Who Reviews the Pitchfork Reviewers?

Pitchfork is the largest indie music site on the Internet (in the English-speaking world, at least), updating its pages daily with the latest indie music rumblings, interviews with budding artists, sneak previews of new albums and artist collaborations, and, most notably, a suite of music reviews by dedicated music critics forming Pitchfork’s staff. I follow Pitchfork’s album reviews religiously and I am not alone in feeling that their ‘Best New Music’ category routinely captures the best that modern music has to offer.

Since its creation in 1999, Pitchfork has gained quite the following among music fanatics and its widely-read album reviews can have a demonstrable impact on an album’s success. Indeed, Pitchfork is credited with propelling Arcade Fire into the limelight after bestowing a glowing 9.7 ‘Best New Music’ review on their 2004 release Funeral. Conversely, they are responsible for striking a devastating blow to Travis Morrison’s solo career after his 2004 release Travistan received a controversial 0.0. This make-or-break phenomenon has appropriately been dubbed The Pitchfork Effect.

Simply put, an album’s commercial fate can rest squarely in the hands of the Pitchfork staff member responsible for its review. But what goes into a review? Are staff members consistent in their reviewing behavior? Do biases exist? If so, what kinds? In the following post, I analyze Pitchfork’s data in an attempt to answer these questions. After all, who reviews the reviewers?

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The 'Sum Over 1' Problem

I recently came across this fun probability problem which I’ve dubbed the ‘Sum Over 1’ problem. It goes like this:

On average, how many random draws from the interval [0, 1] are necessary to ensure that their sum exceeds 1?

To answer this question we’ll use some probability theory (probability rules, distribution functions, expected value) and a dash of calculus (integration).

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Red Fish Blue Fish

As part of the NCSU Statistical Learning Group (SLG), I gave a 50-minute overview of supervised classification methods in the machine learning literature. My presentation was aimed at an audience of primarily undergraduate and graduate students, not all of whom were statisticians. Please enjoy the videos below and follow along with slides (html) and code (Rmd) in the accompanying GitHub repository. The slides were produced using RMarkdown v2.0 and knitr within RStudio.

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