In BopMatch, players are shown a target artist and are tasked with choosing another musician or band who they think will have equivalent popularity. The closer the two are in popularity, the higher the score.
Popularity is a statistic available via the Spotify Web API, which I also pull from to allow players to search for and select any musician on Spotify.
BopMatch was built using React and Vite on the front-end, and Supabase for the backend.
The idea for BopMatch came from a game my brothers and I would play in the car during road trips. One person would give a target artist, and the others would each make a guess as to who would be closest in monthly listener count. Whoever guessed the closest would choose the target for the next round.
BopMatch uses a popularity score rather than monthly listener count for two reasons: First, for some reason, Spotify does not expose monthly listener count via their API. I don't know why this is, since users can view this easily in the app. Second: the popularity score is linear, from 1-100, which makes my life easy. If I were to use monthly listener count, I'd have the issue where artists at the top of the charts would have differeces of millions of monthly listeners between them, while less popular artists would differ only by a few hundred or thousand. Using a linear score means I don't have to worry about compensating for this imbalance.
It's been interesting to think about making a game out of very simple parts. I take some publicly available data, and build a frame around it that makes it a game.
It's also interesting to think about who's actually good at BopMatch. Since the popularity scores are fetched live from the Spotify API, they can change from one day to the next. So to be consistenly good at the game is not just a matter of familiary with a lot of music, but an active antenna towards music culture.