Metacritic Album Data Project

Metacritic is a website that aggregates critical reviews of films, music, games, and other media. Each piece of content featured on the site is assigned two scores: a Metascore and a User Score.

According to one of Metacritic’s own about pages, the Metascore is designed to “capture the essence of critical opinion,” and only takes critical reviews into account. Consequently, any overlap that exists between a film or album’s Metascore and User Score reflects actual consensus between critics and users, and isn’t merely an artifact of Metacritic’s algorithm.

I wanted to get a sense of the relationship between Metascores and User Scores– namely how predictive one is of the other– so I decided to look at the scores of a small sample of albums.

First, I used Beautiful Soup to scrape the All Releases page for links to individual album pages. Then, I scraped each album page for the Metascore and Userscore, and omitted those that hadn’t yet been assigned the latter.

I haven’t invested much time in this project so far, and I haven’t taken as close a look at these data as I’d like to, so I thought for right now I might just share this visualization, which I think is pretty cool even though it mostly mirrors my expectations.


Update: On a reader’s recommendation, I’ve been experimenting with the Seaborn visualization library. I wanted to see what these data would look like when visualized as a hexagon chart. If you’re not familiar with this type of visualization, it basically shows the density of events within the surface area of individual hexagons.

I suspect that using this type of visualization would make the most sense when you’re dealing with a sample that’s too large to be represented as a scatter plot, but I thought I’d share this one anyway:


Update 2: Another helpful reader suggested that I visualize the correlation. Here it is:



There’s probably a lot more I could do with this data. Are there any questions you would like me to answer in a future post? Does anything here make you feel curious? You can let me know by commenting below or emailing me at



    1. This is really cool. Do you know what the correlation is? Using Seaborn, something like the code below should add the best fit line.

      sns.regplot(x=”User Score”, y=”Metascore”, data=Album Data)


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