Here’s a book idea I think some people have been waiting for. Bridging the Statistical Gap  by  Eric J. Seidman (Statistically Speaking, Fangraphs, and Baseball Prospectus)  aims to serve as a fan’s first foray into sabermetrics.  I did a quick interview with Eric, asking him to explain said gap, talk about his own path in baseball statistical analysis, and to give us an idea of what to expect from the book.

EWC: Can you explain the gap?

Eric:The gap is the sheer differential in mindsets between the more casual fans and the statheads. I’ve found, through numerous discussions, that this gap does not need to be as large as it is, primarily because the only real difference between the two groups is that statheads act on the intuition and questions they raise whereas more casual fans may have trouble understanding that they even have this intuition. For instance, many fans understand that a W-L record doesn’t necessarily equate to quality, or that a Batting Average may be high due to singles and not bigger/better hits. Whereas casual fans might not understand what they intuitively understand, statheads will go out and investigate. Since this is the only true difference, I figured that if more casual fans understood these numbers are not necessarily that hard to get, this gap might lessen.

EWC: Who’s the target audience here?

Eric:The target audience is anyone with a love of baseball who is interested in learning about statistical analysis. I call this the fan’s first foray into sabermetrics because I instruct through the usage of analyses. For instance, the first chapter is titled “The Great Batting Average Debate,” and we discuss why batting average is not an effective barometer of offensive performance. Instead of bashing the stat I explain the history behind it, discuss what it does and does not tell us, what we think it tells us based on how we currently use it, and show better/other numbers that offer different (and sometimes better) measurements of what we really seek. So, it’s like that: showing more casual fans easy ways to use numbers that are not intimidating in order to understand how we should be analyzing players and how certain numbers do/don’t work.

EWC: I think a lot of people see a statistic they’re unfamiliar with and not only turn off to it, but more or less rebel against it — or at least assume the writer, or the person utilizing said statistic, is attempting to go over the common fan’s head. You end up with a situation — like the Washington Post piece about VORPlast week — where the mainstream writer is bending over backwards to make it ok for people to read about something unfamiliar. Kind of apologizing for what they’re trying to convey. How did you deal with this in your book?

Eric: I’ve noticed that the majority of the times in which fans turn themselves off from a statistic stem from a lack of understanding with regards to the baselines. And that isn’t the fault of the fans, either, but rather more on those using or creating said statistic. VORP is an incredibly useful statistic, but if nobody knows what constitutes a good, bad, or average VORP, it won’t be used as much as it could be. Derek Jacques at Baseball Prospectus wrote a really good article a week or two ago defining these baselines. See, people know a .300+ batting average is good but they don’t necessarily know if an .840 OPS is good or slightly above average; or if a 54.23 VORP is really good or really just the sign of a solid albeit not a superstar player. In the book I started from scratch, essentially. I mean, literally, the first few pages go over even how to calculate batting average and ERA, the most common and easiest statistics. You don’t need to know anything really to understand what’s discussed in the book, and the reason I wrote it was to show these stats in usage. In the last chapter, I discuss VORP and show it in an analysis that conveys how useful it can be to a fan much more than me saying “VORP good, Batting Average bad.”

EWC: And on that point are some of the complaints valid? Are there people in the baseball statistics community that attempt to place themselves in elite company? Or is everyone just a baseball fan genuinely trying to further express their love for the game?

Eric:I mean, anywhere you go there are going to be those who are out for themselves, but generally speaking, most if not all of my colleagues seem like really good people as well as analysts. The ultimate goal of a GM is to build himself a team that will win and nowadays it also involves using methods that others might not know about in order to offer them advantages. Sometimes it is okay to try to know more than someone else but in the blogosphere, it is much more useful to try and advance the common knowledge for everyone.

EWC: I think Fangraphs– with its concise writing and short posts — does an excellent job in bridging the aforementioned gap, which is why I think your book might be of value to people interested in getting acquainted with baseball statistical analysis. Did your experience writing for Fangraphs help you formulate some of the ideas in the book? What are some other sources you think reach out in a similar way?

Eric:Most of what is in the book has either been mentioned or explored by myself on other websites, although in much smaller versions. It’s not the type of situation where it is a book of things I’ve already covered online but rather taking ideas I’ve written about and really beating the heck out of them. Writing for Fangraphs coincided with the book because I like to break everything down and reach out to all types of audiences. The ideas really came from one single thought - “How would I explain this statistic to my Bubbie.” Bubbie is Yiddish for grandmother, and mine knows numbers but not necessarily baseball. I thought, how would I explain FIP to her? And then I would get the idea that I should write a chapter about how W-L and ERA are not solid barometers, why they aren’t, and that way I can show FIP in an analysis instead of just saying “FIP good, ERA bad.”

EWC: Speaking as someone looking to bring baseball statistical analysis to a wider audience — where did your own breakthrough come? What books or writers drew you in? Was there ever a point when you read an article about Runs Created and said “Ah, this is bullshit!”?

Eric:I understood without understanding, so to speak, at a very young age through baseball video games. I knew certain batting averages were unrealistic through a certain period of time and that these players were prime candidates for regression—even though I didn’t know what regression meant yet, I had always known about Bill James but didn’t really start reading him until a few years ago, and I didn’t become REALLY familiar with all the other great writers until about two years ago. My breakthrough of sorts came from really liking Pat Burrell and disliking how so many write off his abilities because of a lower batting average. I knew his other performance indicators, much more indicative of his quality, told a better story.

EWC:What’s something that you’ve come across recently that someone who isn’t interested in statistical analysis might not be aware of, that they might understand better after reading your book?

Eric: Well, W-L records for pitchers are a pet peeve of mine because we tend to treat them as the barometer of pitching success. A guy was 14-6 so he had a good year. Yeah, well what if he got an inordinate amount of run support? Or what if they were mostly 5 inning games in which he surrendered 3-4 runs but was bailed out? W-L records are largely luck dependent and in the book I present a way to adjust the W-L record to show us what we think we’re looking at. People generally think W-L measures the amount of good games vs bad games, but it doesn’t. In the book I present an easy method to make this adjustment, so we can have a stat on the same scale as the common barometer but “corrected” to show us what we think it already shows.

EWC: Can you maybe break down a few chapters of the book to give  people an idea of what might be in store for people that buy it?  

Eric:   Chapter Four is titled MJ: Scouting, Splits, Sample Sizes, and in it I discuss how the combination of scouting and stats is very vital, how splits work, and what to make of small sample sizes by reconstructing the 1994 minor league season of Michael Jordan, thanks to photocopied scorecards the Birmingham Barons sent me.Chapter Eight is titled Save Rates and explores what goes into being a closer as well as why saves are invalid, as well as using what we intuitively equate to closer quality in order to better measure their abilities.

And, hmm, Chapter Six is titled Mr. Meaningless: Clutch vs. Stat-Padding, and it explores the history of the clutch concept as well as discussing all of the common definitions for the term as well as ways we can measure it.