Data Reporter Uses Numbers to Answer Pop Culture's Biggest Questions

The Man Who Quantifies Pop Culture

Ben Blatt's job description at The Upshot, the data and analysis vertical of The New York Times, sounds deceptively simple: ask questions that everyone is already arguing about, then answer them with numbers. The questions he chooses, however, are anything but simple. When Taylor Swift began appearing at Kansas City Chiefs games alongside Travis Kelce in 2023, a national debate erupted over whether her presence was genuinely moving the ratings needle or whether the sports media was generating heat without light. Blatt was the kind of journalist built to settle it. His instinct is to reach for a dataset where others reach for an opinion, and his track record at The Upshot spans everything from woodworking safety regulation to pedestrian deaths to how airlines schedule flights so they always look good on paper on time.

That range is not accidental. Blatt is both a statistician and a journalist, a combination that makes him equally comfortable in the weeds of a regression model and in the middle of a mainstream cultural conversation. Before joining the Times, he was a staff writer at Slate and at The Harvard Lampoon, building a portfolio that stretched across Seinfeld, mapmaking, The Beatles, and Jeopardy. The throughline in all of it is the same: take a question that feels qualitative, build a quantitative framework around it, and see what the data actually says.

Numbers and the NFL

The Taylor Swift question became one of the defining data journalism assignments of the 2023 football season. Blatt approached it the way any serious statistician would: not by noting that ratings were up when Swift attended games, but by asking whether they were up more than baseline trends would predict. That distinction matters enormously. The Chiefs were already a marquee draw, and Sunday Night Football had been climbing for years. Isolating a "Swift effect" required controlling for those variables, not simply pointing at big numbers.

The broader sports and media world was paying close attention to the same question. Nielsen data from the Chiefs-Jets game showed the broadcast averaged nearly 27 million viewers across all platforms, the biggest audience of that season's Sunday Night Football slate, with women viewers growing in particular. What Blatt's approach adds to that kind of surface-level reporting is analytical rigor: the difference between correlation and causation, and the discipline to say so clearly when the data doesn't yet yield a clean answer.

Literature by the Numbers

Blatt's work extends well beyond the sports and entertainment beats. His 2017 book "Nabokov's Favorite Word Is Mauve," described by The Wall Street Journal as "enlightening" and called "Nate Silver-esque" by O, The Oprah Magazine, applied the same data-first instinct to literary analysis. The premise was straightforward and the execution was revelatory: load thousands of books into a vast database, crunch the numbers, and find out what they reveal about how great authors actually write versus how they say writers should write.

The title finding alone is striking. Vladimir Nabokov used the word "mauve" at a rate 44 times higher than its average occurrence in a large sample of written English assembled by linguists at Brigham Young University. J.K. Rowling's three most characteristic words turned out to be wand, wizard, and potion, which says something obvious but also something instructive about what defines a writer's fingerprint. Danielle Steel, Blatt found, has a particular fondness for opening chapters with weather descriptions, a habit that writing guides routinely warn against. Elmore Leonard, meanwhile, famously hated exclamation points, and the data bore that out.

One of the book's most practically useful findings involves adverbs. Ernest Hemingway, Stephen King, and many other celebrated writers have advised against using -ly adverbs, words like "quickly" or "angrily." Blatt tested whether the greats actually follow that advice. The answer: they do. The best-reviewed books use a lower rate of -ly adverbs, and books with more than 150 adverbs per 10,000 words received favorable reviews just 16 percent of the time. The data vindicated the advice that writers had been giving each other for generations, but it took a statistician to prove it empirically.

The book also traces broader shifts in reading culture. Using Flesch-Kincaid readability scores, Blatt found that bestseller readability has dropped from roughly an eighth-grade reading level in the 1960s to a sixth-grade level today, a finding that raises pointed questions about the relationship between accessibility and commercial success.

The Mathematically Optimal Road Trip

Before the literary analysis, Blatt co-authored "I Don't Care If We Never Get Back" with Eric Brewster, a book that was essentially a data journalism project turned into an adventure narrative. The premise: design the mathematically optimal baseball road trip to attend a game at all 30 major league ballparks in 30 days, then actually do it, covering 20,000 miles without boarding a single plane. The project combined algorithmic thinking with old-fashioned physical reporting, and it established the pattern that defines Blatt's career: the algorithm isn't the end of the story; it's the beginning of one.

Why the Approach Works

What makes Blatt's journalism distinctive is not simply that he uses data, but that he uses data to answer questions people are already emotionally invested in. The Taylor Swift-NFL ratings debate was not an academic exercise; it was a genuine cultural flashpoint, with partisans on both sides. The adverb question is not just a stylistic curiosity; it's advice that writers actually follow or ignore. The baseball road trip question has a practical dimension for anyone who has ever tried to plan a ballpark tour and gotten overwhelmed by the scheduling complexity.

His work has appeared across The New York Times, The Guardian, The Wall Street Journal, The Boston Globe, and The Atlantic, reaching audiences who might not otherwise think of themselves as data readers. That breadth reflects something true about the method: when the question is interesting enough, the numbers become interesting too. Blatt has spent his career proving that the best data journalism is not about the data at all. It is about finding the right question first, and then letting the numbers do their work.