Advanced stats might seem like a complicated foreign language, but becoming conversational is actually very easy. Learn how to analyze a hitter's numbers.

Based on years of watching television, I've learned that arriving to a party exactly on time is uncool. I'm not sure who started that social norm, but it seems to have taken hold. As a result, people arrive late to things that don't have a formal beginning. One such thing is the sabermetric appreciation of baseball. If you grew up reading Bill James at your father's knee, you're unusual. Most people show up halfway through the party and sometimes when you show up late, you don't know where to put your coat and you're too embarrassed to ask. You wander around the house opening doors hoping to find a pile of coats. Sometimes you find it, but sometimes you give up and just wear your coat and pretend you're cold.

At Beyond the Box Score, we'd like to make an effort to help you find the coat room. If you're well-versed in advanced stats, this post isn't going to tell you anything you don't already know. However, if you're curious about how we do things in the modern age but aren't really sure where to start, this might help.

Oftentimes, people ask a FanGraphs writer or a well-established stathead where to start and their typical answer is to start with The Book or to click around in the FanGraphs library. While both of those contain excellent information, they're sort of backwards. The reader has to decide they want to know what wOBA is and then learn about it. What if you don't know where to start? First, you need to know what questions to ask. Then, you move on to learning about the metrics we have which answer those questions. We use numbers in baseball to tell a story and analyze what we see. You can love baseball without ever looking at a single statistic, but if you're going to look at a number or many numbers, looking at the ones that tell the most accurate story is ideal. We all want to understand and appreciate the game.

The rest of this post will walk you through the process of evaluating a hitter using sabermetric thinking and stats. If you want to know more about the game, but don't know where to start, hopefully this will send you on your way!

### Starting out

So you've arrived at the decision that you'd like to evaluate a major league hitter and you'd like to see if you can do so using the the best tools available to the public. In the future, we'll talk pitching, defense, baserunning, etc, but for now we'll walk through the process of measuring performance in the batter's box. Let's choose an example! Buster Posey.

So the first thing we're going to do is head over to his FanGraphs page. FanGraphs is one of the leading sabermetric websites which houses many of the most important stats. Baseball-Reference and Baseball Prospectus are also terrific, but if you're new to the game, I recommend FanGraphs because it's easier to navigate, in my opinion.

There's a lot of information on that page (the top of which appears above), and many links to other pages, so in order to focus our discussion, let's set out two important questions that we want to answer. How does Buster Posey's 2014 season compare to his previous seasons and how good of a hitter is Buster Posey?

### First steps

Since you're already a baseball fan, the numbers on the left side of the page are going to be very familiar and the numbers as you move right are going to look more foreign. Home runs, runs, RBI, stolen bases, and such are all very easy to understand. You know what 30 home runs mean, but let's start to dig deeper.

First let's move to BB%, or walk rate. This tells you how often a player walks when they're in the box. Walks might seem boring, but walks are very valuable for a hitter. Think about it like this: A walk is the same thing as a single with respect to how far it advances the batter. It's not quite as good as a single, however, because it only advances the baserunners one base while a single might advance them two bases under the right circumstances. So if we factor in how frequently a man is on base and how often a single might advance that runner more than one base, you come to recognize that a walk isn't as good as a single, but it's still a great outcome. In other words, more walks are better than fewer walks.

So let's pay attention to walks, but also to how often a hitter hits for extra bases. When we evaluate hitters, we're trying to decide how well they contribute to run scoring—that's always the question. We've already decided that we want to count walks to some degree, but now, we also want to reward hitters who hit for extra bases, because it moves them closer to the plate and it advances any runners further along than a single would have.

Batting average tells us hits divided by at bats. We want to also factor in walks, which gets us to on base percentage (OBP). That's good, but we can do better. Slugging percentage tells us something about extra base hits, but it tells us nothing about walks and assumes a double is equal to two thirds of a triple with respect to its contribution. Better, but not best.

We'd like a stat that tells us exactly how much a hitter contributed to run scoring combining these two factors. Luckily, we've got one!

