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Posts Tagged ‘Big Data’

Baseball fanatics like me love to wallow in cliches such as “baseball is life.” “Baseball is a lot like life,” said Hall of Fame broadcaster Ernie Harwell. “It’s a day-to-day existence, full of ups and downs. You make the most of your opportunities in baseball as you do in life.” Yes, indeed.

Regularly enough, however, a desiccated cliche like “baseball is America” comes alive. Such is the comment of San Francisco sportswriter Henry Schulman upon the announcements that Bryce Harper will get a guaranteed $330 million over 13 years from the Phillies and Mike Trout a guaranteed $430 million over 12 years from the Angels. (Trout will receive annually about 25 times more in real dollars than Babe Ruth ever did. He will make about 800 times as much per year as the median American worker today makes–and he will have a lot more fun making it, too.) “So, if I understand Baseball economics now,” Schulman tweeted, “a few guys at the top earn more than they can spend in 100 lifetimes, a lot of players who used to be paid decently now get scraps, and the group in the middle is shrinking quickly. No, wait, that’s America!” (@hankschulman 3/20/19).

Indeed, the pay disparities developing in major league baseball roughly parallel those that have developed in the general economy. (Mind you, no one need weep for the lowest-paid major leaguers; their minimum wage is about a half-million dollars a year. The real proletarians are average minor leaguers; they effectively earn less than the national minimum wage under difficult working conditions.) Another commonality between baseball and American economics is how massive data-crunching has helped produce growing inequality.

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Big Data” and “Digital Humanities” are two of the hot terms – “with a bullet,” as they used to say on the pop music charts – in the academy these days. The terms label a variety of projects: preserving large archives by digitizing them and crunching vast amounts of raw data to address topics in the humanities, such as visualizing the economic interconnections of ancient China, mapping the lines of influence among abstract artists, and finding out who authored the anonymous Federalist papers (although that was answered 50 years ago here).

An article in the summer issue of Social Science History by Marc Engal is a nice example of both the kinds of discoveries that might be found and the kinds of pitfalls that might be encountered while tramping through the Big Data jungle. Engal seeks to describe in numbers the thematic evolution of the American novel by drawing on Google’s “Ngram” program. This is a publicly available resource that tallies the words that have appeared in millions of books from before 1800 through 2008. We’ll see what a fertile terrain of  findings it offers — and how one can easily get tripped up exploring them.

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