National Sports Journalism Center

The old war between advanced-stats folks and so-called traditionalists has started up again, sparked by a column by Jonah Lehrer about sabermetrics and our natural tendency to focus on quantifiable elements in making decisions, even when things we can’t measure may prove more important.

The subsequent argument brought out a lot of interesting things, from discussions of advanced stats in basketball to one of the best attempts at summing up the proper role of sabermetrics that I’ve read. But though the debate was a fairly rational one, I found myself weary – and wishing for a meshing of different disciplines in beat reporting about my favorite teams.

At its core, Lehrer’s Grantland column wasn’t an attack on sabermetrics – or at least I don’t think it was intended as one. Lehrer acknowledged that sabermetrics has dramatically improved teams’ personnel decisions, helping them find neglected talent and providing a check on executives’ instincts. But he argued, “sabermetrics comes with an important drawback. Because it translates sports into a list of statistics, the tool can also lead coaches and executives to neglect those variables that can’t be quantified.” Lehrer wasn’t saying those intangibles trump all, but worrying about the power of the quantifiable to sway us: “If we were smarter creatures, of course, we wouldn’t get seduced by the numbers. We’d remember that not everything that matters can be measured, and that success in sports (not to mention car shopping) is shaped by a long list of intangibles. In fact, we’d use the successes of sabermetrics to focus even more on what can’t be quantified, since our new statistical tools take care of the stats for us.”

Many writers critiqued Lehrer’s analysis, taking issue with points big and small. As an example of intangibles, Lehrer looked at the Dallas Mavericks’ use of the statistically underwhelming J.J. Barea in their upset of the Miami Heat, which sparked a lively debate over how to assess Barea and what stats the Mavs might have considered in giving him minutes. ESPN.com’s Tom Haberstroh makes a lot of good points worth noting here.

Beyond the Boxscore’s Bill Petti penned the best overall counterargument I found. Petti wrote, “Lehrer’s main argument shouldn’t be that teams are assembling bad teams because of a narrow-minded focus on things they can quantify. The argument should be that teams that don’t think deeply about what are the right metrics and how much variance they account for in player achievement will fail just as much as those teams that used to generally ignore analytical approaches to the game.

Data and statistics are not to blame for bad decisions — their misapplication is.”

Lehrer posted a follow-up on Wired, and the comments there are (mostly) very smart and high-powered – if only every web comments section were half as good. Among the commenters was Tom Tango, one of baseball’s leading sabermetricians, who offered this model explanation of sabermetrics’ goal and proper use: “What sabermetrics does is explain the numbers. Give the saberists the numbers, and he’ll tell you what it means. A saberist will NOT tell you anything else. What the saberist is going to do is tell you the LIMITS of numbers, of how far you can take the numbers. AFTER that, after the numbers have been parsed and exploited, THEN that’s where your scouts and your guts come in. And those are IMPORTANT activities that take place. … What sabermetrics does is allow the scout to focus on things OTHER than the numbers.”

There’s no shortage of critiques of Lehrer’s column, so I’ll keep mine brief: I thought his piece would have benefited from a thorough argument with a friendly reader before publication. (This is an excellent idea for any writer, of course.) Lehrer’s core argument was interesting, but the execution was flawed: Besides points such as Petti’s, his metaphors undermined his case and inflamed sabermetrically-inclined readers. Yes, a determined critic will chase any metaphor down a rabbit hole of applicability, but trotting out Philip Roth talking about a little boy experiencing an entire baseball game by watching numbers change on the scoreboard was an ill-advised choice. Sabermetrically-inclined writers and fans love the sights and sounds of baseball as much as anybody else, and Lehrer seems smart enough not to have suggested otherwise.

Still, Tango’s comment stuck with me – and made me think about what I’d love to see from beat writers covering my favorite team.

I’m not an advanced-stats guy by inclination – I see the season as a soap opera, and focus far more on players as characters and moments as echoes of team history. But I’m fascinated by sabermetrics, and even my limited understanding of it has made me appreciate baseball much more. I’m particularly interested in understanding how as born storytellers we’re quick to make judgments about character and pluck from what may be random good or bad luck.

My sabermetrics awakening came courtesy of Heath Bell, a mid-Aughts reliever for the Mets who shuttled between Triple-A and the majors, where he was inevitably whacked around. Sabermetrically inclined fans insisted Bell was simply unlucky, pointing to the then-foreign concept of BABIP, or batting average on balls in play. I didn’t see it – probably in part because Bell was kind of ridiculous looking, with a big butt and tiny feet. The Mets didn’t see it either: They shipped Bell off to San Diego, where his BABIP fell into line and he became a deadly closer. Lesson learned, at least for me: If nothing else, sabermetrics is a useful check on very human flaws in how we look for patterns and construct stories. More fundamentally, I simply can’t fathom why any fan of any sport would reject new tools offering a deeper understanding and appreciation of that sport.

Journalism is changing: Reporters in all fields are having to become conversant with multiple deadlines, publishing across media, handling audio and video, and being able to sort through data and crunch numbers. Sports reporters are ahead of most of their counterparts in many of these areas, so why can’t the same be true of advanced statistics?

Understanding advanced stats needn’t come at the expense of traditional reporting. The best beat writers are superb at getting players to talk about themselves and how they do what they do, and at asking managers and front-office people to explain their philosophies and react to events. When that reporting rises above the routine of postgame scrums and bland athlete clichés, it anchors fans to the larger narrative of a season and a sport.

I think – or perhaps I hope – that most reasonable people now accept advanced stats are an important way of measuring a player’s ability and likely usefulness to a team. That argues for familiarity with them being a standard part of the beat writer’s toolkit. At the very least, since more and more front offices look at such stats, beat writers need to understand them to explain why a team is making certain moves – or perhaps to ask why it’s not.

I’d like to know what advanced stats my team looks at, including any proprietary metrics. I’d like to know which team decision-makers take them into account, which don’t, and how that affects team moves. And I’d like the beat writer’s own take on what the stats show, and how to balance that against material gathered from solid reporting in the clubhouse.

Tom Tango thinks that’s an ideal for front-office types; I’d say the same for beat writers.

Jason Fry is a freelance writer and media consultant in Brooklyn, N.Y. He spent more than 12 years at The Wall Street Journal Online, serving as a writer, columnist, editor and projects guy. While at WSJ.com he edited and co-wrote The Daily Fix, a daily roundup of the best sportswriting online. He blogs about the Mets at Faith and Fear in Flushing [LINK http://www.faithandfearinflushing.com], and about the newspaper industry at Reinventing the Newsroom [LINK http://www.reinventingthenewsroom.com]. Write to him at jason.fry@gmail.com [LINK: mailto:jason.fry@gmail.com], visit him on Facebook [LINK http://www.facebook.com/jason.fry], or follow him on Twitter. [LINK http://www.twitter.com/jasoncfry]