In one camp, the one that includes those who find the new-wave of baseball statistics credible, Jeter ranks as a middling shortstop at best, thus the award is a sham. In the other camp, the one that includes those who for one reason or another reject the new-wave of baseball statistics, the award is justified since Jeter made only a handful of errors this season.
This is an overly simplified description of the conflict. But I’m not really interested in exploring which position is more correct. Instead, this post is meant to be an explanation of the challenges facing journalists who cover baseball, especially to those who want to convey the ideas of statistical analysis.
For the record, I am a member of the first camp. A few years ago, I picked up a book called “Baseball Between the Numbers,” and it served as my introduction to new baseball statistics. The book exposed the flaws of our old standbys — for example runs batted in, batting average, wins and losses for pitchers — and proposed the use of new measures to help fix those flaws.
To me, the ideas the made sense, and they have changed the way I look at baseball.
All of this comes back to a question I see a lot. Why aren’t advanced statistics featured more in the mainstream media? Well, there are reasons, and they go beyond the “because baseball writers are ignorant slobs.”
One of the biggest problems I face on a daily basis is how much to incorporate those ideas into my work. There are no easy answers.
Though there are thousands of web sites devoted to the study of and exploration of statistical analysis, for various reasons they remain outside of the mainstream. Yet, because new stats have increasingly become a part of the decisions behind the game, they can’t be dismissed as being on the irrelevant fringe.
With so many teams now employing these new concepts, I would feel as if I weren’t doing my job if I simply did not acknowledge them. However, doing so is a risky proposition because the new stats are so easily misunderstood by most of the audience. If I confuse the audience, I once again would feel as if i weren’t doing my job.
For example, weighted on-base average (wOBA) is a statistic that tries to capture a player’s overall offensive contribution. I find it to be a useful measure and a big part of the way I view a player’s worth to a club. Yet, the statistic can be confusing, and it starts with the name. Though “on-base average” is used in the name, the statistic measures far more.
Then there’s the matter of actually explaining how the stat works and how it is calculated. That’s no picnic.
Journalists also face another major hurdle. Never in the history of mass communication have audiences been so splintered, and the Internet has hastened that divide. This is especially problematic for those who work for publications that are still trying to reach as wide an audience as possible, such as newspapers.
But even within my role of working for a newspaper, my audience changes vastly depending on which medium I use. Let’s take the Jeter situation as an example. As the Yankees beat writer for the Star-Ledger, I find that I write for:
1.) The people who read the print edition of the newspaper.
2.) The people who read the Web edition of the paper.
3.) The people who follow via Twitter.
Each audience is vastly different, and predictably, the reaction of each to Jeter winning the Gold Glove differed greatly.
Those who left comments on our web site, which seems more similar to the audience that reads the print edition of the paper, seemed to think that Jeter deserved the award. They mocked the new-school statistics.
Yet, on Twitter, the reaction seemed to be just the opposite. Within moments of posting the news of Jeter’s award, my feed was flooded with fans that expressed their rage at Jeter winning such an honor when his defensive statistics screamed that he was far from deserving.
I saw a similar division awhile back when I wrote about the silliness of small ball, another issue that seems especially divisive among baseball fans.
I suppose one solution is to approach writing to each audience differently. And to a certain extent, I already do this.
For instance, if I’m writing a story for the newspaper, I try to be more judicious of my use of new statistics, simply to lower the risk of being misunderstood. When I use them, I try to explain them as best I can. But if I send out a Tweet, intended for a much narrower audience, I don’t filter myself at all.
Still, this doesn’t solve the fundamental problem.
To me, simply ignoring statistical analysis would be akin to refusing to speak with scouts, who also wield a tremendous amount of influence on how the game is ultimately played. Perhaps stats haven’t gone mainstream. But they have influenced front offices all over the game. That fact alone warrants their inclusion in the coverage of the game.
So, what exactly is the right balance?
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