Now that we’ve completed our journey through the positional power rankings for the upcoming season, I wanted to give an overview of each team’s forecasts for each spot, and then their overall forecast. Keep in mind that simply summing the linear weights contribution of each individual player is a very crude way to project a team’s performance, since it leaves out things that a good projection system should forecast, such as strength of schedule and the non-linear interactions that effect run scoring. However, for being a crude back-of-the-envelope calculation, it also works pretty well, so as long as you take these in the spirit they’re intended and not as the gospel truth, this kind of exercise can give you a lot of information about where teams stand heading into the coming season.

So, here’s the total results for each team’s forecast WAR from the Positional Power Rankings, and the conversion from that into projected wins.

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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.

Over the last couple of weeks, we’ve been going position by position around the sport. We finish up the series with bullpens today, but it’s worth noting that these projections follow a slightly different structure than the rest.

For one, projecting specific innings totals for relievers is a taller task than projecting playing time for position players or even innings totals for starters. There are numerous outside factors impacting bullpen usage, including things that we can’t really predict like the distribution of runs scored and allowed by each team. One team might play in a bunch of blowouts and rarely need their closer, while another could end up in a continuous stream of one run games and ask their best few arms to carry a lion’s share of the workload. Beyond that, the health of a team’s rotation is going to be a factor, as some relievers are also reserve starters who might be pressed into duty mid-season. And the depth charts are continually evolving, as injuries and acquisitions move guys into differing roles that come with different usage patterns.

So, for the relievers, we’ve simply assigned IP totals to each slot on a depth chart. Closers and primary setup men get 65 innings each, with the 3rd/4th relievers getting 55 innings each, and then the rest have their innings allocated in descending order according to their placement on the depth chart. And, in order to make each team’s total number of innings pitched (both starters and relievers) equal out to 1,458, we’ve added in a set for each team that makes up the missing innings in the projections. The performance projection is the same for each team, and is set to be around -0.1 WAR per 100 innings, on the assumption that the 10th or 11th reliever a team uses throughout the season is probably a little bit below replacement level. The statline in the table is just there as a placeholder – those numbers aren’t actually affecting the calculation beyond just setting innings equal and being included in the WAR sum.

Also, since we don’t have separate ZIPS/Steamer projections for guys as starters and relievers, guys who were projected as starters but are going to pitch in relief will likely be under-forecast. Aroldis Chapman, for instance, is getting his starter projections prorated to reliever innings totals, and he’ll almost certainly pitch better in relief than he was projected to do as a starter. There aren’t a lot of those types, but for guys like that, adjust their numbers upwards accordingly.

One final note: we’ve mentioned this on the other lists, but it is worth emphasizing here – the gap between many teams is so slim that you shouldn’t read too much into a team’s placement in the ordinal rank. The gap between #12 and #22 is +0.7 WAR. That’s no difference at all, really. There are good bullpens, okay bullpens, and a couple of bad bullpens, but don’t get too caught up in whether one team is a few spots ahead of another team. With margins this small, the specific placement on the list is mostly irrelevant.

On to the list.

Read the rest of this entry »

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.

Over the last couple of weeks, we’ve been going position by position around the sport. We finish up the series with bullpens today, but it’s worth noting that these projections follow a slightly different structure than the rest.

For one, projecting specific innings totals for relievers is a taller task than projecting playing time for position players or even innings totals for starters. There are numerous outside factors impacting bullpen usage, including things that we can’t really predict like the distribution of runs scored and allowed by each team. One team might play in a bunch of blowouts and rarely need their closer, while another could end up in a continuous stream of one run games and ask their best few arms to carry a lion’s share of the workload. Beyond that, the health of a team’s rotation is going to be a factor, as some relievers are also reserve starters who might be pressed into duty mid-season. And the depth charts are continually evolving, as injuries and acquisitions move guys into differing roles that come with different usage patterns.

So, for the relievers, we’ve simply assigned IP totals to each slot on a depth chart. Closers and primary setup men get 65 innings each, with the 3rd/4th relievers getting 55 innings each, and then the rest have their innings allocated in descending order according to their placement on the depth chart. And, in order to make each team’s total number of innings pitched (both starters and relievers) equal out to 1,458, we’ve added in a set for each team that makes up the missing innings in the projections. The performance projection is the same for each team, and is set to be around -0.1 WAR per 100 innings, on the assumption that the 10th or 11th reliever a team uses throughout the season is probably a little bit below replacement level. The statline in the table is just there as a placeholder – those numbers aren’t actually affecting the calculation beyond just setting innings equal and being included in the WAR sum.

Also, since we don’t have separate ZIPS/Steamer projections for guys as starters and relievers, guys who were projected as starters but are going to pitch in relief will likely be under-forecast. Aroldis Chapman, for instance, is getting his starter projections prorated to reliever innings totals, and he’ll almost certainly pitch better in relief than he was projected to do as a starter. There aren’t a lot of those types, but for guys like that, adjust their numbers upwards accordingly.

