[OC] Ich habe 9 Jahre meiner Laufdaten mit meinem Musikhörverlauf synchronisiert, Routen nach Genre kartiert und dann analysiert, wie sich Musik auf mein Tempo auswirkt

I synced 9 years of music listening history with GPS running data (~3,500 miles across 1,172 activities).

* **Runs with music average 9:56/mi vs 10:20/mi without.** Roughly a 24 sec/mi difference across 776 vs 396 activities
* **Metal and hip-hop are my fastest genres** (~9:49–9:56/mi); indie, folk, and jazz are the slowest (~10:16–10:27/mi)
* **My taste shifts when I run.** Hip-hop jumps from 27% of my everyday listening to 32% during activities, while electronic drops from 44% to 32%. I reach for hip-hop to run to, even though electronic dominates the rest of my day.
* **My running BPM is virtually identical to my everyday listening:** median of 172 vs 170. I expected to gravitate toward faster tempos when running, but apparently I just like fast music all the time.
* **That genre mix has stayed remarkably consistent over 9 years** of running (109 months, 3,410 songs)

The „Genre Map“ (first image) divides my running routes into a grid where each cell is colored by the dominant genre listened to in that area. Opacity scales logarithmically based on how many runs passed through each cell. The map shown is Boston, where the majority of my runs took place. You can see the hip-hop orange and electronic teal trading off across different routes.

**Data sources:** Strava (GPS, pace, cadence, heart rate), Spotify and Apple Music (listening history, track metadata). BPM data from Deezer. Genre classifications from Last fm.

**Tool:** Track-Map: an app I built to sync, visualize, and analyze music listening with activities

Are these findings statistically significant?

Next try podcasts about running.. ultra runners and the like