Joe Morgan’s Socialist Baseball Regime

A popular theme this pre season was parity.  Truth be told, it’s been quite popular since the 2014 preseason projections forecasted the smallest disparity between the best and worst teams going back to at least 2005, but the term was so worn-out leading up to this season that BuzzFeed included it on their end of the year list of “words that need to be stricken from the Sabre community” (source needed).

While the AL was the main driver of parity related conversation, it might be worth mentioning that the results show that the AL was more lopsided than in 2015 while the NL’s gap was more compressed compared to the previous season.  It’s not that that’s incredible, projection systems are conservative and variables such as sequencing and luck are still unpredictable.  Reflections of these points can be seen in Texas’ record in 1 run games, or the Phillies and Braves performing better than they expected, or the Twins performing more like the Phillies and Braves were expected to.

It’s probably reasonable to expect that, as front offices skew more towards advanced analytics, the trend of increased parity will continue.  Of course that’s too simple of a statement as revenue sharing and luxury tax measures have played their part in balancing out the competitive environment as well.  But as the front offices progresses it’s more likely that the true talent level at the major league level spans a smaller range; less and less at bats go to poor players while the top players are more evenly distributed throughout the league, speaking in terms of true talent.

This article, however, is not really about anything based in analytics or reality and I don’t know how to segue from my intro into delivering to you what I set out to do any better than asking you to assume some ridiculous prerequisites:

  1. MLB and the owners of all the teams only care about the viewer’s experience and have agreed to form a socialist baseball regime
  2. Unpredictable variables are now pretty predictable. This includes some luck, breakouts, injuries, rapid declines.  This does not mean, however, that Runs and RBIs are predictable, it just works out perfectly by WAR
  3. The public is unaware of the predictability of baseball and there is an illuminati type presence in baseball headed by a board of trustees that includes, ironically, but obviously, Joe Morgan
  4. Payrolls are dictated by the outcomes that MLB knows will happen and are strictly performance based – by Fangraphs WAR
  5. Rosters are reconstructed every single year
  6. Reconstructing rosters has no effect on luck or sequencing or ball park effects (maybe all ball parks have the same dimensions
  7. The DH is in both leagues but is only reserved for a portion of games throughout the year; teams are required to allocate at least 140 PA to pitchers
  8. Dave Stewart somehow managed to mess up his last season as the Diamondbacks GM (They just happened to be the last team I constructed and there wasn’t enough WAR left to make them as good as the other teams)

What I did was export all the data I felt was relevant from the Leaderboards and built 30 rosters based on the average number of Plate Appearances, Games Started, Innings Pitched, and WAR.  The numbers for the league break down like this:

Offense

PA (Non Pitchers): 179,218 (5,974.93/team)

WAR (Non Pitchers): 572 (19.07/team)

PA (Pitchers): 5,366 (178.87/team)

WAR (Pitchers): -2.6 (-0.09/team)

Pitching

GS: 4856 (161.87/team)

IP: 43306.3 (1443.54/team)

WAR: 429.5 (14.32/team)

The only other things I wanted to be consistent with reality were the distribution of Plate Appearances by position and accounting for the IP by position players.  The first caveat doesn’t work out perfectly, but you’re not going to find a team that received 1,500 PA from their catchers and only 900 from all three outfield positions combined.  The second one, however, I believe I perfected.

After I had built the 30 rosters I realized they were only distinguished by a roster number, so in order to assign each roster a team, I simply took an alphabetical list of the team names and went down one by one with a random number generator and matched that team and random number to the roster with the corresponding number.

When you look at these rosters ask yourself a few things: Who was on your favorite team?  Considering the public doesn’t know about the basically flawless projection systems, how did your team do compared to how you thought they would do? How much would this effect the way you watch the game?  How much would this affect your team loyalty?  Would you enjoy this?  Is this the dumbest exercise you’ve ever seen?

 

 

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Paul Goldschmidt Has A Pop Up Problem

Growing up my dad would sometimes refer to my sister and me as ingrates. I always had a sneaking suspicion that statement was ruthless. I was young and under the assumption that he provided us everything we needed and wanted because that was what he was designed to do. In a sense, that perception of him probably does reflect the ungratefulness that children posses, innocent as it may be. I am now a parent of a two year old boy and just the other night he saw a commercial for a Power Wheels Jeep Wrangler that elicited the following outburst:

“I want to go in there!”

“I want one!”

He then proceeded to turn, peer into my eyes, and, in order to accentuate the severity of his next mandate, raised his index finger and spoke;

“Daddy, better buy me one.”

His tone was more subdued than the first two exclamations, and it made me laugh the hardest. I am certain I was the narrator of many statements similar to this as a kid, but the reality is, when kids are given everything they want, it’s up to the parent to understand that the “ungratefulness” is a direct byproduct of trying to make them happy or keep them alive. If it feels like there’s an undercurrent of unresolved issues from my childhood, don’t worry, I do plenty of cognitive behavioral therapy.

Lately I’ve been thinking of how I can be really ungrateful for even truly fine baseball seasons. Even some all-star seasons disappoint me, and I know I’m not alone. If Mike Trout was in the middle of putting up a 5 win season, we’d all be talking about what could be wrong with Mike Trout. When players set the bar so ridiculously high we tend to hold them to that standard. As an actual example, it’s completely understandable to be disappointed by Bryce Harper’s season after last year’s masterpiece. The reality is, however that he’s 23 and has currently produced 3.4 WAR. His baserunning and defense have been positives and he’s compiled over 20 home runs and 20 stolen bases while hitting 14 percent better than league average; that’s damn fine and yet it’s still a damn shame.

But Paul Goldschmidt is hitting .301/.414/.494 and has accrued 4.7 WAR and might surpass 30 SB this year. His 136 wRC+ is still great even if it’s not quite the 158 he’s put up over the last three seasons. So why am I disappointed? Firstly, and admittedly shallow of me, I like my Goldschmidt with more extra base hits. For the first time in his professional career, at any level, Goldschmidt’s ISO starts with a number under 2. It’s possible he has a nice final week and brings that number up into the .200 range, but there are still some potentially concerning blips in his batted ball profile that could portend of suppressed production. What I’m referring to most specifically, as the title suggests, is that Paul Goldschmidt has developed a pop up problem.

From 2011 through 2015, Goldschmidt’s cumulative IFFB% was 4.8%. This year it sits at 14%. He has 17 IFFB this year, which is the same amount he had in the three previous seasons combined. Pop ups aren’t good. Here are the 10 players with the biggest increases in IFFB% in 2016 compared to 2015 among qualified hitters in both years.

top-10-chart

I’m not suggesting there’s a positive correlation between popping up and performance, but it’s easy to make sense of some of the names that appear on this list. If you watched Josh Donaldson breakdown his swing on the MLB network, you know that a lot of players are thinking about not hitting the ball on the ground because damage is done in the air. Did you know that DJ LeMahieu, at the time of this writing has a higher slugging percentage than Goldschmidt? That’s bonkers. The league’s slugging percentage last year was .405, this year it’s .418, but this group of players, minus Goldschmidt, have added, on average, 21 points to their slugging percentage and part of that, for this group, has to be contributed to putting more balls in the air.

popupsimprove

What I’m hoping to highlight is that what is even more troublesome for Goldschmidt is that he is the only player in this top 10 who had an increase in their IFFB% while also seeing his fly ball rate and hard hit rate drop.

goldschmidtpopsdown

So I have what could be an insultingly obvious hypothesis, but since Goldschmidt has long been a quality opposite field hitter, I am theorizing that pitchers are exploiting him with more fastballs up and in where he can’t quite get his hands extended. A cursory glance at his heat map vs fastballs in 2015 and 2016 reveals a minor shift in approach by the league.

Besides the obvious, which is that pitchers are avoiding the zone even more than they had before, we can see just a bit more red in the specific zone I was referring to. It’s not completely obvious, so let’s see if that’s where pitchers are getting Goldy to pop up. On the year, per Brooks Baseball, he has 22 pop ups, 19 from fastballs and 3 from offspeed pitches. The 17 that are classified as IFFB by fangraphs are plotted in the graph below.homemade-heatmap

But it’s not as if pitchers have previously avoided throwing Goldschmidt up and in, it just appears, despite his overall swing rate being at a career low 39%, he’s upped his swing rate against fastballs by over 5 percentage points in that specific area just above 3.5 ft that has led to the majority of his pop ups.  Looking at the entire area middle/up/and in to Goldschmidt, he has increased his swing rate from 57.2% in 2015 to 60.7% in 2016 while staying away from lower pitches in general.  It’s a philosophy that is being echoed throughout baseball right now, and it is not at all a bad plan, but it has caused him, either deliberately or by the effect of swinging at these pitches more often, to go to the opposite field this season less than he ever has. This also is not necessarily a negative shift to a batted ball profile, but from 2013 – 2015 he was the 5th most productive hitter in baseball going the other way, and in 2016 he’s 33rd. That represents a drop in wRC+ from 204 to 158, and from a .729 SLG (.329 ISO) to a .647 SLG (.255 ISO).

At the end of this season I don’t think I’ll actually be all that worried about Goldschmidt; I can reconcile a 136 wRC+, even if it would feel a little disappointing.  So I think if I’m going to take a 136 wRC+ for granted I should place that appreciation towards the catalyst for this change in Goldschmidt’s performance, and a lot of that credit has to go to the pitchers who have induced 17 IFFB from a player who only averaged 5.7 over the last 3 seasons.

Now I know that setting up a pitch has so much more to do with the pitch that was thrown immediately before it, but for this exercise I want to look at the pitch that caused Goldschmidt to pop up and how it relates to the pitch thrown immediately before it. It’s crude and does not tell the whole story, but it still shows a definite approach – and, for all intents and purposes, it’s probably a decent representation of the general tactic used across the league for inducing pop ups.  I found all the data I needed using Pitch fx at Brooks Baseball.  I recorded the Velocity, horizontal movement, vertical movement, horizontal location, and vertical location of each pitch Goldschmidt popped out on as well as the same data set for each set up pitch if there was one (which would be in any situation where Goldschmidt did not pop up on the first pitch of an bat). Below you’ll find a plot that shows the average location and characteristics of each pitch.

poppy-uppies

And here is that data in a table represented as the average difference between the two plot points.

pitchdiff

Doesn’t it make you feel warm when something fits into the shape you had pegged it to be.  That’s just really simple and makes a hell of a lot of sense.  I really appreciate that.  Now if you’ll excuse me, I have a Power Wheels Jeep Wrangler to buy.

