We’ve spilled a great deal of virtual ink and audible podcasting words on the nature of Wins Above Replacement (WAR) and defensive metrics recently. Jeff Passan of Yahoo! Sports and many who responded to his critique of the current WAR calculation dug into the relative merits of the metric itself and how well we’ve estimated it to date. That’s a great conversation to have and Dave has done the heavy lifting on behalf of FanGraphs in that regard. I’d like to pivot and discuss a very important point about the use of statistics in baseball: Everything has flaws.

Every single statistic is wrong. Your eyes are wrong. It is all wrong. Nothing we have will provide you with perfect information or even truly accurate information with respect to the underlying variables about which you care. You don’t get to choose between flawed and not flawed statistics, you get to choose between useful and not useful statistics. More importantly, statistics become useful based on your awareness of the proper way to wield them.

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There’s a decent chance you’ve arrived at this page without a serious desire to hear more about defensive statistics. Trust me, I understand your frustration and your fatigue. Defensive stats like Ultimate Zone Rating and Defensive Runs Saved are controversial in some circles because they are reasonably new and the underlying data is somewhat hidden from view. You hear words like “flawed,” “absurd,” and “subjective” surrounding them. You’re tired of it.

Yet I’d like to lay out why we have advanced defensive statistics and how they work in the abstract. You won’t get to the end of this post and decide that UZR has perfectly measured Alex Gordon’s defense, but hopefully you will have a better appreciation for why we measure defense the way that we do.

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One of the things people love about baseball is that the game is both very simple and very complicated all at once. Baseball is simple in that all you’re trying to do is score more runs than the other team during 162 finite, nine inning contests. You are trying to reach base and advance runners and you are trying to prevent the other team from doing the same. How you go about doing those things is where baseball gets complicated. Jeff Sullivan often refers to baseball as being “obnoxiously complicated,” which I find to be a fitting description.

Think of all of the different possible outcomes of every pitch and all of the different pitches and locations from which the pitcher can choose. The complicated part of baseball is what makes baseball interesting, but the simple part of baseball is where you need to start to get your head around sabermetrics and player evaluation. Baseball is about producing and preventing runs.

As a result of that simple reality, the heart of baseball analysis is determining what leads to run scoring and run prevention. Specifically, how many runs is each possible action worth? If a player hits a single, how much has that player just increased his team’s odds of scoring a run? If a fielder makes a nice running catch, how many runs has he prevented? We don’t actually care about hits and walks and double plays, we care about how those finite events contribute to the overall goal.

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If you spent time hanging around the comments section of Dave’s Alex Gordon piece, you lurked in the shadow’s of his conversation with Jeff Passan on Twitter, or you’re one of those people who Twitter searches the word “FanGraphs,” you probably saw a decent amount of skepticism about single-season defensive metrics this week. People tossed around words like “flawed” and “absurd.”

The interesting part of the debate, for me at least, was that there was skepticism from both sides. The sabermetric elite dove into an esoteric debate about how to best incorporate defense into WAR and less analytically minded fans used Gordon passing Mike Trout in WAR as kindling for their “WAR is silly” crusade.

Dave’s piece does a nice job covering exactly what it means to say Alex Gordon leads position players in WAR, but the fact that Dave had to write that piece in the first place speaks to a problem we often run into when using advanced metrics. It’s a communication problem. Dave addresses it, but I’d like to expand on it here because it’s vitally important.

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One of the things baseball fans and analysts work very hard to do is isolate individual performance. At the end of a game, there is a final score that tells you how many runs each team scored. At a very basic level, that’s all that really matters. Baseball is a battle to score more runs than your opponent over the span of nine innings repeated 162 times. Yet analyzing the game requires more information than that because we want explanations. We want to know which players are good and which players aren’t so good. We care about how individual performance contributes to winning.

For pitchers, this is especially difficult because while pitchers have a huge impact on the number of runs they allow, they don’t have complete control. You can’t just look at the number of runs a pitcher allowed and say they were definitively responsible for those runs and call it a day. You aren’t isolating their performance and if you aren’t isolating individual performance you’re looking only at outcomes, and that’s not typically very interesting.

Every statistic, or really any analysis in general, should start with a question. On a basic level, the question we have is “How good is this pitcher?” which more specifically translates into “How effective is this pitcher at preventing runs?”

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Batting Average on Balls in Play (BABIP) is actually a pretty tried and true part of the baseball vernacular. Sabermetricians may have given it a long name with a fun-sounding acronym, but the principle goes back as far as presidential first pitches and wooden bats. Everyone knows that bloop hits and seeing eye ground balls go for hits quite regularly and that screaming rockets get snatched out of the air by leaping defenders pretty often. You couldn’t find a baseball fan alive who would argue with you on that simple fact.

BABIP is really just the amalgamation of all of those screaming rockets and bouncing grounders. When a batter puts the ball in play, it either goes for a hit or it doesn’t. Sometimes it’s a clean single, sometimes the defender can’t quite reach it. It’s a game of inches and these things happen.

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In addition to updated glossary entries and blog posts extolling the virtues of various sabermetric statistics and principles, the revitalized FanGraphs Library is also going to be a place where we highlight features available at the site that will allow you to get the most out of our data.

Below, you’ll find everything you ever wanted to know about the FanGraphs Leaderboards. If you’ve been a long-time reader who never misses a single post, a lot of this might be old news. If you’re anything short of that, there’s a good chance you’ll pick up a few tricks to get the most out of the site.

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This introduction is a setup. Don’t fall for it. I’m going to present you with two stat lines and ask you to silently compare them. Your job is going to be to determine which player had the better season at the plate. Remember, it’s a trick.

• Player A: 697 PA, .372/.463/.698, .476 wOBA, 42 HR, 59 2B, 103 BB, 61 K
• Player B: 716 PA, .323/.432/.557, .423 wOBA, 27 HR, 39 2B, 110 BB, 136 K

If I hadn’t primed you, it would be hard to suggest anything other than that Player A had the better season. He’s leading everything, except for a slight disadvantage in walk rate. Player A had the better season, right? It’s obvious. Even though I told you it was a trick, you’re still struggling to find a way to argue the opposing side. I’m telling you that Player B actually had the better season, but that’s because I have more information. I know a couple of important pieces of information that you don’t have and it makes a world of difference.

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Despite all of the rhetoric and talk-radio bluster, sabermetric principles and statistics aren’t actually very complicated. It might take a sharp statistician or savvy programmer to derive perfect park factors, but it doesn’t take anything more than a curious mind to understand and apply the basics. In my time working to help spread these principles, one of the most common and useful questions I get is about which few statistics a person should learn when trying to get into the world of advanced stats.

On Wednesday during my chat I got such a question. Here’s how I responded:

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