Many artists and people see the Super Bowl as the pinnacle of music popularity. So much so that the Super Bowl doesn’t even pay the artists for their performances; it is almost purely for “exposure.” Now that all forms of media are monetized, artists are very likely to gain some revenue from their Super Bowl performance anyway, but the main goal is increased media presence. In this day and age, there are two main standards to meet in order to be regarded as a popular or successful artist in the US: Grammy awards/nominations, and streams. Streaming apps such as YouTube, Spotify, Apple Music, etc, have almost completely rendered physical media useless. It has become the way that major music corporations across the world now use to gauge success.

With the Super Bowl being one of, if not the largest, stages for an artist, I wanted to see how much of an impact a Super Bowl performance has on the number of streams of that artist’s particular genre within that year, and if there are any notable changes from the year prior or after. And then I further made the decision to only go off of the black Super Bowl artists from 2010 – 2023, as there has been a higher frequency of black artists being chosen to perform for the Super Bowl. Then, after identifying each black artist or group that was predominately black, I then classified them into only one genre. This was met with some difficulty because the data and Spotify itself limit one artist to one genre, even though their music career may span over many different genres. So I just classified them into a genre that fit their music the most at that time, so example, even though now most will say that Bruno Mars makes RnB and Soul music, the majority of his music circa 2014 was pop. Although I still chose to have Bruno Mars and Beyonce cross listed with pop and RnB. Another hiccup in this was the year that many different artists that were under Death Row Records headlined in 2022. The artists were: Dr. Dre, Snoop Dogg, Mary J Blige, Eminem, and Kendrick Lamar. While Mary J Blige is infamous for her RnB records, as the majority of artists and the theme around this year’s Super Bowl performance were California Rap and Hip-Hop, it was labeled under the Hip-Hop/Rap genre.

The graph above was created using the aggregated dataset mentioned previously. I made sure to emphasize which year there was a black artist during the Super Bowl and the name of the artist. As you can see above, using the pop genre as an example because it has the most Super Bowl performances, after 2019, all streams went down collectively, and the Super Bowl performances for the following years were no exception. This can be seen for all other genres as well. In conclusion, based on the data, there is no significant change in the number of streams per genre based on the black Super Bowl artists’ performances of that year after 2019.

While my question did not lend any new and profound knowledge to the world, this project has taught me a lot about data literacy and visualization. The main shift in my thinking about data is determining what data is necessary and unnecessary. The entire dataset includes the streams for genres not shown above, and when I was looking at all the categories, I got lost in what I was supposed to find, which made it harder for me to generate a question from it. I often have trouble creating interesting and achievable research questions. Because of my refusal to leave out information; I tend to find myself trying to incorporate everything and every possibility into my research, which is often times impossible. So instead, with this project I made the conscious and honest decision to only ask questions about what I was personally the most interested in. This also allowed me to analyze my own visualizations’ legibility with graphic design practices such as color, contrast, and typography. Making sure that the colors used in the graph were distinguishable from one another and that the axis and labels were legible. If I were to do this project again, I would first find another question to ask that went deeper into data manipulation and context. Reorganizing or using the data more creatively and knowing when, where, and how the data was sourced, as the data gathered does limit the breadth of questions that can be generated.

Citations:

• I used Grammarly to check for grammar, spelling, and sentence conciseness throughout the draft. No ideas or substantive text were generated by the tool.
• Dataset from: Iryna Tokarchuk. Top Streamed Spotify Songs by year 2013-2023.” 
   https://www.kaggle.com/datasets/irynatokarchuk/top-streamed-spotify-songs-by-year-2010-2023  

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