Leon Wieseltier writing about DH is like Maureen Dowd writing about hash brownies

What’s most striking about Leon Wieseltier’s essay in the New York Times Book review is how it confirms almost every cliché about the humanities as technophobic, insular, and reactionary. Not to mention some stereotypes about grouchy old men. Now I should confess at the outset to being a longtime Wieseltier cynic. His misreadings of popular culture always seemed mildly ridiculous. But what’s striking about the NYT piece is his vast ignorance of the subject. Wieseltier writing about digital humanities is like Maureen Dowd writing about hash brownies . Note to New York Times editorial writers: show a remote understand of the subject. Your ignorance is not a cultural crisis.

This line in particular, caught my eye: “Soon all the collections in all the libraries and all the archives in the world will be available to everyone with a screen.” Really? On what planet? Perhaps Wieseltier was thinking of this 1999 Qwest commercial for internet service?

Now I’m a specialist in Japanese history, and I’m certain that the millions of pages of handwritten early-modern documents in archives across Japan will not be all online “soon.” But even assuming that for Wieseltier “all the libraries” might mean modern publications in English, French and Hebrew, this is just nonsense. Has Wieseltier noted the metadata problems on Google Books? Or would understanding the limits to digitization be too much to ask?

What’s tragic about Wieseltier’s mindless opposition of the humanities versus technology it that it precludes exactly what we should be teaching: how to employ critical thinking when using technology. Dan Edlestein has a marvelous essay exploring how to search for the concept of “the Enlightenment.” His piece shows how, first, one can’t do a search without a basic understanding of the history of the Enlightenment itself, second, that quirky results are more than “mistakes.” Parsing weird and unstable search results can inform our understanding both of digital technologies and the history of ideas. The need for critical thinking in database searches actually proves the ongoing relevance of humanities in the internet age.

Of course, at the heart of Wieseltier’s panic is the “decline of the humanities.” Too bad Wieseltier doesn’t read the Atlantic. The humanities aren’t in decline. “The same percentage of men (7 percent) major in the humanities today as in the 1950s.” The overall drop over that period came from women, who began to pursue careers in the sciences because of the end of institutional gender bias. But that analysis came from the great digital humanities researcher Ben Schmidt. And understanding it would require taking both numbers and gender seriously. Which apparently is something great humanistic minds need not do.

Baseball, Football, Moneyball

In fall 2014 I taught a freshman seminar on data visualization entitled “Charts, Maps, and Graphs.” Over the course of the semester I worked with the students to create vizs that passed Tukey’s “intra-ocular trauma” test: the results should hit you between the eyes. Over the coming months I’ll be blogging based on their final projects.

Today’s post is based on the work of Jeffrey You, who used US professional sports data, comparing baseball and football. As Jeffrey noted, the vizs highlight two key differences between the sports. First, the shorter football season (16 vs. 162 games per season) means that many football teams finish with the same record. The NFL scatterplot is therefore striated, and the winning percentage looks like a discrete variable. In fact there are limited outcomes for both baseball and football, but 162 possibilities looks continuous while 16 does not.


The other contrast is relative importance of total payroll in baseball. In neither case is there a strong correlation, but football is astonishingly low: r= 0.07 for the NFL compared to r=0.37 for MLB. What’s going on? Jeffrey suspected that injuries might play a greater role in the NFL, so a high payroll might pay for less actual playing time. He noted as well, the greater importance of single player. Tom Brady, he noted, was a 199th draft pick with a starting salary of “only” $375,000.

The graphs also highlight the greater payroll range in MLB compared to the NFL. The regression line for MLB suggests that increasing a win-loss record by one game costs about $8 million. But the payroll spread in MLB so large that it can become a dominant factor. Jeffrey noted that for 2002-2012 the average payroll for the Yankees was $162 million while that of the Pirates was merely $41 million. For that same period, the Yankees have never won less than 50% of their games while the Pirates never won more than 50%. There is no comparable phenomenon for football. The standard deviation for MLB payrolls is about $35 million but for the NFL it’s less than $20 million.


NB: Technically, one should use the log of the odds rather than use winning percentage as the dependent variable, but in this case the substantive results are the same. For MLB the values range from 25% to 75%, in the more linear range of a logit relations. For NFL, there’s no appreciable correlation in either a linear or a logit model.