Data

2
Aug 10

What Can You Learn From Last.FM? (Part II)

FT + Proven By Science//5 comments • 707 views

In part 1 of this series looking at artist metrics on Last.FM, I talked about PPL (Plays Per Listener) and also the relative popularity of each act’s top track.

In this part we dig a little bit deeper into an artist’s catalogue, with two more metrics based on their list of top tracks (which, remember, are the tracks with most listeners over the last six months, not over the whole of LFM’s history). I’m calling these metrics – rather unimaginatively – head and body. “Head” is the number of listeners to the tenth most popular track expressed as a percentage of the number of listeners to the first. “Body” is the number of listeners to the fiftieth most popular track expressed as a percentage of the number of listeners to the tenth.

Both of these are based on the same principle – ratios of popular and less popular songs in an artist’s catalogue – but they turn out to measure quite different things. Head measures the extent to which an act is a several-hit wonder. A high head means that your top track isn’t that much more popular than your tenth, which usually means you’ve racked up either a bunch of successful singles or have at least one album that people are keen to listen to in toto. A low head means that you have a few, or maybe just one track which people are particularly keen on but that interest doesn’t extend very far – it suggests a big chunk of casual listeners in your audience.

23
Jul 10

What Can You Learn From Last.FM? (Part I)

FT//3 comments • 783 views

Last week a question occurred to me: what interesting things can you find out by playing around with Last.FM listening data? Last.FM themselves offer a fair bit of extra analysis to users in their “Playground” section, but it’s all to do with individual listeners or their networks (or “neighbourhoods”). I wanted to see how much LFM data could tell us about specific artists, and how people listen to them.

So using the most topline, publically available data possible – the artist pages and charts of most-played tracks – what can we find out? I created a few metrics which I could generate (by hand! no programmer I!) in 20 seconds or so for each artist and set to work populating a mini database out of the artists on the overall LFM charts, then the ones on my personal charts, then anyone I thought might be interesting. The results are this series of three – somewhat wonkish – posts: the conclusions will be in Part III so if you don’t fancy seeing me crunch numbers (albeit very EASY numbers) wait around for that.

Here’s what I came up with!