 

 

2016 Composite Projections

2016 Composite Projections: Where Convenience is Job #1

https://docs.google.com/spreadsheets/d/1zmGUjhS_mcfrnkPKb5oQgckuy2S1uQlhKEGzzRB1IVA/pubhtml

 

In order to circumvent my rambling and better understand anything in the table you may find mystifying, skip down the page until you see the link for the spreadsheet again…

In Kyle Kinane’s 2010 stand-up set, which is immortalized in listenable format under the title, “Death of the Party”, he delivers his despondent outlook on life like a brilliant, seemingly drunk poet. There is a specific passage in which he speaks to his self-worth as it relates to his time spent as a gourmet cake decorations salesman; he refers to himself as:

“a stripped-bare toothless cog spinning freely and ineffectually in the working machine of society.”

Magnifique! As bleak as that is, I’m sure most of us have felt that way at one point or another – and not just about our jobs. As I grow older, I’ll be 31 this year; I find it harder to separate anything I do from a bigger scale, which of course leads to bouts of nihilism and depression; which leads to imaginary scenarios of myself never allowing my son to believe in Santa Clause – which is just anxiety over the idea of being a bad parent coupled with a dash of hopelessness about my existence. Looking back on my life sometimes yields the same results. I’ve spent an insane amount of my life’s time playing, watching, predicting, thinking about, and listening to baseball. We could say that it’s a strange existence, but then I guess you could say that about anything.

But man! Making this spreadsheet every year (mainly) at work really makes me feel like a ne’er-do-well. I’ve become so hyper focused I tune out co-workers and sometimes I feel like it’s brought on aspergers-like symptoms. For these reasons, this is probably the last time I’ll be doing this, albeit the first time I’m sharing this with other people.

My own projections started out (years ago) incredibly optimistic, as fans’ projections are wont to be and while I’ve refined them, and I really do love to do them. However, I realized that in a world with Steamer, ZIPS, and PECOTA, among others, the best results are yielded from a composite projection system (I’ve won my main league five of the last seven years using this system with no finishes outside the top 3).

The systems used to create this Composite Projections System include:

Steamer, ZIPS, PECOTA, Marcell, Rotochamp, ESPN, my own projections, previous year performance (2015), a 3 year average stat line, and for players with limited or no MLB experience, high level minors numbers are regressed and thrown in as well.

I understand using 2015 and past 3 year average is a little redundant as it’s baked into almost all the projection systems, but I do it because those numbers aren’t regressed in any way.

Let it be noted that a lot of the work on this spreadsheet isn’t mine. It’s an amalgamation of many different sites, authors, and ideas. I’ll try to parse it out for you the best I can and hopefully you can find it usable.

https://docs.google.com/spreadsheets/d/1zmGUjhS_mcfrnkPKb5oQgckuy2S1uQlhKEGzzRB1IVA/pubhtml

The first two rows are the headers – the first row is for hitters and the second row is for pitchers.

Hitters:

For the hitters, you should recognize all the stats until FAVG (Column AC). It’s a crude quotient and it stands for Fantasy Average. It really only provides value over larger sample sizes if it even provides any value at all. I like to use it to compare players that may end up with similar lines at the end of the season (Cespedes, A. Jones, C. Gonzalez) or players who have played in parts of the last couple seasons (Justin Turner).

The equation for hitters is simply:

(Hits + Runs + Home Runs + RBI + Stolen Bases) / At-Bats

Since all offensive stats are weighted equally, there’s a ton wrong with this, but generally speaking their can be a few tiers:

Tier 1: .700+ FAVG: elite offensive production, likely from a number 3 hitter. (Only Trout, Goldschmidt, Stanton, McCutchen, M. Cabrera, Bautista, and Encarnacion occupy this tier as an average score over the last 3 years.)

Tier 2: .650 – .699: Usually players that make up rounds 2 – 3. 20/20 players or players with monster power.

Tier 3: .600 – .649: Players that excel in maybe 2 – 3 categories. It’s likely to be HR/RBI guys that either score a lot of runs or hit for a decent average as well. Less likely to be the super speedy guys, but if they score runs and add somewhere close to 10 – 15 HR, they’ll be here – think Altuve, Cain, Blackmon types.

Tier 4: .550 – .599: Here are the speedy players like Dee Gordon (though he may have moved up to the next tier by now. Solid players inhabit this realm, too.

Tier 5: .500 – .549: Catchers probably. Or single skill players, and bottom of the lineup dudes.

Tier 6: .499 and below: steer clear.

Column AD is titled ZIMM and it’s yanked directly from Jeff Zimmerman’s Draft Prep article from 2015. It’s actually a series of 3 posts and I did not run any positional adjustments for my table. The only other difference is that I used 5.9 as my adjusted slope for SB so that stolen bases aren’t so heavily valued – although that may be a mistake on my part due to the depressed stolen base environment in mlb.

Moving over one column to the right, R.R. stands for Roster Resource, and the numerical value signifies the projected lineup spot for each player. If they are on the DL, I have provided with where I think a player will be slotted once he returns from injury. If they are a back-up or are going to start in the minors it will say BE for Bench, or AAA (despite what level they might start at).

2Pos is just a column to denote second-position eligibility, which is why it is empty for most hitters.

The next 5 columns are lifted directly from Fantasy Pros‘ Average ADP page. This is the recommended way to sort these rankings as the default (column A) is set to my current rankings.

Now we see FAVG and ZIMM again, followed by more stats. These are all representative of a player’s average production over the past three years.

The headers for the colorful sections should be self-explanatory. The cells coated green are skills that are exactly at, or above league average. The more green cells the better, obviously. The reports were exported from fangraphs except for the exit velocity data (columns CD – CJ), which I pulled from Baseball Savant.

Pitchers:

The first thing we’ll run into that looks strange is A. Score. This column rips data from Eno Sarris’ Arsenal Scores series. If a pitcher’s arsenal score was not available in his table for 2015, I went back and took them from the 2014 installment.

FAVG for Pitchers:

(Innings Pitched – Hits – Earned Runs – Walks + Strikeouts + Wins + Saves) / Innings Pitched

As with hitters, this works better with more information. There’s also the caveat that starters and relievers cannot be compared.

Tier Kershaw: Clayton Kershaw – he’s the only starter with an average FAVG of over 1.00 over the last 3 years.

Starter Tiers

Tier 1: .800 – .999 – it’s mainly Scherzer and Sale, although players will jump in and out of this tier (as with others).

Tier 2: .700 – .899 – While the term is vague, these guys are still fantasy aces.

Tier 3: .600 – .699 – fringe ace guys, or perceived aces.

Tier 4: .550 – .599 – pitchers with above average K rates, but not elite numbers.

Tier 5: .300 – .549 – either more contact oriented starters, or good K guys who have a bit of a free-pass issue. It’s a bigger net because wins are so unpredictable. We’re still top 70 type guys though.

Tier 6: There are still a ton of serviceable pitchers here and even below…like I said this is a crude stat.

Relief pitchers are much different and even non-closers tend to post rates above 1 – it’s a poor bell weather for relievers due to the high variance in role.

Moving on – the ZIMM score here directly reflects Jeff Zimmerman’s equation.

The Roster Resource feature shows what rotation spot pitchers will occupy and is pretty meaningless.

Off RS/G stands for Offense Runs Scored per game and I took these values from the projected standings page at fangraphs. Wins are still, for the most part, unpredictable, but a good supporting offense definitely doesn’t hurt.

The next thing included that could be ambiguous is at the tail end of the 3 year average section (Columns AZ – BB). These indicate quality starts, quality start percentage, and game scores. Game scores aren’t really thought about too much, but if you sort the spreadsheet for pitchers by AVG game score, it’s a pretty good indicator of where they should be drafted.

Then of course it’s the comparison against league average section – again, the more green cells the better.

I really hope you find something helpful in this sheet. I know it’s pretty packed, but if you take a couple minutes to figure it out, you’ll find that almost everything you need is in there (no auction calculator or dollar values), so it’s pretty convenient.

Plus if you find value in it, maybe I’ll let my son believe in Santa Clause.

Creating the Best Teams For Each Organization Over the Past Decade

I was perusing the hallowed pages of the internet recently and stumbled upon a Reddit Post that presented an interesting challenge – If you don’t want to click the hyperlink, here’s the gist: construct the best team you can for an organization by using one player from that team from each year of the last decade (going back to 2006).

The caveats instated to make it a decent challenge included the following: you couldn’t use the same player twice; you couldn’t use 2 players from the same year; and a player had to qualify for the position you’re using them in (except for DH), meaning you can’t just throw a CF in LF just because it’s assumed that they could play that position.  I also added the caveat that a player must be on the team at the start of the season – no players acquired by trade can be featured on a team.  The roster had to be constructed like this:

C

1B

2B

3B

SS

3B

SS

LF

CF

RF

DH

SP

It’s like this person (hopesfail) traveled deep into the pleasure center of my mind, conducted a research experiment to determine what type of activity would give me the most satisfaction, and then posted it on reddit.  Below are my findings for each team – I have included the DH for NL teams because it’s just tidier that way (that rule will be instated in the next 5 years or so anyway).

 