Oh, and we’ve mentioned this on the other lists, but it is worth emphasizing here – the gap between many teams is so slim that you shouldn’t read too much into a team’s placement in the ordinal rank. The gap between #12 and #22 is +0.7 WAR. That’s no difference at all, really. There are good bullpens, okay bullpens, and a couple of bad bullpens, but don’t get too caught up in whether one team is a few spots ahead of another team. With margins this small, the specific placement on the list is mostly irrelevant.

On to the list.

Read the rest of this entry »

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.

Read the rest of this entry »

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 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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Note: 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.

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.

Update: Boy! What a difference park factors make! In the original iteration of this article — the one where we thought the park factors were park factoring, but they weren’t — the distribution of DH talent appeared skewed left. Now, not only have the teams shifted closer together, but teams from hitter-friendly parks — such as the Yankees and White Sox — have slunk to the rear while those in pitcher havens — the Mariners and Rays — have edged to more prominent slots.

Because I attempted to weave together these rankings into a grander sort of narrative, much of my original text requires revision. I am happy to report, however, the majority of my in-post complaining about the rankings became validated by the fixed park factors. However, in lieu of covering this article with strike-throughs, I am going to just update the test (as minimally as possible) to reflect the updated rankings.

Originalish post: These rankings are fun. They do not affect the results on the field or the players ranked in them or the GMs glowering over the players. But we are inexorably drawn to these sorts of rankings. With egos invested into our teams, rankings give us pre-season bragging rights or grinding axes.

In all this fun, however, it is important to remember the function of our list. As we are wont to do at FanGraphs, we have attempted to make our lists in the most clinical, mathematical and unbiased ways as possible. Whereas many MLB power rankings are based on gut judgements or broad, basic analyses, we have computed a scientific power ranking system that requires human input only when it is an improvement over an algorithm.

This means, however, the space between each team is discrete. The distance between No. 1 and No. 2 is much greater than, as you will see, between No. 13 and No. 14:

Two are clustered near the top, others are rounding errors apart, and two teams appear clustered near the bottom. But an ordinal ranking does not represent that accurately.

And even despite our best utilization of projection systems and playing time predictions, the season is unpredictable. Not just hard to predict, but unpredictable. If it weren’t, who would watch it? But as of now, as of our best playing time estimations, as of the best projection systems, this is how the DH world settles. This is how the big and sluggerish stand.

Without further ado, I present the Slow and Sluggering Show:
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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.

If for some reason you have been under a rock for the past week or perhaps you’ve been closing your eyes, plugging your ears, and hollering “I can’t hear you” until the Left Field Positional Power Rankings were unveiled, be sure to acquaint yourself with the methodology of the following. The quick and dirty is that the projections are a hybrid of Steamer and ZiPS, it takes into account expected playing time and players at multiple positions.

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If for some reason you have been under a rock for the past week or perhaps you’ve been cranking up the INXS, putting on your mother’s coke bottle eye glasses, and hollering “Where’s the cat at” until the Right Field Positional Power Rankings were unveiled, be sure to acquaint yourself with the methodology of the following. The quick and dirty is that the projections are a hybrid of Steamer and ZiPS, it takes into account expected playing time and players at multiple positions.

Right field seems like a place you put slow-footed sluggers that can murder the ball at the plate, and yet I remember playing a lot of right field because it was thought that I would do the least amount of defensive damage at the position. The combination of big offense and bad defense at the position might be changing — look closely at this year’s crop and you could be underwhelmed by the bats, and you’ll also see some players that produce despite low-powered plate production. And yet, one of the most exciting young (and, yes, powerful) players in baseball is atop the chart at the position — at least the Marlins have one thing going for them.

Right field — maybe not as sexy as your father’s right field, but still fairly attractive.

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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.

* * *

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.

Center field is one of the most star-laden positions in baseball at the moment, but a whole lot of those stars are dealing with injuries or coming off down years or trying to change positions. It also hurts that arguably the best player in the game figures to spend most of his time in left field this summer, but so be it. There is still plenty of center field talent — third base was the only position with more 5+ WAR players in 2012 — with a few interesting youngsters due to get regular playing time this year.

The league average center fielder hit .264/.328/.414 (101 wRC+) last summer, so the offensive bar is low compared to the corner spots. Defense is a big separator between the good and great players, though I feel like no position is more prone to the surprise 4+ WAR season. We’ve seen quite a few players pop-up out of nowhere to post star-caliber seasons driven largely by their center field defensive ratings, which can be a sketchy proposition. The established center field stars are among the best players in the world and perennial MVP candidates, so it’s no surprise teams with those players dominate the top of our rankings.

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