Finding Statistics About Your iTunes Library

iTunes logo with graph

Anyone who has been reading the tunequest for a while knows that statistics, numbers, figures and graphs have played a large part in its progress. In fact, it was the discovery that 10% of my songs were responsible for 49% of my total play counts that prompted me to set out on this endeavor in the first place.

To this day, I’m still surprised by the lack of sophisticated options available for gathering and analyzing iTunes’ stored data. That XML file has been a statistical treasure trove since the day it started recording star ratings and play counts. You’d think that in the four years since, there would be a more mature market of programs to choose from.

However, 2006 has actually seen some positive developments in that regard. While there is still no killer app for iTunes stats, there are a number of solutions for parsing your XML file and learning more about your music, and yourself.

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Lovage: The most common word in my iTunes Library

According to Super Analyser for iTunes, the most common word in the song titles of my library is "love."

Unfortunately, the program doesn’t tell how it generates that number. Does it include variations like “lovely,” “loves” and “lover”? Probably not. It’s probably a straight-up word-pattern match.

Still, that result surprises me. Certainly it filters out “a,” “an,” “the,” “that,” etc, but I would have expected some kind of standard nomenclature to take that top spot. Something like “mix,” “remix” or “version.”

After doing my own quick analysis using iTunes’ search box (song names only), I find this:

  • Love (252) – loves (6) – lover (17) – lovely (5) = 224 songs.
  • Mix (376) – remix (155) = 221 songs

So it is neck and neck. Of course, those numbers are not quite 100% accurate. There are undoubtedly a handful more combinations and variations that I missed. But for now, “Love” is the reigning champion of my iTunes library.

The question is: What’s yours? Download Super Analyser for iTunes to find out. It runs on both Windows and Mac OS X.

The tunequest half way point

So today, July 22, marks the half way point of tunequest in terms of calendar days. And while I’m a little behind overall, it doesn’t look as bad as I was projecting a couple weeks ago. The count shows a deficit of roughly 500 songs, which seems like a lot. However, the time count shows about a 10 hour deficit.

Some quick math tells me that the average song length will be 5% less on the back end of the project, which should help accelerate the number count.

In any case, it’s been a great five and a half months getting to know the songs in my library more fully. And I can quantify that progress. From the about page:

After 3.5 Years of itunes statistics, 9597 songs (65%) had never been played or only played once. Further analysis revealed that 10% of the songs were responsible for 49% of the play counts.

As of today, which includes 4 years of stat collecting, I can say that 7622 songs have a playcount of 0 or 1. That’s now 54% of my total library, a 11% improvement. Also, the top 10% of my songs are now responsible for 42% of the my total plays. It’s still a large disparity, but there’s been a marked improvement.

And honestly, given the rules of the tunequest, I may listen to every song in my library, but that will still leave about 5700 songs with a count of 1, so the lowest that first indicator will get is 40%.

As for the top 10%, the lowest they’ll go is 35%.

But hey, I’m really having too much fun with this project to worry that much about the numbers. So here’s to more another 5 and a half months of great music listening.

iTunes Statistician for the iTunes Stats Obsessive

There’s a new program in town (for Mac users) to help you gather more nuggets of information about your listening habits as reflected by your iTunes library. it’s a nifty little piece of donation-ware called iTunes Statistician and it analyzes your library data to calculate your top 100 songs, artists, albums and genres, based on playcounts (and optionally weights for star rating as well). additionally, it calculates the total number of plays of all your songs and tells you how much total time you’ve spent listening to your music. It also tells you the average length of your songs (4:23 in my case) and average number of songs you play each day.

The program makes a pretty good desktop-based substitute to the ailing iTunes registry (which is looking to beef up its service, so kick in if you’ve got a few extra bucks). unfortunately, iTunes statistician only samples the entire library. the iTunes registry, on the other hand, allows you to submit any exported song list in XML format. In that regard, it is much more versatile. If you wanted to see the stats for all your 90s music, you’d simply create a smart playlist with condition year is between 1990 and 1999. export song list from iTunes as XML and upload it to the registry for analysis.

However, until the registry is fully operational again, I’m certain that iTunes statistician will provide me with all the information I need.

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