AL WAR PA H AB R HR RBI SB AVG OBP SLG OPS ISO BB% K% wOBA DRS SIERA K-BB%
Angels 50.1 5667 1478 5032 775 168 708 165 .294 .372 .464 .836 .170 9.1 16.3 .358 58 3.13 19.8
Astros 42.4 5189 1298 4569 672 164 589 158 .284 .365 .463 .835 .179 10.3 15.8 .358 92 2.84 18.1
A’s 38.4 5458 1263 4739 698 198 694 84 .267 .355 .455 .810 .188 11.2 18.6 .353 55 2.98 18.1
Blue Jays 47.0 5439 1398 4809 786 267 798 84 .291 .368 .521 .889 .231 9.8 16.2 .380 -7 3.12 16.9
Indians 48.7 5524 1372 4764 746 215 810 134 .288 .385 .493 .878 .205 11.5 16.6 .373 1 2.98 22.6
Mariners 42.6 5605 1454 4999 694 174 688 96 .291 .359 .455 .814 .164 8.9 17.0 .352 29 3.18 16.2
Orioles 48.2 5952 1533 5300 852 269 820 93 .289 .360 .510 .570 .220 9.4 17.8 .375 14 2.92 22.4
Rangers 45.8 5171 1329 4539 750 206 698 88 .293 .368 .501 .869 .208 9.6 16.8 .375 78 2.76 23.4
Rays 47.4 4689 1171 4126 642 178 605 115 .284 .362 .492 .854 .208 10.1 18.2 .368 79 3.23 17.5
Red Sox 50.9 5424 1464 4832 763 189 760 98 .303 .368 .493 .861 .190 8.4 14.9 .372 78 3.15 19.1
Royals 43.7 5400 1441 4920 706 139 642 77 .293 .349 .453 .802 .160 7.1 15.5 .349 88 3.04 20.9
Tigers 49.3 5633 1459 4983 827 217 813 69 .293 .368 .497 .866 .205 9.9 18.0 .372 28 3.33 18.7
Twins 39.6 4674 1160 4081 634 162 620 76 .284 .367 .475 .842 .191 10.8 18.0 .366 26 2.55 21.5
W. Sox 35.2 5225 1333 4662 708 207 704 84 .286 .353 .482 .834 .196 8.4 16.7 .362 -18 2.52 27.2
Yankees 52.8 5744 1471 5034 857 211 786 198 .292 .370 .482 .852 .190 10.0 15.1 .371 16 3.13 17.2
NL WAR PA H AB R HR RBI SB AVG OBP SLG OPS ISO BB% K% wOBA DRS SIERA K-BB%
Braves 49.1 5567 1374 4961 779 235 775 58 .277 .356 .487 .843 .210 9.6 17.7 .363 94 2.86 22.2
Brewers 54.3 5723 1471 5040 828 251 835 130 .292 .372 .525 .897 .233 9.7 18.7 .384 60 3.88 13.7
Cards 57.1 5593 1486 4913 731 196 740 69 .302 .384 .501 .885 .199 9.7 14.3 .379 114 3.47 15.1
Cubs 42.8 5444 1337 4804 736 197 664 72 .278 .358 .477 .835 .199 9.5 18.1 .361 67 2.75 21.6
D-Backs 46.6 5891 1496 5210 823 218 767 183 .287 .363 .489 .852 .202 9.7 17.5 .368 37 2.99 13.5
Dodgers 50.0 4952 1279 4339 733 178 716 110 .295 .371 .496 .867 .201 10.4 17.2 .374 51 2.13 29.1
Giants 52.8 5185 1328 4565 702 185 684 90 .291 .382 .493 .875 .202 10.5 16.7 .370 42 2.91 21.3
Marlins 40.7 5178 1341 4527 723 198 662 97 .296 .378 .501 .878 .205 11.0 19.0 .379 -14 2.19 18.9
Mets 44.4 4925 1261 4347 737 180 615 139 .290 .375 .493 .868 .203 10.2 15.8 .372 3 2.63 23.2
Nats 49.2 5451 1342 4775 755 243 731 103 .281 .362 .508 .870 .227 10.6 20.3 .374 35 3.13 15.9
Padres 41.3 4852 1142 4249 619 176 596 93 .269 .359 .459 .818 .190 10.9 19.4 .353 56 2.54 19.2
Phillies 50.9 5388 1393 4794 760 225 732 120 .291 .361 .499 .860 .208 8.6 18.6 .371 24 2.78 19.8
Pirates 37.2 4591 1172 4014 583 156 622 69 .292 .382 .479 .861 .187 10.7 18.3 .370

 

34 3.48 13.9
Reds 41.6 5128 1229 4397 695 196 598 100 .280 .375 .483 .857 .203 11.8 19.5 .374 4 3.84 13.5
Rockies 40.9 5181 1437 4617 751 215 809 80 .311 .375 .533 .908 .222 8.8 17.3 .390 41 3.76 12.4

Here are the individual rosters (presented as lineups).  They are presented by league, alphabetically by mascot.

Los Angeles Angels of Anaheim

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Chone Figgins 3B 2009 6.5 .298 .394 .355 5 42 29
Mike Trout LF 2013 10.5 .323 .557 .423 27 33 0
Vladimir Guerrero DH 2006 3.5 .329 .552 .393 33 15
Albert Pujols 1B 2012 3.6 .285 .516 .364 30 8 8
Kole Calhoun RF 2015 3.8 .256 .422 .321 26 4 6
Mike Napoli C 2008 2.6 .273 .586 .402 20 7 -7
Howie Kendrick 2B 2014 4.7 .293 .397 .326 7 14 7
Orlando Cabrera SS 2007 4.8 .301 .397 .327 8 20 5
Peter Borjous CF 2011 4.2 .271 .438 .335 12 22 12
WAR IP K-BB% ERA WHIP SIERA DRS
Jered Weaver SP 2010 5.9 224.1 19.8 3.01 1.08 3.13 -2

 

Houston Astros

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Michael Bourn CF 2010 4.4 .265 .346 .307 2 52 30
Jose Altuve 2B 2014 4.8 .341 .453 .361 7 56 -7
Lance Berkman 1B 2008 7.7 .312 .567 .418 29 18 15
Carlos Lee LF 2007 2.6 .303 .528 .375 32 10 0
Hunter Pence RF 2009 3.3 .282 .472 .357 25 14 17
Jason Castro C 2013 4.4 .276 .485 .362 18 2 2
Jed Lowrie DH 2012 2.5 .244 .438 .339 16 2
Morgan Ensberg 3B 2006 3.8 .235 .463 .377 23 1 8
Clint Barmes SS 2011 2.8 .244 .386 .309 12 3 14
WAR IP K-BB% ERA WHIP SIERA DRS
Dallas Keuchel SP 2015 6.1 232 18.1 2.48 1.02 2.84 13

 

Oakland Athletics

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Rajai Davis CF 2009 3.4 .305 .423 .345 3 48 6
Jed Lowrie SS 2013 3.5 .290 .446 .345 15 1 -18
Josh Donaldson RF 2014 6.5 .255 .456 .348 29 8 20
Frank Thomas DH 2006 2.4 .270 .545 .399 39 0
Josh Reddick RF 2012 4.5 .242 .463 .330 32 11 22
Jack Cust LF 2008 2.1 .231 .476 .373 33 0 -9
Stephen Vogt C 2015 2.3 .261 .443 .339 18 0 5
Mark Ellis 2B 2007 4.4 .276 .441 .341 19 9 13
Daric Barton 1B 2010 4.9 .275 .408 .360 10 7 18
WAR IP K-BB% ERA WHIP SIERA DRS
Brandon McCarthy SP 2011 4.4 170.2 14.2 3.33 1.13 2.98 -2

 

Toronto Blue Jays

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Vernon Wells CF 2006 5.8 .303 .542 .387 32 17 11
Josh Donaldson 3B 2015 8.7 .297 .568 .402 41 6 11
Jose Bautista RF 2011 8.1 .302 .608 .443 43 9 -7
Edwin Encarnacioin DH 2012 4.3 .280 .557 .400 42 13
Adam Lind 1B 2013 1.6 .288 .497 .369 23 1 -7
Aaron Hill 2B 2009 4.0 .286 .499 .359 36 6 13
Matt Stairs LF 2007 1.4 .289 .549 .392 21 2 -2
John Buck C 2010 2.8 .281 .489 .346 20 0 -11
Jose Reyes SS 2014 3.5 .287 .398 .320 9 30 -16
WAR IP K-BB% ERA WHIP SIERA DRS
Roy Halladay SP 2008 6.8 246 16.9 2.78 1.05 3.12 1

 

Cleveland Indians

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Shin-Soo Choo RF 2010 6.0 .300 .484 .375 22 22 5
Michael Brantley LF 2014 6.1 .327 .506 .384 20 23 0
Grady Sizemore CF 2008 7.4 .268 .502 .368 33 38 -3
Travis Hafner 1B 2006 6.0 .308 .659 .449 42 0 1
Victor Martinez C 2007 5.4 .301 .505 .376 25 0 -2
Jason Kipnis 2B 2013 4.5 .284 .452 .365 17 30 -1
Asdrubal Cabrera SS 2011 3.6 .273 .460 .336 25 17 3
Carlos Santana DH 2012 2.9 .252 .420 .352 18 3
Mark DeRosa 3B 2009 1.3 .270 .457 .349 13 1 -3
WAR IP K-BB% ERA WHIP SIERA DRS
Corey Kluber SP 2015 5.5 222 22.6 3.49 1.05 2.98 1

 

Seattle Mariners

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Ichiro Suzuki DH 2007 6.1 .351 .431 .360 6 37
John Jaso C 2012 2.6 .276 .456 .375 10 5 -1
Nelson Cruz RF 2015 4.8 .302 .566 .396 44 3 -8
Richie Sexson 1B 2006 2.4 .264 .504 .363 34 1 4
Robinson Cano 2B 2014 5.2 .314 .454 .355 14 10 0
Franklin Gutierrez CF 2009 6.0 .283 .425 .335 18 16 32
Raul Ibanez LF 2008 2.8 .293 .479 .362 23 2 -9
Kyle Seager 3B 2013 4.0 .260 .426 .335 22 9 -8
Brendan Ryan SS 2011 2.8 .248 .326 .278 3 13 18
WAR IP K-BB% ERA WHIP SIERA DRS
Felix Hernandez SP 2010 5.9 249.2 16.2 2.28 1.06 3.18 1

 

Baltimore Orioles

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Nick Markakis RF 2008 6.0 .306 .491 .391 20 10 22
Manny Machado 3B 2015 6.8 .286 .502 .374 35 20 14
Chris Davis 1B 2013 7.0 .286 .637 .421 53 4 -7
Nelson Cruz LF 2014 3.7 .271 .525 .367 40 4 1
Adam Jones CF 2012 4.3 .287 .505 .365 32 16 -16
Luke Scott DH 2010 2.7 .284 .535 .387 27 2
Miguel Tejada SS 2006 4.9 .330 .499 .379 24 6 -11
Matt Wieters C 2011 4.4 .262 .450 .340 22 1 17
Brian Roberts 2B 2009 3.4 .283 .451 .352 16 30 -8
WAR IP K-BB% ERA WHIP SIERA DRS
Erik Bedard SP 2007 5.0 182 22.4 3.17 1.09 2.92 2

 

Texas Rangers

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Ian Kinsler 2B 2011 7.2 .255 .477 .364 32 30 18
Shin-Soo Choo RF 2015 3.5 .276 .463 .369 22 4 -11
Josh Hamilton LF 2010 8.4 .359 .633 .445 32 8 7
Adrian Beltre 3B 2012 6.5 .321 .561 .392 36 1 13
Mark Teixeira 1B 2006 3.5 .282 .514 .379 33 2 8
Marlon Byrd CF 2007 2.5 .307 .459 .353 10 5 -2
Robinson Chirinos C 2014 2.3 .239 .415 .308 13 0 4
Milton Bradley DH 2008 4.2 .321 .563 .425 22 5
Elvis Andrus SS 2009 3.2 .267 .373 .313 6 33 15
WAR IP K-BB% ERA WHIP SIERA DRS
Yu Darvish SP 2013 4.5 209.2 23.4 2.84 1.08 2.76 0

 

Tampa Bay Rays/Devil Rays

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Carl Crawford LF 2010 7.7 .307 .495 .368 19 47 8
Ben Zobrist 2B 2009 8.6 .297 .543 .408 27 17 16
Carlos Pena 1B 2007 5.9 .282 .627 .435 46 1 8
Evan Longoria 3B 2013 6.5 .269 .498 .360 32 1 12
Matt Joyce RF 2011 3.6 .277 .478 .353 19 13 -2
Julio Lugo SS 2006 2.4 .308 .498 .382 12 18 -8
Dioner Navarro C 2008 1.9 .295 .408 .334 7 0 -2
Kevin Kiermaier CF 2015 5.5 .263 .420 .313 10 18 42
David DeJesus DH 2014 0.3 .248 .403 .333 6 0
WAR IP K-BB% ERA WHIP SIERA DRS
David Price SP 2012 5.0 211 17.5 2.56 1.10 3.23 5

