For an explanation of this series, please read the introductory post. 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.

Last week, we tackled the positional players, grading out each team’s options at each spot that is occupied by a fielder. You can see all those posts here, and yes, they’ve now been updated to reflect the correct park adjusted numbers. So, today, we move on to the pitching side of things. Because we’re dealing with 7-10 starters and an equal number of relievers for each club, we’re breaking these posts into two parts, less they become our own version of War and Peace.

After doing the bottom tier this morning — while noting again that the dividing line is essentially a false one, since there’s basically no separation between teams from #13 to #17 — we’re on to the strong pitching staffs, including a couple at the top that are exceptionally strong. There are also a few surprises in the top half, but overall, I think the projections look pretty good. There are inevitably going to be innings allocations or performance forecasts than one can quibble with, but overall, I think this system has done a pretty good job.

On to the list.

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For an explanation of this series, please read the introductory post. 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.

Last week, we tackled the positional players, grading out each team’s options at each spot that is occupied by a fielder. You can see all those posts here, and yes, they’ve now been updated to reflect the correct park adjusted numbers. So, today, we move on to the pitching side of things. Because we’re dealing with 7-10 starters and an equal number of relievers for each club, we’re breaking these posts into two parts, less they become our own version of War and Peace.

We’ll start off with the starting staffs that occupy the 16th-30th spots on the list, but also keep in mind that the ordinal rank is often not that important, as there’s no real difference between the #13 and #17 teams in terms of projected outcome. The actual performance is the interesting thing here. And, since we’re starting in the lower half of the list, there are some pretty ugly projections to follow.

Also, note that the innings projections are not equal for every team. Due to durability and bullpen deployment, not every team gets the same amount of innings from their starters over the course of the season. We have equalized the innings at the team level, so teams that are projected for fewer innings from their starters will get a larger number from their relievers, but the IP totals for each team’s rotation and bullpen won’t match up like the PA totals did for each hitter. We’ve made sure they fall within a reasonable range, however, and think the overall distribution of playing time makes sense for each club.

All that said, on to the write-ups.

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As we rolled out the Positional Power Rankings over the last week, several people noted that there were some pretty large variances in the WAR totals between positions. Indeed, if you look at the totals by position, you see two distinct groups:

Catcher: +107 WAR
Center Field: +104 WAR
Third Base: +101 WAR
Left Field: +81 WAR
Shortstop: +80 WAR
Second Base: +80 WAR
First Base: +78 WAR
Right Field: +74 WAR

Side note: The projected WAR totals here are higher than observed WAR totals because projections essentially put a lower bound on bad performances, with very few players projected for negative WAR. Because of injuries and the non-normal distribution of variance around a player’s true mean, it’s easier to underperform than overperform projections, and inevitably, there will be below replacement level performances in MLB next year. Because of this lower bound, the replacement level in this system is more like 39 wins, so adjust accordingly. Now, back to the subject at hand.

C/CF/3B are all expected to perform at a similar level, and that production is quite a bit higher than the LF/SS/2B/RF/1B group. Instinctually, this feels incorrect, but I think it’s probably worth talking about the reasons for why positions might not be projected to provide the same value.

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As you’ve no doubt noticed, we’ve been rolling out posts that work through the expected production for each team at each position on the diamond, and with the DH post going up this morning, we’ve now done a post for each of the nine spots occupied by position players. If you missed them, I’ll put the links below.

Introduction
Catcher
First Base
Second Base
Shortstop
Third Base
Left Field
Center Field
Right Field
Designated Hitter

We’ll tackle pitchers at the beginning of next week, but with hitters behind us, I thought it’d be interesting to take a little bit of time to look at some of the data to come out of the project so far. There are several things to note, and I’ll be writing about several of those things over the next few days. For now, let’s start with the main thing we noticed as we’ve gone along.

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Last year, we decided to do season previews a little bit differently, and instead of running down each individual team, we previewed the league by position. We liked the format, so we’re doing it again this year. For those who didn’t see the series the first time around, let me borrow from last year’s introduction:

This is only looking at the upcoming season and doesn’t account for potential long term value – we’re just concerned with what each team may get from a given spot on the field this year…

The fun part of comparing teams at a given position is that we’re not limited to just looking at one player, but can compare the expected production of an everyday guy against a left/right platoon, or we can note what a team should expect from giving a stop-gap two months of playing time before they call up their top prospect in the early summer. Few teams get an entire season’s worth of playing time at a position from one guy, so by using depth charts to create an expected playing time matrix, we can give a more thorough evaluation of what kind of strength an organization has at a given spot.

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At the MIT Sloan Analytics Conference, Dan Rosenheck offered a presentation on the effects of infield fly rate and in-zone contact rate on predicting BABIP. He has generously let us republish his talk here. The full text of his presentation is published below. You can find more of Dan’s work at The Economist and The New York Times, or check out his previous project on Wins Above Replacement.

Long before baseball’s statistical revolution entered the mainstream, Hollywood writers showed an impressive grasp of one of the game’s most confounding nuances. In the 1988 movie “Bull Durham,” Kevin Costner plays Crash Davis, a career minor league catcher tasked with tutoring the pitching prospect Nuke LaLoosh. Explaining the cruel randomness that determines the fates of so many aspiring major leaguers, Crash gives Nuke a brief math lesson at a bar.

Crash’s speech actually contradicted much of conventional wisdom about baseball at the time. Ever since the Hall of Famer Wee Willie Keeler explained the secret to his success as “hit ‘em where they ain’t,” fans and sportswriters have generally given hitters credit for expertly placing their seeing-eye grounders and Texas Leaguers into the gaps between opposing fielders. Similarly, they have praised pitchers who seem to induce opposing hitters into serving up a steady stream of routine plays for the defense. At one point, even Crash himself preaches the value of pitching to contact. “Don’t try to strike everybody out,” he advises Nuke. “Strikeouts are boring. Besides that, they’re fascist. Throw some ground balls. It’s more democratic.”

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