Statistics of

Total Tracks: 498898

New Songs today: 14

Scheduled Songs: 0

API Requests: 287 adds currently about 3'000 Songs to its database every day.
Scheduled songs are marked to be updated because information to the song are missing, in a perfect world this should be 0 ;)
The Website is running about 50'000 API requests to amazon, spotify and beatport every day.

Histogram of Songs and their Score

The histogram shows the root count of songs in dependence of their score.
About half of the songs are having a score of 0 while only 135 songs have a score larger than 20.

Distribution through the Genres

Each block is representing 1000 songs of a given genre.
Due to its popularity on Beatport my crawler favorites some genres.

Growth of Database

The line shows the growth of the database in songs per day.
The first part is pretty much linear until I implemented another method to find "good" songs and it started to accelerated quickly.
At some point I ran into a bug that caused the database to die for a weekend and me shutting down the crawler for a bit resulting in a small plateau. Afterwards I slowed the crawler down to prevent further issues.

Scatter Blots

The blots show almost no correlation whatsoever.
There is a slight positive correlation between score and popularity and score which i would have expected to be larger.
To my surprise I could not find a correlation between energy and popularity.
Looking at the values of danceability I can see a peak around 80/81 with two to five times as many songs as the surrounding values, hinting there could be a bug in spotifys analyzer.

Danceability vs. Popularity
Pearsons R = -0.087

Energy vs. Danceability
Pearsons R = -0.274

Energy vs. Popularity
Pearsons R = -0.016

Popularity vs. Score
Pearsons R = 0.282

Danceability vs. Score
Pearsons R = 0.054

Energy vs. Score
Pearsons R = 0.026