 

Boston Red Sox

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Dustin Pedroia 2B 2008 6.3 .326 .493 .377 17 20 13
Jacoby Ellsbury CF 2011 9.4 .321 .552 .400 32 39 7
David Ortiz DH 2006 5.3 .287 .636 .436 54 1
Adrian Beltre 3B 2010 6.4 .321 .554 .391 28 2 19
Adrian Gonzalez 1B 2012 2.8 .300 .469 .351 15 0 13
Shane Victorino RF 2013 5.9 .294 .451 .353 15 21 24
Jason Veritek C 2007 2.6 .255 .421 .345 17 1 -1
Daniel Nava LF 2014 2.6 .270 .361 .318 4 4 3
Xander Bogaerts SS 2015 4.3 .320 .421 .342 7 10 0
WAR IP K-BB% ERA WHIP SIERA DRS
Jon Lester SP 2009 5.3 203.1 19.1 3.41 1.23 3.15 0

 

Kansas City Royals

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Alex Gordon LF 2011 6.6 .303 .503 .383 23 17 20
Lorenzo Cain CF 2015 6.6 .307 .477 .364 16 28 18
Billy Butler DH 2010 2.0 .318 .469 .372 15 0
Eric Hosmer 1B 2013 3.2 .302 .448 .350 17 11 3
Emil Brown RF 2006 3.3 .287 .457 .354 15 6 -5
Salvador Perez C 2014 3.1 .260 .403 .302 17 1 8
Mike Moustakas 3B 2012 3.4 .242 .412 .308 20 5 14
Mark Grudzielanek 2B 2007 2.6 .302 .426 .338 6 1 8
Mike Aviles SS 2008 4.3 .325 .480 .360 10 8 14
WAR IP K-BB% ERA WHIP SIERA DRS
Zack Greinke SP 2009 8.6 229.1 20.9 2.16 1.07 3.04 8

 

Detroit Tigers

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Carlos Guillen SS 2006 5.8 .320 .519 .396 19 20 -2
Magglio Ordonez DH 2007 8.0 .363 .595 .440 28 4
Miguel Cabrera 1B 2010 6.0 .328 .622 .431 38 3 -5
J.D. Martinez RF 2015 5.0 .282 .535 .377 38 3 4
Curtis Granderson CF 2008 4.1 .280 .494 .374 22 12 -10
Ian Kinsler 2B 2014 5.2 .275 .420 .317 17 15 20
Alex Avila C 2011 4.6 .295 .507 .384 19 3 -2
Brandon Inge 3B 2009 2.0 .230 .406 .320 27 2 12
Andy Dirks LF 2013 1.8 .256 .363 .306 9 7 6
WAR IP K-BB% ERA WHIP SIERA DRS
Justin Verlander SP 2012 6.8 238.1 18.7 2.65 1.06 3.33 5

 

Minnesota Twins

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Denard Span CF 2008 3.2 .294 .432 .361 6 18 0
Joe Mauer C 2009 7.6 .365 .587 .440 28 4 2
Justin Morneau 1B 2010 4.9 .345 .618 .447 18 0 8
Miguel Sano DH 2015 2.0 .269 .531 .396 18 1
Josh Willingham LF 2012 3.4 .260 .524 .384 35 3 -13
Michael Cuddyer RF 2011 2.4 .284 .459 .351 20 11 -10
Brian Dozier 2B 2013 2.5 .244 .414 .320 18 14 9
Trevor Plouffe 3B 2014 3.6 .258 .423 .329 14 2 6
Jason Bartlett SS 2007 3.3 .265 .361 .312 5 23 15
WAR IP K-BB% ERA WHIP SIERA DRS
Johan Santana SP 2006 6.7 233.2 21.5 2.78 1.00 2.55 9

 

Chicago White Sox

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Tadahito Iguchi 2B 2006 1.8 .281 .422 .344 18 11 -5
Alex Rios RF 2012 3.9 .304 .516 .365 25 23 7
Jim Thome DH 2007 3.4 .275 .562 .415 35 0
Jose Abreu 1B 2014 5.3 .317 .581 .408 36 3 -11
Carlos Quentin LF 2008 4.7 .288 .571 .416 36 7 -1
Alejandro De Aza CF 2013 2.5 .264 .405 .321 17 20 -18
Gordon Beckham 3B 2009 2.3 .270 .460 .354 14 7 -1
A.J. Pierzynski C 2011 1.0 .287 .405 .315 8 0 -9
Alexei Ramirez SS 2010 4.1 .282 .431 .324 18 13 20
WAR IP K-BB% ERA WHIP SIERA DRS
Chris Sale SP 2015 6.2 208.2 27.2 3.41 1.09 2.52 0

 

 

New York Yankees

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Brett Gardner CF 2010 6.1 .277 .380 .344 5 47 9
Derek Jeter SS 2006 6.1 .343 .483 .395 14 34 -16
Alex Rodriguez 3B 2007 9.6 .314 .645 .449 54 24 -1
Mark Teixeira 1B 2009 5.1 .292 .565 .405 39 2 2
Robinson Cano 2B 2012 7.6 .313 .550 .398 33 3 15
Brian McCann C 2015 2.9 .232 .437 .330 26 0 0
Johnny Damon LF 2008 3.7 .303 .461 .369 17 29 5
Ichiro Suzuki RF 2013 1.3 .262 .342 .281 7 20 7
Jacoby Ellsbury CF 2014 4.0 .271 .419 .325 16 39
WAR IP K-BB% ERA WHIP SIERA DRS
C.C. Sabathia SP 2011 6.4 237.1 17.2 3.00 1.23 3.13 -5

 

Atlanta Braves

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Martin Prado 2B 2010 3.9 .307 .459 .352 15 5 2
Jason Heyward RF 2012 6.5 .269 .479 .354 27 21 20
Chipper Jones 3B 2007 6.9 .337 .604 .437 29 5 4
Andruw Jones CF 2006 6.0 .262 .531 .386 41 4 12
Brian McCann C 2008 5.1 .301 .523 .387 23 5 8
Justin Upton LF 2014 4.0 .270 .491 .361 29 8 0
Freddie Freeman 1B 2015 3.4 .276 .471 .368 18 3 3
Dan Uggla DH 2011 2.3 .233 .453 .334 36 1
Andrelton Simmons SS 2013 4.5 .248 .396 .303 17 6 41
WAR IP K-BB% ERA WHIP SIERA DRS
Javier Vasquez SP 2009 6.5 219.1 22.2 2.88 1.03 2.86 4

 

Milwaukee Brewers

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Jonathan Lucroy C 2014 6.1 .301 .465 .366 13 4 11
Carlos Gomez CF 2013 7.4 .284 .506 .363 24 40 38
Ryan Braun LF 2011 7.1 .332 .597 .427 33 33 3
Prince Fielder 1B 2009 5.9 .299 .602 .425 46 2 0
Aramis Ramirez 3B 2012 5.6 .300 .540 .388 27 9 4
Corey Hart RF 2007 4.2 .295 .539 .382 24 23 7
Adam Lind DH 2015 2.2 .277 .460 .354 20 0
Bill Hall SS 2006 5.1 .270 .553 .383 35 8 13
Rickie Weeks 2B 2010 6.0 .269 .464 .367 29 11 -16
WAR IP K-BB% ERA WHIP SIERA DRS
Ben Sheets SP 2008 4.7 198.1 13.7 3.09 1.15 3.88 0

 

St. Louis Cardinals

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Matt Carpenter RF 2013 6.9 .318 .481 .381 11 3 0
Matt Holliday LF 2010 6.2 .312 .532 .397 28 9 7
Albert Pujols 1B 2007 7.7 .327 .568 .421 32 2 31
Ryan Ludwick DH 2008 5.4 .299 .591 .411 37 4
Scott Rolen 3B 2006 5.5 .296 .518 .380 22 7 16
Jason Heyward RF 2015 6.0 .293 .439 .349 13 23 22
Yadier Molina C 2012 6.1 .315 .501 .379 22 12 16
Jhonny Peralta SS 2014 5.3 .263 .443 .341 21 3 17
Jon Jay CF 2011 2.3 .297 .424 .338 10 6 4
WAR IP K-BB% ERA WHIP SIERA DRS
Adam Wainwright SP 2009 5.7 233 15.1 2.63 1.21 3.47 1

 

Chicago Cubs

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Kosuke Fukudome RF 2009 2.4 .259 .421 .353 11 6 5
Alfonso Soriano LF 2007 6.7 .299 .560 .383 33 19 17
Anthony Rizzo 1B 2014 5.7 .284 .523 .391 32 5 6
Aramis Ramirez 3B 2006 3.7 .291 .561 .390 38 2 -7
Carlos Pena DH 2011 2.5 .225 .463 .356 28 2
Marlon Byrd CF 2010 4.0 .293 .429 .342 12 5 4
Starlin Castro SS 2012 3.1 .283 .430 .326 14 25 3
Welington Castillo C 2013 3.2 .274 .397 .331 8 2 19
Mark DeRosa 2B 2008 4.2 .285 .481 .374 21 6 -10
WAR IP K-BB% ERA WHIP SIERA DRS
Jake Arrieta SP 2015 7.3 229 21.6 1.77 0.87 2.75 6

 

Arizona Diamondbacks

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Conor Jackson DH 2008 2.9 .300 .446 .362 12 10
A.J. Pollock CF 2015 6.6 .315 .498 .375 20 39 14
Paul Goldschmidt 1B 2013 6.2 .302 .552 .404 36 15 13
Justin Upton RF 2011 6.3 .289 .529 .386 31 21 8
Aaron Hill 2B 2012 5.3 .302 .522 .379 26 14 -2
Mark Reynolds 3B 2009 3.3 .260 .543 .384 44 24 -5
Stephen Drew SS 2010 4.8 .278 .458 .354 15 10 0
Miguel Montero C 2014 1.1 .243 .370 .306 13 0 -7
Eric Byrnes LF 2007 3.6 .286 .460 .354 21 50 12
WAR IP K-BB% ERA WHIP SIERA DRS
Brandon Webb SP 2006 6.5 235 13.5 3.10 1.13 2.99 4

 

