National Sports Journalism Center

As arguably the most popular sports columnist in the country, ESPN’s Bill Simmons has a very big platform. Last week he used it to great effect for a good cause: reaching across the divide between baseball fans who grasp sabermetrics and those who don’t.

That divide may have narrowed in recent years, but it still remains – to the increasing detriment of fans’ understanding of the game.

I find sabermetrics fascinating, but I struggle to understand the stats and internalize them. Though I’ve tried, sabermetrics haven’t become part of my fandom the way blunt old tools such as AVG and ERA have been for years.

For a long time, I thought this was no big deal. I saw stats such as VORP and WAR as take-it-or-leave-it means of dissecting baseball: They were interesting to think about, but not essential. But I no longer think that’s true. The world is changing – not just in baseball but in all sports — and fans who want to understand what’s going on need to respond to that change.

I’m not saying you need a working knowledge of BABIP to appreciate a day at the ballpark – that would be every bit as pointlessly doctrinaire as grumping that advanced stats are nonsense because computers don’t play baseball. (Yes, I’m looking at you, Joe Morgan.) But more and more front offices are using sabermetrics to inform their decisions about trades, free agents and roster construction. Fans without a basic knowledge of sabermetrics will find themselves increasingly unable to assess those decisions. And this is where sportswriters come in: It’s our job to analyze those strategies and comment on them – to be fans’ eyes and ears and create raw material for countless discussions, debates and arguments. If we don’t understand sabermetrics, our analyses and commentaries will suffer.

Exhibit A is the changes made by the Boston Red Sox over the winter, which is what led Simmons to his own reluctant engagement with sabermetrics – and, to his surprise, an enthusiastic embrace of them. Simmons began by disliking Boston’s decision to remake itself as a team oriented around pitching and defense, but there was a huge gulf between how he viewed that strategy and how baseball friends who knew sabermetrics did. Sensing he was missing something, he educated himself about sabermetrics so he could make his own judgment about Boston’s makeover – and found that his efforts led him to change his judgment pretty thoroughly.

As sabermetrics become more important to more teams, more fans are going to have moments like Simmons had – a feeling that they’re not understanding their team’s strategy. Understanding advanced stats, I believe, will eventually be as important to discussing your team’s moves as knowing players’ contract statuses has become. That’s why the gap has to close. But who will close it?

The Web has a wealth of sabermetrics resources available to inquisitive fans, but I think more popularizers and evangelists are needed, both from the ranks of “traditional” media and from the sabermetrics community. Simmons has done his part, turning his conversion story into a primer about OPS, OPS+, UZR, VORP, WAR, BABIP, and FIP.

Where to start? WAR and FIP are the two stats Jonah Keri singles out as the easiest to introduce to tradition-minded fans. Keri – who helped walk Simmons through some aspects of sabermetrics – is a writer for BloombergSports.com and numerous other publications. He’s done a lot to popularize sabermetrics, as have writers such as Nate Silver, Rob Neyer, Joe Sheehan and Joe Posnanski. (Keri is also writing a book about the Tampa Bay Rays, due next spring.)

“The key to fostering understand and acceptance of any new idea is to make it intuitive and easy to understand,” Keri says. “The most basic currency in baseball is wins — there’s a winner and a loser in every game. A stat like Wins Above Replacement simply measures how many wins a given player adds to your team. Yes, the back-end calculations of offense, defense, park effects and everything else are complicated. But the output that a stat like WAR gives you is incredibly simple.

“Fielding Independent Pitching (FIP) is another one,” Keri adds. “Here you’ve got another stat with complicated input but easy-to-understand output. FIP focuses on what a pitcher can control — strikeouts, walks, home runs — and attempts to strip out the impact of luck, defense, bullpen support and other, similar factors. The best part is that it runs on a similar scale to ERA. So if a pitcher posts a FIP of 3.00 or lower, that’s terrific; if it’s 5.00 or higher, that stinks.”

Both of those stats resonate with me as well. WAR is a great way to size up your team’s roster and figure out which players are complementary to your stars and which aren’t pulling their weight. FIP is a needed corrective to ERA, which every fan above 10 understands isn’t particularly fair. As Keri notes, its biggest selling point is that it uses the same scale, meaning you could simply replace ERA with it if you liked. Along the same lines, I’m keenly interested in UZR (Ultimate Zone Rating) because it takes fielding beyond obviously flawed measures such as assists, putouts and errors, and BABIP (batting average on balls in play) because it tries to quantify who’s lucky and who isn’t. All of these stats dig into areas that most any longtime baseball fan knows aren’t measured particularly well by traditional stats and would like to understand better.

One problem some fans have with advanced stats is that calculating them is intimidating: Generations of young fans have used batting average to unlock division, but UZR and other statistical measures demand a knowledge of math I certainly never achieved. This doesn’t bother Simmons overmuch – he’s more interested in the output than the input. It used to trip me up but I’ve let go, focusing on trying to understand what’s being measured and what the scale is. But as Keri notes, some fans will feel differently.

“Some people don’t trust any stat, or any concept without seeing its inner workings,” he says. “It’s tough to blame them, too – blind trust can be a dangerous thing. But if they want to peek behind the curtain, they have to be ready for some complex formulas, because many of these stats took a great deal of machination to produce.”

So what needs to change to make sabermetrics more mainstream? A few thoughts:

* Education about baselines: I’ve known since I was a kid that a .300 batting average is great. I’ve learned as an adult that a .300 on-base percentage is awful. I’ve struggled to add to my repertoire the fact that BABIP averages somewhere between .290 and .300 each year. Similarly, WAR really comes into its own when you know how many wins a team of plain-vanilla replacement-level guys would win. (It’s 47. At least I think it is.) Not knowing the baselines leaves sabermetrics newcomers feeling unmoored, which makes the stats a harder sell.

* Less preaching to the choir: I think the willful, proud ignorance of sabermetrics that some veteran baseball writers have infamously demonstrated goes beyond juvenile to shameful – I don’t get why anyone who loves baseball wouldn’t be interested in trying to understand its complexities better and use that knowledge to write smarter stuff. But I’ve been driven away from some sabermetrically inclined sites by a mindset I find equally exasperating: a snarling impatience with and immediate dismissal of those who don’t look at the world through a sabermetric lens. I understand it’s wearying to see some front offices continue to ignore advanced stats, and I get that anti-intellectual Neanderthal attacks will make anyone prickly. But no one ever said changing the world would be easy. Being right is a great starting point, but it isn’t the whole battle. That will be won through more patience and evangelism.

* History as a teaching tool: I finally grasped the beauty of WAR by looking at the 1984 Mets and Cubs, who spent that summer in an agonizing pennant race. Breaking down the two teams’ rosters using WAR confirmed some things I knew (Keith Hernandez and Ryne Sandberg were awesome), forced me to rethink things I thought I’d known (Mookie Wilson was a lot better than I gave him credit for), and showed quite clearly something I’d suspected but not quantified: The Cubs’ complementary players thoroughly outgunned the Mets’. Using an unfamiliar stat to look at a familiar situation meant I started with my feet on firm ground. And because I was looking at the past instead of the present or the future, I was trying to understand things rather than predict them. Simmons’ statistical come-to-Jesus moment was about 2010, but I think for a lot of fans predictions are so bound up with prejudices that sabermetrics become an object of suspicion, rather than a chance to illuminate. It doesn’t have to be that way.

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, and about the newspaper industry at Reinventing the Newsroom. Write to him at  jason.fry@gmail.com, visit him on Facebook, or follow him on Twitter.