### Finding wOBA

Weighted On-Base Average, or wOBA (often pronounced Whoa-buh), is a statistic that uses linear weights (read: math) to determine exactly how valuable each offensive outcome truly is. We intuitively know a single is better than a walk and a triple is better than a double, be we can actually compute the precise values. Don't worry, you don't have to do the math, you only need to read the output.

If you're familiar with OPS, wOBA is just a more accurate statistic that tries to answer the same question. wOBA is designed to be scaled to OBP, so a league average wOBA for 2014 position players is .322, but you can think about it as .320 if that makes things easier.

wOBA is a great statistic. It tells you almost everything you want to know about a player's offensive results right there in one number. But there are two problems which one more statistic is going to help us solve. One thing about baseball is that the style of play is always changing. Right now, pitchers are dominating; a decade ago, hitters were king. In order to evaluate a hitter, we want to compare them to their contemporaries, not guys who were facing Bob Gibson in 1968. We need to adjust for the era they play in, but we would also like to adjust for the ballparks they play in because, as you know, Coors Field and Petco Park are really different. The same swing on the same pitch in one leads to a different result in the other. How can we solve this?

### Park and league adjustments

The solution is to use Weighted Runs Created Plus (wRC+), which takes park and era into account. It's very simple to read, too. League average is always 100, so the average hitter in 1998 and 2014 would both have a 100 wRC+. If you don't want to compare hitters like that, stick to wOBA. wRC+ also makes adjustments for park, meaning that you don't have to worry that Buster Posey plays in AT&T while Joey Votto plays at GABP.

Simple enough? You want to know which hitters are contributing to run scoring when you evaluate hitters and wRC+ does the trick. You don't have to remember league average because it's always 100 and every point above or below is a percentage point above or below league average. So a 110 wRC+ is ten percentage points better than league average at the plate. You now have one statistic that is easy to understand that combines all of the relevant information. Done!

### Is the player for real?

Now that we have a statistic that measures offensive production quite well, we now need to learn how to decide if this is a streak or a real measure of their ability, so let's hit a couple of other important numbers.

#### Must Reads

First, let's take a look at plate appearances (PA). This isn't a new stat for you, I'm sure, but it's important to know that baseball is a weird sport and basically anything can happen in a small number of trips to the plate. If your hitter only has 30 PA this year, you're better off just assuming they're only as good as their last season or career rather than this weekly sample. However, a sample of 500 PA is more likely not simply due to luck.

The dirty little secret of sabermetrics is that there isn't a magic number of plate appearances at which statistics start to matter. More is always better, fewer is always worse. Generally, 100 PA or so will be enough to calm down the crazy fluctuations in walk and strikeout rate and something closer to 400 or 500 will do the trick for most other stats except for one key exception, coming in a moment. Remember, though, a 150 PA sample isn't meaningless, you're just less sure about your evaluation than if you had 1000 PA. Don't ignore small samples, be appropriately cautious.

So now that you've decided how meaningful your sample is, let's look to see if our hitter is capable of keeping this up whether that's a good thing or a bad thing. The most important number to check first is their Batting Average on Balls in Play, or BABIP. The premise behind BABIP is that balls in play (i.e. not a home run, walk, strikeout, or sac bunt) tend to fall for hits based on three factors. The first his how well the ball was hit, the second is the quality of the defense, and the third is dumb luck. We care a lot about the first thing, but you don't want to penalize a hitter for the second two. Luck and defense will eventually even out over a big enough sample, but it can take 500 to 1,500 PA in some cases.

So what does this mean? Most hitters have a BABIP of about .300. Good hitters will have higher BABIPs, but no one runs hotter than about .380 and no one runs colder than about .250 (other than pitchers). So a good rule is that if a player's BABIP is above .400 or below .250, they're going to come back to the pack no matter what. They aren't guaranteed to regress to .300 exactly, but you won't live out in the extremes for long.