Los Angeles Dodgers

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Russell Martin C 2007 5.5 .293 .469 .369 19 21 18
Hanley Ramirez DH 2013 5.1 .345 .638 .442 20 10
Matt Kemp CF 2011 8.3 .324 .586 .414 39 40 -5
Adrian Gonzalez 1B 2014 3.5 .276 .482 .349 27 1 12
J.D. Drew RF 2006 4.2 .283 .498 .384 20 2 4
Casey Blake 3B 2009 4.6 .280 .468 .358 18 3 9
Andre Ethier LF 2008 3.4 .305 .511 .384 20 6 -1
Mark Ellis 2B 2012 2.7 .258 .364 .314 7 5 10
Rafael Furcal SS 2010 4.1 .300 .460 .358 8 22 4
WAR IP K-BB% ERA WHIP SIERA DRS
Clayton Kershaw SP 2015 8.6 232.2 29.1 2.13 0.88 2.24 0

 

San Francisco Giants

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Randy Winn CF 2008 5.2 .306 .426 .354 10 25 9
Buster Posey C 2012 7.7 .336 .549 .417 24 1 0
Barry Bonds DH 2007 3.2 .276 .565 .415 28 5
Ray Durham 2B 2006 3.6 .293 .538 .384 26 7 -6
Pablo Sandoval 3B 2011 5.3 .315 .552 .393 23 2 15
Hunter Pence RF 2014 4.7 .277 .445 20 13 .339 0
Brandon Crawford SS 2015 4.7 .256 .462 .326 21 6 20
Brandon Belt 1B 2013 4.4 .289 .481 .361 17 5 4
Andres Torres LF 2010 6.3 .268 .479 .355 16 26 5
WAR IP K-BB% ERA WHIP SIERA DRS
Tim Lincecum SP 2009 7.7 225.1 21.3 2.48 1.05 2.91 -5

 

Miami Marlins

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Hanley Ramirez SS 2008 7.5 .301 .540 .404 33 35 -3
Chris Coghlan LF 2009 2.7 .321 .460 .374 9 8 -19
Miguel Cabrera 1B 2006 6.3 .339 .568 .421 26 9 -10
Giancarlo Stanton RF 2014 6.2 .288 .555 .400 37 13 7
Dann Uggla 2B 2010 4.6 .287 .508 .381 33 4 -9
Justin Ruggiano CF 2012 2.6 .313 .535 .394 13 14 6
Gaby Sanchez 1B 2011 2.7 .266 .427 .342 19 3 5
J.T. Realmuto C 2015 1.8 .259 .406 .301 10 8 6
Jeremy Hermida DH 2007 2.2 .296 .501 .378 18 3
WAR IP K-BB% ERA WHIP SIERA DRS
Jose Fernandez SP 2013 4.1 172.2 18.9 2.20 0.98 3.15 3

 

New York Mets

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Jose Reyes SS 2011 5.9 .337 .494 .380 7 39 -13
David Wright 3B 2007 8.4 .325 .546 .416 30 34 12
Carlos Beltran CF 2006 7.8 .275 .594 .419 41 18 13
Carlos Delgado 1B 2008 2.4 .271 .518 .367 38 1 -11
Scott Hairston LF 2012 1.6 .263 .504 .346 20 8 3
Curtis Granderson RF 2015 5.1 .259 .457 .357 26 11 12
Daniel Murphy 2B 2014 2.5 .289 .403 .326 9 13 -10
Josh Thole C 2010 1.3 .277 .366 .322 3 1 -3
Angel Pagan DH 2009 2.9 .306 .487 .366 6 14
WAR IP K-BB% ERA WHIP SIERA DRS
Matt Harvey SP 2013 6.5 178.1 23.2 2.27 0.93 2.63 0

 

Washington Nationals

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Anthony Rendon 2B 2014 6.5 .287 .473 .359 21 17 4
Alfonso Soriano LF 2006 5.4 .277 .560 .386 46 41 18
Bryce Harper RF 2015 9.5 .330 .649 .466 42 6 7
Ryan Zimmerman 3B 2009 6.6 .292 .525 .379 33 2 22
Adam Dunn 1B 2010 3.0 .260 .536 .381 38 0 -11
Ian Desmond SS 2013 4.8 .280 .453 .341 20 21 -3
Wilson Ramos C 2011 2.6 .267 .445 .336 15 0 -5
Ryan Church CF 2007 2.8 .272 .464 .354 15 3 3
Willie Harris DH 2008 3.0 .251 .417 .337 13 13
WAR IP K-BB% ERA WHIP SIERA DRS
Gio Gonzalez SP 2012 5.0 199.1 15.9 2.89 1.13 3.13 -1

 

San Diego Padres

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Brian Giles RF 2008 4.1 .306 .456 .376 12 2 1
Chase Headley 3B 2012 7.5 .286 .498 .381 31 17 -3
Adrian Gonzalez 1B 2009 5.8 .277 .551 .406 40 1 15
Justin Upton LF 2015 3.6 .251 .454 .344 26 19 8
Chris Denorfia DH 2010 1.6 .271 .433 .334 9 8
Jedd Gyorko 2B 2013 2.4 .249 .444 .325 23 1 -1
Khalil Greene SS 2006 2.3 .245 .427 .329 15 5 9
Rene Rivera C 2014 3.0 .252 .432 .327 11 0 10
Cameron Maybin CF 2011 4.3 .264 .393 .316 9 40 15
WAR IP K-BB% ERA WHIP SIERA DRS
Jake Peavey SP 2007 6.7 223.1 19.2 2.54 1.06 3.30 2

 

Philadelphia Phillies

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Jimmy Rollins SS 2007 6.5 .296 .531 .374 30 41 5
Chase Utley 2B 2009 8.2 .282 .508 .395 31 23 12
Ryan Howard 1B 2006 5.9 .313 .659 .447 58 0 -9
Jayson Werth RF 2008 5.1 .273 .498 .375 24 20 6
Domonic Brown LF 2013 1.9 .272 .494 .351 27 8 -7
Marlon Byrd DH 2014 2.2 .264 .445 .327 25 3
Carlos Ruiz C 2012 5.2 .325 .540 .401 16 4 3
Placido Polanco 3B 2010 3.7 .298 .386 .320 6 5 7
Odubel Herrera CF 2015 3.9 .297 .418 .337 8 16 10
WAR IP K-BB% ERA WHIP SIERA DRS
Roy Halladay SP 2011 8.3 233.2 19.8 2.35 1.04 2.78 -3

 

Pittsburgh Pirates

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Russell Martin C 2014 5.0 .290 .430 .368 11 4 12
Jung-ho Kang 3B 2015 3.9 .287 .461 .380 15 5 4
Andrew McCutchen CF 2013 8.4 .317 .508 .393 21 27 7
Jason Bay LF 2006 5.2 .286 .532 .398 35 11 -2
Garrett Jones 1B 2009 2.7 .293 .567 .397 21 10 0
Xavier Nady RF 2008 2.3 .330 .535 .397 13 1 2
Pedro Alvarez DH 2010 1.6 .256 .461 .343 16 0
Neil Walker 2B 2011 2.6 .273 .408 .324 12 9 -3
Jack Wilson SS 2007 2.5 .296 .440 .341 12 2 14
WAR IP K-BB% ERA WHIP SIERA DRS
A.J. Burnett SP 2012 3.0 202.1 13.9 3.52 1.24 3.48 -3

 

Cincinnati Reds

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Shin-Soo Choo CF 2013 5.5 .285 .462 .393 21 20 -17
Joey Votto 1B 2015 7.4 .314 .541 .431 29 11 6
Adam Dunn LF 2007 2.9 .264 .554 .402 40 9 -26
Scott Rolen 3B 2010 4.6 .285 .497 .369 20 1 10
Brandon Phillips 2B 2011 5.4 .300 .457 .353 18 14 6
Devin Mesoraco C 2014 4.5 .273 .534 .384 25 1 2
Jonny Gomes DH 2009 1.0 .267 .541 .377 20 3
Zack Cozart SS 2012 2.3 .246 .399 .301 15 4 12
Ryan Freel RF 2006 3.8 .271 .399 .342 8 37 12
WAR IP K-BB% ERA WHIP SIERA DRS
Edinson Volquez SP 2008 4.2 196 13.5 3.21 1.33 3.84 -1

 

Colorado Rockies

Player POS YEAR WAR AVG SLG wOBA HR SB DRS
Dexter Fowler DH 2012 2.3 .300 .474 .378 13 12
Carlos Gonzalez CF 2010 5.8 .336 .598 .413 34 26 -1
Troy Tulowitzki SS 2011 5.4 .302 .544 .389 30 9 11
Matt Holliday LF 2007 6.9 .340 .607 .432 36 11 0
Nolan Arenado 3B 2015 4.5 .287 .575 .376 42 2 18
Justin Morneau 1B 2014 2.6 .319 .496 .371 17 0 8
Michael Cuddyer RF 2013 2.3 .331 .530 .396 20 10 -16
Chris Iannetta C 2008 3.1 .264 .505 .392 18 0 0
Jamey Carroll 2B 2006 2.7 .300 .404 .348 5 10 18
WAR IP K-BB% ERA WHIP SIERA DRS
Ubaldo Jimenez SP 2009 5.3 218 12.4 3.51 1.23 3.76 3

Here is how the teams ranked overall:

  1. Cardinals 57.1
  2. Brewers 56.9
  3. Yankees 52.8
  4. Giants 52.8
  5. Red Sox 50.9
  6. Phillies 50.9
  7. Angels 50.1
  8. Dodgers 50.0
  9. Tigers 49.3
  10. Nationals 49.2
  11. Braves 49.1
  12. Indians 48.7
  13. Orioles 48.2
  14. Rays 47.4
  15. Blue Jays 47.0
  16. Diamondbacks 46.6
  17. Rangers 45.8
  18. Mets 44.4
  19. Royals 43.7
  20. Cubs 42.8
  21. Mariners 42.6
  22. Astros 42.4
  23. Reds 41.6
  24. Padres 41.3
  25. Rockies 40.9
  26. Marlins 40.7
  27. Twins 39.6
  28. Athletics 38.7
  29. Pirates 37.2
  30. White Sox 35.2

Considering their dominance, the Cards were a good pick to be first, but the Brewers didn’t seem like a second place consideration.  That’s just like real baseball though – so much has to do with sequencing.  I liked seeing unlikely names pop up more than once on this list – I’m looking at you, Mark DeRosa.  This was a fun exercise and I’m probably a better fan for it.

I think it would be interesting to look at average payroll, for each club over this time period.  If you want to do that – DO IT! And shoot it over to me!

It’s also interesting to look at the teams who have been rebuilding for a while that made the playoffs last year: Blue Jays, Rangers, Mets, Royals, Cubs, Astros, Pirates are all ranked (by this exercise) 15th or below – and of course the perennial threats: Cardinals, Yankees, Dodgers are all ranked in the top 10 (top 8 in fact)

Free Agent Landing Places Predictions Part III: Starting Pitchers

Deep pitching market.

