Update at 4:12 p.m.: on the new interactive chart, mouse over any point for details on the blog: name, URL, tagline, description, Alexa rank, Technorati 100 rank and Google Reader subscriber count.

How many people subscribe to feeds from the most popular blogs? Moving down the list, how quickly do the numbers fall off? The answer: it's a power law distribution, popularly known as a "long tail" thanks to Chris Anderson's 2006 book and ongoing blog. A key earlier work: Clay Shirky's 2003 essay, Power Laws, Weblogs and Inequality.

Granting that this weekend's subscriber stats are flawed and that the manually-assembled list is incomplete, the curve shows the expected shape. The above image is a static teaser; I hope to post an interactive chart by the end of the day. [update: done]

(Kevin Burton charted similar data on Sunday. We omitted non-blogs and added color coding by category. FYI: Kevin's Tailrank meme tracker currently leads with Sunday's TechCrunch post about Google Reader. I wonder whether the Mashable post will appear later today.)

First, a very sincere kudos to Pete Cashmore of Mashable for digging up hard data that shows how Google Reader's "feed bundles" can skew results. It's clear that subscribing to a bunch of feeds with a single click isn't the same as choosing individual feeds by hand.

What Pete added: real numbers.

(I haven't dug into the specific examples to see if Pete's conclusions are the mostly likely explanation, though his results aren't that surprising.)

Where Pete went wrong: concluding that these specific errors make the whole dataset worthless. ("**" added)

Google Reader stats, in case you don't know, are bulls**t. In fact, all Feedburner stats for most top blogs are bulls**t due to the effect of default feeds.

I've looked at lots of data over the years, including as part of corporate data quality projects. Even without gaming, any non-trivial dataset I've seen has flaws. The answer is to understand and attempt to fix or work around flaws, not to throw out the whole thing or wait for flawless data. The perfect is the enemy of the good.

Even if the data is flawed for some or all of the 91 blogs included in the feed bundles, much of that can be corrected for based on analysis like Pete has done. And, there's a whole world of blogs that aren't affected. Sure, there are similar problems with other feed readers, home pages with RSS subscriptions, etc. Switching from feeds to sites, there's also no shortage of problems measuring page views (bots!), unique visitors (cookie deletion!), time spent on site (tabs!), etc. But data is useful and important, so people make do with what's available given time and budget constraints. (Mashable isn't shy about mentioning some stats on every page of the site: "in excess of 5 million monthly pageviews" and "ranks among the Top 100 blogs worldwide".)

Has anyone automated the process of harvesting the subscriber stats? If so, please send details. I'll bet there's meaningful data to be found.

One more bit of overreach:

The easiest way to get a default feed on one of these startpages is to own it, promise to promote it on your blog or be friends with the person who runs it.

Did the post include any evidence? (I'm sure these things happen, but making implied accusations without backing them up doesn't lend credibility to the post.)

On Crunchnotes, Mike Arrington presents another side to the story, including a useful data point:

the complainers rallied around the notion that the stats are somehow fixed. In particular, some of the feeds are included in bundles that users can add to the reader, jacking up their stats.

First of all, these blogs and other news sites did something interesting to get included in those bundles. Second, the effect is not nearly as great as everyone seems to think. Gizmodo, for example, is included in the technology bundle on Google Reader, but it has just 30,000 subscribers. Competitor Engadget, by contrast, has 345,000. So where did Engadget get those additional 315,000 subscribers? Perhaps they simply earned them.

It wasn't that long ago that TechCrunch wasn't on any list at all. We got big by writing about things we care passionately about and linking early and often to good stuff out there on the web, particularly other blogs.