Understanding your listening stats

Everything is computed from one thing, your plays, each a single listen with a timestamp and a source. No weighting, no algorithmic massaging: a play is a play. What makes the numbers trustworthy is the cleanup that happened on the way in, de-duplication across services and MusicBrainz matching, which pools name variants of the same recording into one line. The example profile shows every view below with sample data.

Top artists, albums and tracks

The library view ranks your artists, albums and tracks by play count, with a time-period filter: the last 7, 30, 90, 180 or 365 days, or all time. The filter counts plays inside the window, an artist you binged in 2019 can top your all-time list while being absent from your last-30-days list. Click through any artist, album or track for its full history: when you found it, when you wore it out, every listen on a timeline.

Streaks

A streak is consecutive calendar days with at least one play. Stick shows your current streak (running up to today) and your longest ever. Streaks are only as complete as your data. If your history has gaps, a stretch before you scrobbled, an offline period the Apple Music poller couldn't see, a real-life streak can show as broken. The fix is usually importing the fuller export file that covers the gap.

The listening clock

The clock is a day-of-week × hour-of-day heatmap of your entire history: each cell is how often you've listened during that hour on that weekday, darker meaning more. It's the view that tells you who you are, the Monday 9am commuter block, the Saturday-afternoon vinyl-hour column, the 2am insomnia rows. Nothing else in your stats is as instantly recognizable as the shape of your own weeks.

Plays by year and month

A simple bar per year (and per month, zoomed in), covering everything you've imported. This is where multi-year exports pay off: import a decade of data and you can watch your listening volume rise and fall around jobs, moves and life phases. It's also a good integrity check, a year that looks suspiciously empty usually means a gap in your source data, not a year of silence.

Sources

The per-source breakdown shows how your plays divide between Last.fm, Spotify, Apple Music and Plex. Beyond curiosity, it's the fastest way to sanity-check an import, if you just imported ten years of scrobbles and Last.fm's share barely moved, something's off (probably a column mapping; check the import guide).

Genres

The genre breakdown is computed from MusicBrainz's community genre tags for the artists, albums and recordings you actually played, not from any service's marketing categories. Genres fill in gradually after an import, as the background worker fetches tags alongside its matching work, so don't worry if this chart looks sparse on day one and richer a few days later.

Recommendations

Once a week, Stick builds a set of artist and track suggestions from your own history: forgotten favourites you haven't played in ages, artists similar to what you've been living in lately, and the occasional wildcard. They're computed from your plays and refreshed weekly, the more history you import, the better they read you.

Getting more out of it

• Import all your sources, stats computed from half your listening are half-true. The Apple Music, Spotify and Last.fm guides cover each export.
• Keep it flowing with live tracking or the Plex webhook so today's listening shows up today.
• Spend a few minutes in Needs Attention after a big import, every fix sharpens every chart.