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (IP)
Zack Greinke 32 Dodgers Giants Diamondbacks 13.6 (602.2)
David Price 30 Cubs Red Sox Dodgers 16.9 (655.1)
Johnny Cueto 30 Red Sox Cubs Tigers 9.5 (516.1)
Jordan Zimmermann 30 Giants Dodgers Tigers 12.0 (614.2)
Jeff Samardzija 31 Cubs Diamondbacks Yankees 9.5 (647.1)
Wei-Yin Chen 30 Royals Tigers Nationals 7.2 (514)
Doug Fister 32 Tigers Pirates Mariners 5.8 (475.2)
Yovani Gallardo 30 Royals Tigers Pirates 6.7 (557.1)
Scott Kazmir 32 Astros Mariners Giants 8.3 (531.1)
Hisashi Iwakuma 35 Mariners Dodgers Rangers 8.6 (528.1)
J.A. Happ 33 Pirates Blue Jays Orioles 5.3 (422.2)
John Lackey 37 Cardinals Rangers Cubs 8.4 (605.1)
Mike Leake 28 Giants Diamondbacks Royals 6.0 (598.2)
Kenta Maeda 28 Diamondbacks Red Sox White Sox N/A (Japan)
Danny Salazar 26 Reds Rockies Marlins 6.0 (347)
Carlos Carrasco 29 Marlins Reds Rockies 8.2 (364.1)
Jose Quintana 27 Marlins Nationals Dodgers 13.4 (606.2)
Stephen Strasburg 27 Dodgers Red Sox Yankees 11.1 (525.1)
Matt Harvey 27 Dodgers Red Sox Yankees 12.2 (427)
Tyson Ross 29 Red Sox Dodgers Orioles 9.6 (516.2)
Julio Teheran 25 Diamondbacks Yankees Orioles 6.8 (607.1)
Shelby Miller 25 Yankees Red Sox Pirates 6.4 (563.2)
Andrew Cashner 29 Cubs Tigers Yankees 7.3 (483)

10/27 possible points thus far

Predicting Landing Spots For Free Agents and Potential Trades: Part II

This time we’re looking at outfielders and John Jaso.  Yup.

 

Left Fielders:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Nori Aoki 34 Reds Giants Orioles 5.3 (1,615)
Yoenis Cespedes 30 Yankees Angels Orioles 12.4 (1,895)
Alex Gordon 32 Royals Yankees Angels 13.1 (1,765)
Gerardo Parra 29 Giants Reds Braves 5.0 (1,826)
Ah-seop Son 28 Phillies Nationals Mariners
Justin Upton 28 Angels Yankees Nationals 10.6 (1,904)
Chris Coghlan 30 Indians Athletics Angels 5.3 (1,149)
Brett Gardner 32 Reds Cubs Mets 9.4 (1,901)

 

Center Fielders:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Dexter Fowler 30 Mets Cubs Rockies 6.8 (1,687)
Austin Jackson 29 Twins Rockies Giants 6.2 (1,797)
Denard Span 32 Cubs Rangers Nationals 8.8 (1,605)
Jackie Bradley Jr 26 Cubs Mets Phillies 2.6 (785)
Marcell Ozuna 25 Orioles Athletics Dodgers 6.5 (1,397)

 

Right Fielders:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Marlon Byrd 38 Braves Athletics Orioles 7.2 (1,760)
Jason Heyward 26 Yankees Cubs Cardinals 14.6 (1,699)
Jay Bruce 29 Angels Orioles Indians 3.4 (1,891)
Jorge Soler 24 Indians Mets Rangers 0.8 (501 PA)
Carlos Gonzalez 30 Angels Indians Cardinals 6.6 (1,325)
Josh Reddick 29 Cardinals Orioles Indians 8.3 (1,419)
Yasiel Puig 25 Mets Indians Rangers 10.9 (1,383)

 

Designated Hitter:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
John Jaso 32 Orioles Angels Rays 3.5 (809 PA)

2/6 thus far

Predicting Landing Spots for Free Agents and Potential Trade Targets Part I: Infield Edition

The title really says it all.  I’m not diving deep into the bargain bin, but instead I’ll be focusing on the more marketable players. The non free agent players I have named are speculative targets for the teams listed with the team in the first column being the team they’d most likely be acquired by IF (big ifs) they are traded.

Without standing on ceremony, here ya go:

 

Catchers:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Alex Avila White Sox Diamondbacks Nationals 3.0 (1,055)
Chris Iannetta Mariners Angels Rays 5.5 (1,089)
Dioner Navarro 32 Blue Jays Braves Mariners 4.7 (948)
Stephen Vogt 31 Braves Twins White Sox 4.3 (946)
Derek Norris 27 Rangers Mariners Braves 7.2 (1,307)
Jonathan Lucroy 30 Rangers Twins Nationals 10.6 (1,650)

 

First Basemen:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Chris Davis 30 Astros Cardinals Yankees 13.4 (1,868)
Justin Morneau 35 Orioles Rays Astros 3.8 (1,367)
Mike Napoli 34 Rockies Rangers Cardinals 7.0 (1,547)
Steve Pearce 33 Mets Nationals Marlins 6.1 (846)
Adam Lind 32 Blue Jays Mariners Astros 5.3 (1,411)
Freddie Freeman 26 Astros Rangers Indians 12.6 (1,818)
Mitch Moreland 30 Brewers Pirates Braves 2.7 (1,217)
Joey Votto 32 Red Sox Yankees Mariners 14.4 (1,693)

 

Second Basemen:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Kelly Johnson 34 Mets Orioles Pirates 2.1 (1,039)
Howie Kendrick 32 White Sox Yankees Nationals 9.4 (1,682)
Daniel Murphy 31 Angels Mets Orioles 8.1 (1,877)
Chase Utley 37 Athletics Angels Dodgers 8.3 (1,618)
Ben Zobrist 35 Dodgers Yankees Mets 12.6 (1,887)
Brandon Phillips 35 Diamondbacks Angels White Sox 6.7 (1,788)
Neil Walker 30 Yankees Angels Mets 8.7 (1,725)

Shortstops

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
Asdrubal Cabrera 30 White Sox Rays Nationals 4.2 (1,729)
Ian Desmond 30 Mets Padres Phillies 10.5 (1,944)
Alexei Ramirez 34 Phillies Padres Rays 5.8 (1,953)
Starlin Castro 26 Padres Mets Phillies 3.7 (1,852)

 

Third Basemen:

Player 2016 Age Team 1 Team 2 Team 3 3 Year fWAR Total (PA)
David Freese 33 White Sox Braves Padres 3.9 (1,502)
Juan Uribe 37 Braves White Sox Mets 10.5 (1,227)
Evan Longoria 30 Angels Dodgers Astros 14.1 (2,063)
Todd Frazier 30 Indians Astros Angels 12.2 (1,938)

10/33 thus far

 

 

 

 

 

 

 

BatCast – Bat Flip Tracker (ft. preachy commentary)

I love bat flips. I would have no problem if bat flips became a more theatrical experience. By the power of inference, or by simply reading the first sentence, I’m certain you can accurately predict how I feel about Jose Bautista’s bat flip.  While anyone with an incorruptible soul has been nobly spewing self-righteous significations about how disrespectful Bautista’s bat flip was, I’ve been primarily concerned with one thing: the trajectory of that bat flip. It was a huge exclamation point on a huge moment and it was a pretty significant departure from more “conventional” bat flips.

Most bat flips do not exceed shoulder height. Think about the bat flips that you have mimicked the most in your life.  For me it’s been Griffey Jr,  SosaMcGwire,Ortiz, and McGriff. One could argue that what those players possessed were, by definition, closer to bat drops rather than flips, but you’ll still find these players featured in various “best of bat flips” videos on Youtube. Bautista’s bat flip diverges from the norm immediately upon release, in that it actually started at his shoulders. While this is awesome, it didn’t break new ground. Yasiel Puig flips his bat from above his head on fly outs and triples. Yoenis Cespedes had a triumphant bat flip of his own on Monday night, but for a superabundance of reasons that you already know, Bautista’s bat flip has hogged the limelight. In lieu of this, we’ll focus on breaking down Bautista’s bat flip into some tangible numbers and simply apply that same method to Cespdes’ for a comparison.

MLB debuted statcast this year, and among its nifty features was the home run tracker.  The home run tracker allowed viewers at home to process new data on home runs, specifically, exit velocity, the angle of the home run, and distance. The data I’m about to bring to you is based on this exact premise, but it studies the bat flip.  BatCast: The Bat Flip Tracker™.

Disclaimers:

  1. There is no ™ on BatCast, I just thought it was funny and hope that it’s not illegal to falsely claim a copyright.
  2. I am not an engineer, mathematician, or a numerically inclined vampire.  The last math class I took was trigonometry during my junior year of high school 13 years ago.
  3. I’m about to present some very inexact numbers based on frozen images I’ve gathered from the internet to bring you the BatCast data on Jose Bautista’s bat flip.

 

Without further ado:

 

bautJOSE BAUTISTA

7th Inning – ALDS Game 5

Rangers @ Blue Jays

Score: 3 – 3

BATCAST
Initial Launch Velocity 14.63 mph (23.54 km/h)
Total Horizontal Distance 6′-6″ (1.98m)
Launch Angle 78.6 Degrees

Here is the freeze frame of the moment in time that somehow is already emblazoned across purchasable T-shirts. Following the majestic shot will be the explanation of the method I used to come up with the rough, ROUGH BatCast numbers (also featured in metric to honor the Blue Jays and the soil, or turf, of Canada where it all went down).

(Darren Calabrese/The Canadian Press via AP) MANDATORY CREDIT

You didn’t think I’d forget the GIF(s), did you? (GIF sources: FS1 + mlb.com) 1475063766308945444               101415_tor_bats_batflip_lowres_gjvlzoc9

 

The numbers:

Launch Height: 5′-2″ (1.57m)

Jose Bautista stands exactly 6′-0″ tall (1.83m). In the image I printed out and measured hastily, he is about 3.33″ tall. If you’re disappointed in my measurements already, I did warn you that it would be rough, and you have every right to stop reading. If we measure up to his shoulder/trap area, where he released the bat, we get 2.87″. After we apply some simple algebra: 3.33/2.87  =  72/x we come up with 62″ or 5′-2″ (1.57m) for the launch height. This also works with the idea that the head and neck comprise 10.75% of our total height.

Horizontal Distance: 6′-6″ (1.98m)

Bautista hurls the bat across his body with his left hand from his right shoulder, which at point of launch, was pretty much on the inside corner of home plate for a right-handed hitter.  The bat lands just outside the left-handed batter’s box which we know is 4’ wide.  Given that the plate is 17” wide and there is a 6” cushion between the batter’s box and the plate, we can estimate the horizontal distance that the bat traveled to be right around 6.5’ (1.98m).