My rule of thumb is that any BABIP change from year to year of more than about 15 points should be viewed with caution. If you can tie the change to something meaningful, like a mechanical adjustment or more line drives, they can probably sustain it, but otherwise, always expect their long run BABIP to take over. Line drives go for hits more often than ground balls and fly balls, so if you're all of a sudden hitting more line drives, the BABIP spike might be real, for example. Practically speaking, if your hitter is running a really high BABIP and they've never done that before, you're probably looking at luck. Same thing if it's too low. You can always dig deeper, but this is an introduction.

You also want to glance at home/road, platoon, and situational splits. If your hitter has always been great against lefties and bad against righties, and all of a sudden they're hitting righties, that's something to investigate. Another place to look is the Plate Discipline section of a player's FanGraphs page, which tells you how often a hitter swings at pitches in and out of the zone and how often they make contact when they do. Big changes in either of these profiles can often lead to meaningful differences in results.

### Putting it all together

So I've given you a ton of information already and I feel like I've only scratched the surface. I haven't given you any specific formulas or explained how we get from these numbers to Wins Above Replacement (WAR), which is probably a stat about which you've heard plenty. But for now, we're just talking about how to evaluate a hitter's season. So let's put together what we've learned and try to answer our two questions about Buster Posey.

First we want to compare this year to his previous seasons (stats as of May 23).

Season PA BB% K% BABIP AVG OBP SLG wOBA wRC+
2009 17 0.0 % 23.5 % 0.154 0.118 0.118 0.118 0.105 -51
2010 443 6.8 % 12.4 % 0.315 0.305 0.357 0.505 0.371 134
2011 185 9.7 % 16.2 % 0.326 0.284 0.368 0.389 0.335 116
2012 610 11.3 % 15.7 % 0.368 0.336 0.408 0.549 0.406 163
2013 595 10.1 % 11.8 % 0.312 0.294 0.371 0.450 0.357 133
2014 171 12.3 % 11.7 % 0.279 0.279 0.368 0.442 0.350 127

We can completely ignore his 2009 season because it was 17 PA, and after that Posey has always been above average, ranging from 116 to 163 with two seasons around 130 wRC+. He's a very good to great hitter. This year, he's at 127. That's a little worse, but not too much worse.

He's walking more, which is nice, and his power numbers don't seem to be down. Take a look at his BABIP and you'll see it's the lowest mark of his career by a decent amount. Odds are that if you give Posey 400 more PA, those hits will start to fall and his wRC+ is going to climb up a little closer to 140 than 127. Posey is having a very Posey-like season. Good.

Now let's decide if Posey is a good hitter. We know he has a 127 wRC+ and that his BABIP is probably holding him back a little bit. How does this compare to everyone else?

Among qualified hitters, Posey ranks 47th this year. He's 27 percentage points better than the average hitter, but he's not an elite hitter overall. But now let's look at catchers. Among players who play most of their games at catcher (min 100 PA total), Posey is tied for 6th behind Derek Norris (!), Jonathan Lucroy, John Jaso, Yadier Molina, and Matt Wieters. Posey isn't the best hitting catcher in baseball right now, but he's right near the top despite a lower than expected number of hits on balls in play. So yes, Posey is a good hitter and a very good hitter for his position this season.

You should always make use of as much information as possible when evaluating a hitter, but if you want to do so in a straightforward way, take a look at their wOBA and wRC+ because those are the best reflections of performance. Then poke around at PA and BABIP to see if this is lucky or sustainable. Then compare them to the rest of the league and others at their position to see where they rank.

You don't have to be a math wiz to take a big leap forward in analyzing hitters. If you're using HR, RBI, and batting average, you can do better. Those aren't meaningless, but they leave out important information and if you're going to look at a statistic anyway, it might as well be the best one possible. You don't need to learn how to run a regression or write computer code. This is about finding the right statistic for the questions you've been asking yourself since you were a sleep deprived eight year old sneaking downstairs to watch the west coast games. There are lots of ways to learn more and there are many more nuances to cover, but hopefully this was a good first step toward and more accurate and rewarding use of numbers in your baseball viewing life.

. . .

All statistics courtesy of FanGraphs.

Neil Weinberg is the Associate Managing Editor at Beyond The Box Score, a contributor to Gammons Daily, and can also be found writing enthusiastically about the Detroit Tigers at New English D