If it seems like you’ve read dozens of articles about Aroldis Chapman potentially being a starting pitcher over the years, it’s only because you’ve missed hundreds more. This is an old topic, as is usually the case with relievers who appear to have the potential for more. The Reds invested a lot in Chapman when they first signed him, and as of 2012 he’s mastered the bullpen. Coming into 2013, Chapman was to move into the rotation, but now it’s a question again. Sometime within the next few days, the Reds hope to decide whether Chapman will open as a starter or a reliever. We’ve done this before, and we’re doing it again.

This has all come to a head once more because of something Chapman said over the weekend. From an AP article:

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Due to an unfortunate data error, the numbers in this story did not include park factors upon publication. We have updated the data to include the park factors, and the data you see below is now correct. We apologize for the mistake.

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What’s all this, then? For an explanation of this series, please read the introductory post. As noted in that introduction, the data is a hybrid projection of the ZIPS and Steamer systems with playing time determined through depth charts created by our team of authors. The rankings are based on aggregate projected WAR for each team at a given position.

A note on what you’re going to see below. Below, in accordance with the series, you’ll see all the teams ranked 1 through 30, based on projected shortstop WAR. The team ranked #1 will be in a much better position than the team ranked #30. That’s how rankings work. However, how much separation is there? Between #1 and #30, a lot. Between #1 and #2, a lot. Between #2 and…well here’s a chart I made:

In terms of projected shortstop WAR in 2013, the gap between #1 and #2 is bigger than the gap between #2 and #15. This isn’t, of course, great science, even if it is science. This isn’t, of course, how things are actually going to work out. But this gives you a sense of the spread, and it gives you a sense you shouldn’t care about the ranking as much as you care about the WAR. This, as you might realize, is one of the issues with prospect lists — the slope is never perfectly linear. As long as you know that going in, you won’t misinterpret what you see. Let’s get on now with the actual list, so you can see who’s #1, and who isn’t.

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Due to an unfortunate data error, the numbers in this story did not include park factors upon publication. We have updated the data to include the park factors, and the data you see below is now correct. We apologize for the mistake.

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What’s all this, then? For an explanation of this series, please read the introductory post. As noted in that introduction, the data is a hybrid projection of the ZIPS and Steamer systems with playing time determined through depth charts created by our team of authors. The rankings are based on aggregate projected WAR for each team at a given position.

With the intro out of the way, we have to start this series somewhere, and I can’t think of a compelling reason not to start with the catchers. So, we’re going to start with the catchers, and yes, since the rankings are based on imperfect projections and subjective depth chart determinations, there are quibbles to be had here if you’re the type who enjoys quibbling.

Especially because catchers occupy the position about which we probably know the least. Oh, we know a lot about how catchers run and hit, and we know something about how they throw, but we’re still in the beginning stages of understanding the importance of handling a pitching staff. There’s been some groundbreaking research in the study of pitch-framing, but those numbers aren’t included here. There’s a lot more than pitch-framing, too, which also isn’t included here. So while, below, you’ll find rankings based on what we can measure, I’ll take care to note when I think a ranking might be off for other reasons. With that all expressed, let’s start from the top.

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Begin with the principle that spring-training stats are meaningless. Use that as your rule of thumb and far more often than not, you’ll end up in the right. Player X mashes a dozen dingers before the end of camp. It’s probably meaningless. Pitcher Y finishes with an unheard-of strikeout-to-walk ratio. It’s probably meaningless. Team Z ends up with a really good or really bad record. It’s probably meaningless. We know this. If you’re reading FanGraphs, you know this. People try to tease meaning out of spring-training statistics, but the meaning is almost impossible to find.

Still, we talk about spring-training statistics, mostly because they’re our first new statistics after months of desolation. By and large we can’t help ourselves, and we trick ourselves into believing we’re better than we are at separating signal from noise. We look for numbers that seem to be out of character. We consider other factors that might give numbers more substance. I turn your attention now to Rick Porcello.