Hang Time: 1.52 seconds

I derived this number from watching the video and using my phone as a stop watch.  After 10 runs, I had an average time of 1.52 seconds. There is no metric conversion for time (winky face).

Parabolic Trajectory Calculator

This online calculator was paramount to finding the rest of the data provided. Once I had the initial height, the hang time, and the horizontal distance, I tinkered with numbers for the initial velocity and trajectory angle until everything jived with the rough numbers I had figured.
trajectory

Launch Angle: 76.8 Degrees

Jose launches this bat pretty tight to his body, as evidenced by where the bat lands (at the outside edge of the left-handed batter’s box).  A rough/convenient measurement of the launch angle gives us 76 Degrees. But after manipulating the numbers in the calculator, we have a more accurate launch angle of 76.8 Degrees.

 

launch angle

Launch velocity: 14.63 mph (23.54 kmh) and Apex: 12′-0.36″ (3.67m)

I had actually tried to measure the apex using the same method I performed to figure the launch height, but it would be a disservice to us all had I used the 10.5′ number that produced. Jose Bautista flips the bat in such a manner that he would have thrown it over himself STANDING on top of himself – or twice his height. In the trade of bat flipping, this is probably considered light tower power.

 

Cespedes vs Bautista

Using the same method let’s look at, what we can figure to be at least a pretty similar and recent comp.

First, Cespedes’ flip.

THIS_Cespedes_launches_NLDS_home_run_into_the_night

 

Yoenis Cespedes’ bat flip came in the 4th inning of game 3 of the NLDS with the score already 7 – 3 in favor of the Mets.  The tension in this game was obviously very high as the series was tied at 1 – 1, but circumstantial tension also built differently as there had been a day between this game and the game that saw the Utley v.Tejada incident.

yoenisYOENIS CESPEDES

4th Inning – ALDS Game 3

Dodgers @ Mets

Score: 7 – 3

BATCAST
Initial Launch Velocity 12.08 mph (19.44 km/h)
Total Horizontal Distance 10’-8.1″ (3.254m)
Launch Angle 60 Degrees

 

yoenis

By the numbers, these are two fairly similar bat flips. What Cespedes’ flip lacked in height (8.5 ft; 2.6 m), it made up for in sheer distance (reference table above). But judged by context (inning, game, score), isn’t Cespedes’ bat flip actually more wrong? Of course I’m saying that with my tongue in my cheek – a bat flip is neither wrong nor right. A bat flip is really just like adding an exclamation point to a moment instead of a period. How would you write it?

Home Run.

or

Home Run!

 

Part II: (preachy commentary)

In the end, people only start talking about a bat flip in context of right or wrong if it’s offensive to a player on the opposing team. Well, it was. It was offensive to Sam Dyson, who, without coincidence, was the pitcher who had just given up the home run to Bautista that spurred the bat flip. Dyson’s reaction seemed to be more of an unhinging; a singular representation of the collective mind of the Rangers. As history now goes, the Rangers were the beneficiaries of strange fortune in the top half of the 7th, nudging them ever closer to the Championship Series. The following half inning saw the Rangers’ 167 game journey and bid for a championship, suddenly unravel in a strange, beautiful, sad, and unpredictable sequencing of events. Dyson’s cortisol levels were no doubt already higher than usual, having inherited 3 base runners and tasked with getting two outs against the middle of baseball’s most potent offense that features the near certain American League MVP winner and MLB’s leading home run hitter over the better part of the last decade – oh and these would also be the first two players he would be facing. These facts about the the situation and the prowess of the hitters aren’t are somewhat minimized in a pitcher’s mind that is focusing on executing his game plan, but I felt compelled to catch a glimpse into Dyson’s psyche before it all went down.

And then it went down (refer to GIFs of Bautista above).

Dyson will have to internalize the experience, if he’s not/hasn’t already, and I don’t know what that will be like for him. But immediately following the Bautista home run was not the time for that since Dyson still needed to get one more out in the inning. In the moment, amidst all the pandemonium, he needed something he thought he had some semblance of control over and he found it, eventually, supposedly, in the bat flip. In fact, the bat flip was something that would, in some strange way, vilify the hero and deflect the attention away from the fact that he just gave up the home run that would eventually be the nail in the coffin (purely from a runs standpoint) for his team’s season (of course it’s more complicated than that). I’m not saying Sam Dyson consciously thought of all this; we’re animals and we’re not always aware of, or able to keep up with the torrid pace of our physiological states – and BELIEVE that Dyson was going through some stuff. However, to believe that Dyson acted above the bat flip, or any of it, is to ignore the fact that he too reacted instinctually to the situation. He made things worse by misinterpreting gestures and pointing fingers at inconsequential things like bat flips. Dyson’s reaction, while not as grandiose as Bautista’s, was a reaction that was just as impulsive as Bautista’s bat flip, and yet, somehow, it seems like a lot of people deem his reaction to be more acceptable. Is it because he did his best to feign composure through it all? Do you really think he wouldn’t have approached Edwin Encarnacion in the midst of all the mayhem if the bat flip didn’t happen? I don’t. There is a Great Repression in this country, and I hate the way I phrased that, because it sounds so cheesy and adolescent, and really, what do I know? But it does feel like there is a sweeping under the rug of emotions, of feelings, and of truth. This is just how we’ve structured things; to be poised in all circumstance so that no one can see how truly horrible or beautiful we really are. Newscasters delivering horror stories; politicians admitting to affairs; talking about something you did that you’re extremely proud of but don’t want to seem too proud of – there are guidelines right down to accepted cadences, gestures, tones, and expressions for delivering each of these like they each came from an acceptable social norms textbook. Big, real emotions tend to make other people feel uncomfortable because conduct says we repress them and stay status-quo. If I seem to be disgusted by it all, I’m not…as much as I used to be. But that’s probably because of anger management, finding love, and the birth of my son – 3 things I can talk in restricted excitement about because I’m starting to well-adjust…obviously, I’m not there yet if I’m ranting about all of this because I’m caught up in the debate of whether or not a bat flip is acceptable.

Sam Dyson said, in a post-game interview, “he (Bautista) is a huge role model for the younger generation coming up playing this game and he’s doing stuff kids do in wiffle ball games and backyard baseball, it shouldn’t be done”. First of all, Sam Dyson has been teammates with Jose Bautista, Giancarlo Stanton, and bat flipper extraordinaire, Jose Fernandez. Do you think he was appalled at their flips when he was their teammate? Also, Sam Dyson literally just said, it’s a game, and that’s the point anyone who has ever played the game has been hammered with – “never stop having fun! Remember why you play! It’s just a game!”. I understand the game Dyson and Bautista play is also their job. I understand that to play the game professionally means having to work harder than you ever thought you could work, and that that probably has a tendency to mute and mature the game a bit – and like with anything, sometimes it’s a struggle to remember why you do it. But for Jose Bautista, everything culminated in that one swing. In that one moment after he connected with the pitch from Dyson, every swing he ever took, every time he tried out for a team, every early morning and every late night spent training made perfect sense to him. He was experiencing the moment most people, including himself up to that point, only conjure up in back yard wiffle ball games. So please, in a world where we’re forced to repress so much, don’t take the humanity out of the game, and don’t try to take away anything from Jose Bautista’s moment. Given everything in his life that led Bautista to that immensely emotional game 5, given the gravity of the situation, the no doubt distance of the home run, the do-or-die premise of the game, I’d say that bat flip was absolutely, spot-on, 100% perfect.

End of Year Awards

Here are my votes for the end of season awards.  I think I’ve covered every award here and it represents who I would vote for, not who I think will win (though a lot of them will win):

MVP AVG OBP SLG OPS WAR wOBA wRC+ R HR RBI
Bryce Harper .336 .467 .657 1.124 8.6 .467 201 104 36 85
Josh Donaldson .307 .374 .592 .966 7.9 .407 162 108 37 115
Cy Young IP ERA WHIP K/9 FIP xFIP SIERA WAR
Jake Arrieta 199 1.99 0.92 9.23 2.51 2.74 2.91 5.5
Dallas Keuchel 200.2 2.29 0.99 8.30 2.81 2.69 2.78 5.5
Rookie of the Year AVG OBP SLG OPS WAR wOBA wRC+ R HR RBI
Kris Bryant .267 .366 .482 .847 5.1 .367 132 77 23 86
Carlos Correa .273 .339 .500 .839 2.7 .361 131 39 17 51
Comeback Player AVG OBP SLG OPS WAR wOBA wRC+ R HR RBI
Joey Votto .316 .459 .559 1.018 6.7 .434 176 86 27 70
Alex Rodriguez .256 .358 .497 .855 2.6 .365 133 75 30 78
Gold Glove FLD% DRS CS% RAA RZR OOZ ARM RngR UZR UZR/150
Buster Posey .999 7 39.3 15.5
Brandon Belt .998 7 .857 31 4.5 7.4 10.8
Danny Espinosa .997 10 .833 36 6.5 8.9 16.3
Nolan Arenado .971 18 .772 61 9.4 10.8 12.1
Andrelton Simmons .993 18 .810 48 2.3 10.8 13.1
Billy Hamilton 1.000 7 .952 66 2.0 12.0 15.1 20.8
Jason Heyward .988 15 .935 88 2.1 8.8 11.4 15.0
Michael Taylor .989 7 .954 76 2.3 11.5 13.5 18.2
Zack Greinke .966 8
Jason Castro .999 6 35.5 10
Mitch Moreland .997 3 .759 15 4.5 4.8 7.8
Carlos Sanchez .991 6 .767 37 0.2 4.4 6.6
Manny Machado .964 15 .793 67 8.0 7.9 9.5
Alcides Escobar .980 4 .813 58 7.3 3.8 11.7
Kevin Kiermaier .992 37 .928 87 5.3 20.4 25.6 43.8
Kevin Pillar .995 19 .941 116 1.1 8.9 10.6 13.1
Adam Jones .993 5 .921 66 8.6 -2.6 6.4 10.2
Dallas Keuchel 1.000 13
Silver Slugger AVG OBP SLG OPS ISO wOBA wRC+ HR SB R+RBI
Buster Posey .329 .387 .487 .874 .157 .372 144 17 2 148
Paul Goldschmidt .317 .431 .548 .979 .232 .408 157 27 21 183
Dee Gordon .328 .355 .408 .764 .080 .332 108 2 50 108
Nolan Arenado .288 .325 .580 .904 .292 .375 121 37 1 191
Jung-ho Kang .287 .358 .469 .826 .181 .360 132 15 5 112
Carlos Gonzalez .272 .330 .556 .886 .285 .372 120 36 2 163
Andrew McCutchen .300 .403 .511 .914 .211 .388 151 21 7 170
Bryce Harper .336 .467 .657 1.124 .321 .467 201 36 6 189
Madison Bumgarner .258 .279 .515 .794 .257 .339 121 5 0 18
Stephen Vogt .271 .349 .467 .816 .196 .347 124 18 0 121
Chris Davis .260 .351 .556 .907 .296 .383 145 41 2 185
Jose Altuve .311 .352 .426 .779 .116 .336 113 11 36 125
Josh Donaldson .307 .374 .592 .966 .285 .407 162 37 5 223
Carlos Correa .273 .339 .500 .839 .227 .361 131 17 11 90
Nelson Cruz .310 .380 .587 .967 .277 .409 169 39 3 159
J.D. Martinez .281 .342 .548 .890 .267 .376 141 36 3 171
Mike Trout .293 .394 .570 .963 .277 .404 166 34 10 164
David Ortiz .269 .356 .540 .896 .271 .372 134 32 0 154
Rolaids Relief Man W SV ERA K/9 WAR
Trevor Rosenthal 2 44 1.59 10.54 2.2
Andrew Miller 2 32 1.76 13.94 1.6
Manager of the Year
Joe Maddon
A.J. Hinch
Delivery Man W SV ERA K/9 WAR
Trevor Rosenthal 2 44 1.59 10.54 2.2
Hank Aaron Award AVG OBP SLG OPS ISO wOBA wRC+ HR SB R+RBI
Bryce Harper .336 .467 .657 1.124 .321 .467 201 36 6 189
Nelson Cruz .310 .380 .587 .967 .277 .409 169 39 3 159

