This began with an observation: Since 2008 — covering the bulk of the reliable PITCHf/x era — Miguel Cabrera has swung at just under 47% of pitches with the bases empty, and just over 53% of pitches with a runner or runners on. Now, in that span, 130 different players have faced at least 10,000 pitches. Out of all of them, Cabrera has the biggest positive difference in swing rate. As it happens, Derek Jeter has the biggest negative difference in swing rate, but maybe that’s a different article. Cabrera has swung more with men on; and at bats with men on are more important at bats.

I wanted to dig deeper.

The next step was to break things down by year, to see if there might be a developing trend or a steady pattern. As is often the case, I have to express my gratitude for the existence of Baseball Savant. This table suggests one thing:

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I’m probably not being biased at all when I say we offer a lot of different great features here at FanGraphs, but I’m personally a huge huge fan of our projected standings and playoff-odds pages. Now that we have ZiPS folded into the mix, things are pretty complete, and it’s exciting to be able to see the numbers whenever one wants to. The numbers are based on depth charts maintained by some of our own authors, and they’re living and breathing, so you can see the direct impact of, say, the Phillies signing A.J. Burnett. (It lifted their playoff odds about four percentage points.) FanGraphs is always improving, and these additions have been a big recent improvement.

Now, as discussed briefly yesterday, we never want the projections to be actually perfect. Thankfully, that’s never going to be a problem, on account of the damned human element. But we do want the projections to be meaningful, because otherwise, what’s the point? We want the data to be smart and more right than wrong. So that brings to mind the question: how did things go last year, in our first thorough experiment with depth charts and team projections?

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With the recent addition of the ZiPS projections to the present site, the playoff odds that are also available within these electronic pages now feature both the ZiPS and Steamer figures as the raw material for those calculations.

“How do those playoff odds compare, for example, to what might be offered by a notable sportsbook?” a weblogger with an interest in producing content might wonder. “Yes, how do those FanGraphs playoff odds compare to what’s offered by a sportsbook?” a reader might wonder, as well, as part of his or her attempt to locate a distraction from whatever work ought to be done right now.

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I’ve been thinking about Chad Qualls this week, because… well, because no one can stand thinking about Ervin Santana any longer, probably, but also because when I wrote about the Astros a few days ago, I linked to an Eno Sarris post over at RotoGraphs about the Astros bullpen. Eno briefly touched on how Qualls, who had been reliably the butt of jokes for several years, had returned to his old mechanics, helping him have something of a quietly successful 2013.

Since Qualls signed with Houston during that insane first week of December when there were approximately 40 new signings each day, we never really wrote him up, instead touching on him here and there. While that’s partially because middling relievers don’t always deserve their own posts, mechanical changes are always a bit fascinating when it comes to changes in performance — just check out the difference in where Jayson Werth held his hands during his monster second half last year. That being the case, I thought it’d be interesting to track down the source there and check this out.

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Here’s the thing about projections: we always want them to get better, but we never want them to be perfect. Not that perfect is anywhere within our grasp, but in the hypothetical reality where we knew for sure what was going to happen, sports would be ruined. We don’t want to know the future — we just want to think we do, so we can talk about and analyze things that haven’t fully played out. With that in mind, hey look, we have complete combined 2014 data for Steamer and ZiPS!

We have combined 2014 season projections, and we have author-generated team-by-team depth charts. So what we have is an idea of the projected upcoming standings, an intelligent declaration of how things will go that we know will look kind of silly in six months. Reality always deviates from the projections, but that doesn’t mean the projections are valueless, and I thought it could be worth looking at which teams appear the most and least improved from last season.

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The non-appendix portion of The Book is 367 pages long. Chapter 9, “To Sacrifice or Not” is 50 pages long and represents nearly 14% of the entire book.  The math within may not be for everyone to read, but the information is simplified with the addition of several “The Book Says” callouts that would be easy for any reader, say a manager, to find.

Ron Washington tells us to take those “analytics on that and shove it up our [bleep][bleep]”

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Nelson Cruz got tired of unemployment and signed a one year, $8 million deal over the weekend, taking nearly half of the salary he turned down when the Rangers made him a qualifying offer back in November. However, according to agents Scott Boras and Bean Stringfellow, fellow remaining free agents Ervin Santana, Kendrys Morales, and Stephen Drew aren’t particularly interested in following in Cruz’s footsteps, and are even openly talking about waiting until after the June draft — when they will no longer have compensation picks attached — before signing a new contract. The theory is that, without the encumbrance of draft pick tax, teams would be lining up to sign these players.

There’s a problem with this theory, however; the math simply doesn’t work. Over at MLBTradeRumors, Tim Dierkes did a great job laying out the picks that each team would have to surrender if they signed any of the remaining qualified free agents. He also helpfully included the pool amount allocated to each pick, so we can see that the exposed draft pick “values” range from $2.8M down to $600K.

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It was back during the winter meetings when major league baseball made headlines by announcing their intention to eliminate home plate collisions. On Monday, MLB and the players’ association announced that a new rule will take effect in time for the 2014 season. The rule will be reviewed and possibly tweaked prior to the 2015 season.

The impetus to make a change is obvious, many teams count their catcher as one of their best players. In an otherwise non-contact sport, catchers get knocked off the field all too often. Baseball is a bit behind the curve. Other sports have been protecting exposed players for over a decade, like quarterbacks and kickers in football or goaltenders in hockey. Players like Buster Posey and Yadier Molina have been injured in recent seasons, as these two videos show (video one, video two).

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In Part One of this series, published yesterday, I ranked the projected 2014 Opening Day payrolls, estimated the number of pre-arbitration players on each Opening Day roster, and calculated the percentage of each team’s payroll attributed to the highest paid player.

Today, in Part Two, I break down the payrolls even further, into four component parts: the starting rotation, the starting lineup, the bullpen and the bench. In so doing, I made a judgment on who was likely to slot into these roles to start the season. FanGraphs’ Depth Charts and MLB Depth Charts were my go-to sources, but I made a deliberate decision to exclude all non-roster invitees from Opening Day rosters, as those players’ salaries aren’t included on Cot’s Contracts. Invariably, some of my judgment calls will be wrong. Feel free to note those in the comments, as many did yesterday in Part One.

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So we’re agreed: it’s interesting to see where different projection systems disagree. After all, the projections are based on the same information, for the most part, so it could be telling where there might be significantly different interpretations. Monday, I compared and contrasted 2014 Steamer and 2014 ZiPS for position players. This is the natural and obvious follow-up, for pitchers.

You’d think this would be just as simple as the position-player version. In truth, it’s more complicated, and analysis required a few more judgment calls. I’m okay with them — I’m the one who made them — but if you’re dissatisfied, you’re free to go into the spreadsheets and run the numbers the way you’d prefer to. One of the neat things about our hosting the projection data is that it’s all so easily exportable. Now then, let’s walk through the process so we can get to the end of walking through the process.

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