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Previously, it was Oliver Perez. Earlier in his career, Oliver Perez was a good major-league pitcher. Then he was a worse major-league pitcher, then he was a bad major-league pitcher, then he was nothing, thought to be hopeless. Perez resurfaced in winter ball, allegedly reaching his old velocity levels as a reliever. The Mariners gave him a chance, and he worked his way into a big-league bullpen. Perez will be back in that bullpen in 2013, armed with a rich new contract, and just like that, Oliver Perez has been resurrected.

Now we have the case of Scott Kazmir. Earlier in his career, Kazmir was a good major-league pitcher. Then he was a worse major-league pitcher, then he was a bad major-league pitcher, then he was nothing, thought to be hopeless. Kazmir was awful the last time he pitched in affiliated ball, and he wasn’t particularly good with the independent Sugar Land Skeeters. Kazmir resurfaced in winter ball, allegedly reaching his old velocity levels as a starter. The Indians gave him a contract, and right now Kazmir is on his way to making the starting rotation out of camp.

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It’s no secret that there are some differences between the numbers posted on FanGraphs and Baseball-Reference. Even though, in theory, they’re measuring the same things, not everything is black and white, so differences are unavoidable so long as the two sites draw data from different sources. The most well-known case is probably how FanGraphs’ WAR has been different from Baseball-Reference’s WAR. But there are other, less visible cases where the numbers don’t quite match up.

One such case was brought to my attention Monday morning by colleague Carson Cistulli, whose last name I apparently still haven’t added to my Firefox browser dictionary. The idea wasn’t to suggest something had to be written; the idea was to suggest, if something were to be written, it would be written by me. Below, please find the rest of this post.

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You might be wondering where Dave Cameron is. He’s presently down in Arizona, at the SABR Analytics Conference. You’re probably not wondering where David Appelman is. He’s presently also down in Arizona, at the SABR Analytics Conference. Plenty has already been shared at the conference, and plenty more will be shared later on, but before the FanGraphs contingent and also several others, an announcement was made pertaining to the Rawlings Gold Glove Awards.

We’ll draw straight from the Rawlings official website. Excerpted:

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Chase Utley gets hit by a lot of pitches. Pretty much always been that way. Over his career, about 8% of the time Utley has reached base, he’s reached base on a hit-by-pitch. He led the league in hit-by-pitches for three consecutive years between 2007-2009. Utley ranks fourth among active players in career hit-by-pitches, and he ranks 21st all-time, sneaking up quickly on Brady Anderson and Fred Clarke. Since 2000, 676 different players have batted at least 1,000 times in the major leagues. The average player got hit in 0.9% of his plate appearances. Utley has gotten hit in 3% of his plate appearances, ranking near the very top of the list (albeit well below Carlos Quentin). Utley gets hit, and people have noticed.

Now, usually, people try to stay away from making direct accusations. There’s often the suspicion of an intentional hit-by-pitch, but it’s usually just alluded to, rather than stated outright. On the matter of Chase Utley maybe getting hit all the time on purpose, Charlie Manuel follows the pattern:

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Last season, after joining the Nationals, Gio Gonzalez threw 3,198 pitches in the regular season, plus 209 more in the playoffs. That is an awful lot of pitches, but this article isn’t about that sort of pitcher abuse. It’s about a different sort of pitcher abuse to which the headline can also misleadingly refer. Of those thousands and thousands of pitches thrown by Gonzalez in 2012, many were thrown to opposing pitchers. It is on those pitches that we’re going to focus.

Pitchers have a lot of success when they’re pitching against opposing pitchers as batters. This is because pitchers are pitchers and not batters, and pitchers who are better at batting than pitching tend to become batters instead. Last season, pitchers struck out in 37% of their plate appearances. They struck out in nearly 42% of their plate appearances that didn’t result in sacrifice bunts. Collectively, they posted a .162 OBP. Collectively, they posted a .165 slugging percentage. Pitchers suck at hitting! You come to FanGraphs for the cutting-edge analysis.

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