In many ways, this is a great book! It’s well written (and well read), and it made me care about baseball in ways I usually don’t. It’s also an interesting story—a great example of the power of statistics and data science to do cool things. That last part, though, is why I read it. I expected to be critical of the book’s take, and I wasn’t wrong. It cheerleads attitudes about (data) science that I’m skeptical of, like its supposed superiority in terms of objectivity and rationality.

As often happens at the end of a semester, I’ve been spending a lot of time thinking about grades: What they mean, what purpose they serve, and how to best assign them. In thinking about this, I’m also thinking about a comment that a number of my colleagues put on each class syllabus: something to the effect of “I don’t give grades, you earn them.” These colleagues are gifted teachers whose examples I strive to follow, and I appreciate the sentiment behind their statement, but it’s also always struck me as oversimplifying what it means to grade.

At the beginning of my senior year of high school, Tyler and I were neck and neck in class rankings—if memory serves, he was slightly ahead. This never got in the way of our friendship. We had spent too much time playing the Wizards of the Coast Star Wars Roleplaying Game together, and a few years earlier, we’d even spent one memorable night with our mutual friend Chris hiking repeatedly back and forth between Tyler’s house and mine so that we could find the right hardware for hooking up someone’s GameCube to my family’s venerable TV so that we could play TimeSplitters 2.

Just because you can topic model something doesn’t mean it actually tells us anything (and please don’t ever describe computational text analysis as “objective”).