analysis and features for tracks.
The audio analysis describes the musical content and structure of a particular track. To name a few, it describes the rhyme, pitch and timbre. It is also able to divide the track in similar sounding segments, and further divide those into sections and bars. The documentation provided by Spotify can be found here.
The provided audio features describes the high-level characteristics of a track, like the level of “acousticness”, “instrumentalness” and “speechiness” for example. The documentation provided by Spotify can be found here.
Some of these features and analysis are displayed in Spotify Statistics, under the playlist section. Furthermore I am assuming Spotify itself uses this information to, among other things, create recommendations, by comparing tracks using their features and analysis.
Personally I find it very remarkable that Spotify is able to derive these kinds of features and analysis from a particular track in an automated way. Unfortunately Spotify does not publish any details about the impressive logic behind this. Regardless, it would be too complex to look into during this short article anyway.
I am Jeroen Kappé, a Computer Science student from Holland, and the creator of Spotify Statistics (2018).
I was inspired to create SpotifyStatistics.com because I really enjoyed the yearly reports Spotify offered, but I got tired of having to wait for a whole other year. I still try to actively develop the website further whenever I have time.
Because we are talking music; My favorite artist? Kanye West. Favorite album? The Life Of Pablo. Favorite Song? Hey Mama.