The Cleveland Indians’ Starting Rotation: The Trail of Tears to The Trail of No Fears

Remember at the end of last season and before this season when we all foresaw an Indians rotation that could possibly feature somewhere between 2 and 5 really good, and possibly great, starting pitchers?  Don’t get bogged down on the slight exaggeration of that 1st sentence – To recap what we were looking at coming into this season for the Indians’ rotation:  Corey Kluber won the 2014 AL Cy-Young; Carlos Carrasco had a string of starts to end 2014 in which he seemingly (finally) figured out how to harness all of his powers in a bid to ascend his name to an echelon where only Clayton Kershaw’s name resides; Danny Salazar has always had elite swing and miss stuff and was also excellent in the second half of 2014;  Trevor Bauer and his costco sized arsenal of pitches have made some of us incredulously, if not warily optimistic since he was taken 3rd overall in 2011; and even T.J. House made us pause and take notice with his strong second half of 2014.

Then, like hype men with a special blend of Cleveland Kool-Aid being intravenously administered, Eno Sarris and Daniel Schwartz posted one of my favorite fangraphs articles ever, Pitch Arsenal Score Part Deux, and the anticipation over the Indians’ rotation pulsated like a vein in the neck of John Rambo in the midst of fleeing from man-hunters.

The supporting cast, the lineup, looked poised to support the staff with plenty of runs.  Returning would be: break out star Michael Brantley; bounce-back candidate Jason Kipnis; now-full-time-first-basemen, Carlos Santana; a supposedly healthy Michael Bourn; an offense-first but totally-respectable-defensively, Yan Gomes; and an actually-not-that-horrible-in-2014, Lonnie Chisenhall.  Slugger Brandon Moss, and contact-happy-supposedly-glove-firstJose Ramirez had secured full-time spots as well in RF and SS respectively.  So even though it wasn’t without flaws, it seemed like they would allow the pitchers to rack up plenty of fantasy-relevant wins.

Note: This post isn’t about the disappointment of the Indians, though they have been disappointing, it’s more about what factors beyond luck have contributed to the numbers of the Indians’ starting rotation at various points throughout the year, and the disparity (big or small) between the pitchers’ rates and predictors at those points.

The Indians’ starting pitchers, or at least the top 4 (Kluber, Carrasco, Salazar, and Bauer) have, for the most part, been putting up good, albeit, inconsistent numbers all year despite posting some elite peripheral rates and ERA indicators.  A number of reasons have caused these numbers to grow apart (bad), come together, and then grow apart again (good).  Luck can work like a bit of a pendulum, swinging from one extreme, through the middle, and to the other extreme before evening out and that is at the core of what the Indians’ starting pitchers have experienced this year; although they have yet to experience the final stabilization phase.

We will examine plenty of numbers (Beginning of season to August 18th) based on this time frame: (Spoiler alert – this article is long and dense, and this timeline serves as a sort of cliff notes as to how the staff’s numbers have improved throughout the year – so if you’re the type of person who feels like looking at a bunch of data is superfluous when the bullet points are in front of your eyes, just read the timeline and be done with it)

timeline

April 6th – May 23rd/May 24th – June 15th

One week into the season, before it was evident that the team’s defense was very sub-par, Yan Gomes hurt his knee and hit the disabled list for over a month.  Roberto Perez filled in quite nicely, and looking at just a couple numbers, could be considered the more valuable catcher (1.4 WAR compared to 0.5 WAR for Gomes).  Brett Hayes (0.0 WAR) was called up and was the secondary catcher during this period.  Behold, a table from StatCorner:

statcorner

 

 

 

 

 

 

Perez has had the least amount of pitches in the zone called balls and the most amounts of pitches out of the zone called strikes.  Overall, despite receiving fewer pitches than Gomes, he has saved more runs (4 DRS to Gomes’ 1) and their caught stealing rates are basically identical with a slight edge going to Perez – 38% to Gomes’ 35%.  Gomes was much better in terms of framing in 2014, and it’s possible the knee injury has limited his skills all around this season.  Anyways, from April 6th – May 23rd, the combined stats of Kluber, Salazar, Carrasco, and Bauer look like this:

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.49 2.16 2.46 2.51 25.3 48.6
Salazar 3.50 3.27 2.46 2.30 28.7 43.8
Carrasco 4.74 2.60 2.67 2.82 22.3 48.9
Bauer 3.13 3.23 4.09 3.94 14.2 35.7
3.75 22.7 44.7

Gomes returned as the primary catcher on 05/24, and from that point through June 15th, the cumulative numbers aren’t too different, although there is a dip in both K-BB% and GB% that we’ll have to look into.

ERA FIP xFIP SIERA K-BB% GB%
Kluber 3.67 3.26 3.20 3.19 19.8 43.8
Salazar 3.60 3.72 3.36 3.43 17.3 47.7
Carrasco 3.65 2.83 3.29 3.17 20.2 44.1
Bauer 3.96 4.72 4.47 4.30 11.5 36.8
3.74 17.2 43.1

So despite lower K-BB and ground ball percentages (leading to higher ERA predictors), the group’s ERA in the segment of the season when Gomes was reinstated is essentially exactly the same as from the first block of time with Perez.  Now, I am not a big believer in CERA because there is a high level of variation and too many unknown variables pertaining to how much of the responsibility/credit goes to the catcher, the coaching staff, or the pitcher; but I do think that it’s possible Gomes’ extra service time has enabled him to be more in tune with his staff as well as understand hitter tendencies better than Perez and Hayes.  I realize we’re getting into a gray area of intangibles, so I’ll reel it in with some results based on pitch usage%.

% Difference in Pitch Usage with Yan Gomes compared to Roberto Perez

Pitcher FB% CT% SL% CB% CH% SF%
Corey Kluber -0.09 8.8 -17.3 5.0
Danny Salazar 9.8 -12.6 -4.4 17.1
Carlos Carrasco -6.5 9.4 49.2 13.3
Trevor Bauer -2.9 -15.0 -8.9 78.5 25.8

Using BrooksBaseball Pitch f/x data, let’s painstakingly find out how different each pitcher’s pitch usage was in regards to different counts, or better known as Pitch Sequencing.  We’ll look at first pitches, batter ahead counts, even counts, pitcher ahead counts, and 2 strike count situations.  As good as pitch f/x is, the data still isn’t perfect.  There may be discrepancies if you look at usage at Brooks compared to the usage at fangraphs, so for each pitcher we’ll split the pitches up into three categories: Fastballs (fourseam, sinkers, cutters), Breaking Balls (sliders, curve balls), and Change Ups (straight change/split finger) – I’m aware that splitters are “split fingered fastballs”, but I liken them to change ups more because of the decreased spin rate and generally lower velocity.

*Having a table for each pitcher in regards to pitch sequencing made this article quite messy, so I’ve included a downloadable Excel file, and briefly touched on each pitcher below.

Pitch Sequencing Excel Doc.

Corey Kluber

Looking at the data, Gomes stays hard with Kluber more than Perez until they get ahead in the count.  Perez swaps some early count fastballs for curve balls, but they both see his curve ball as a put away pitch.  Gomes tends to trust Kluber’s change-up more than Perez later in counts and Perez likes it more earlier in counts.

Danny Salazar

Much like with KIuber, when Gomes catches Salazar, they have a tendency to stay hard early.  Gomes pulls out Salazar’s wipe out change up after they’re ahead whereas Perez will utilize it in hitter’s counts as well.

Carlos Carrasco

Carrasco has 5 good pitches and he’s pretty adept at throwing them for strikes in various counts which is why there is some pretty even usage across the board, at least in comparison to Kluber and Salazar.  There is quite a bit more usage of Carrasco’s secondary pitches in all counts and there are pretty similar patterns when Gomes and Perez are behind the plate.  With Hayes, it doesn’t look like there is much that changes in sequencing until there are two strikes on a hitter.

Trevor Bauer

Bauer is probably a difficult pitcher to catch because of the number of pitches he has and the constant tinkering in his game.  Side note: Gomes is the only catcher to have caught a game in which Bauer threw cutters, and in their last game together, Bauer threw absolutely no change-ups or splits.  Bauer’s highest level of success has come with Hayes behind the plate and perhaps that’s from their willingness to expand his repertoire in more counts than Gomes and Perez do, but there is no way I can be certain of that.

Pitch sequencing can effect the perceived quality of each pitch and therefore, can produce more favorable counts as well as induce higher O-Swing and SwStrk percentages (or less favorable and lower).  So despite the framing metrics favoring Perez, the group throws more strikes with Gomes and also induces more swings at pitches outside the zone – although, as previously noted, there is some regression with Gomes behind the dish in terms of SwStrk% and K-BB%.

swing tendencies

 

 

 

 

 

 

 

 

 

aaa0ide

 

 

 

 

 

 

 

 

**These graphs represent numbers through the entire season to garner a bigger sample size.

With lower line drive rates and more medium + soft contact, and (in the case of the Indian’s defense), more fly balls, a conclusion could be jumped to that the staff’s BABIP has trended downward since Gomes regained his role.  A look at BABIP throughout the course of the season:

babip

 

 

 

 

 

 

 

 

 

Woah!  It was well above league average in April and then plateaued at just above league average through mid June, but has been plummeting ever since.  Obviously a catcher is not responsible for this dramatic of a swing in BABIP, so the Indians’ defense must have improved.

June 16th – August 18th

The rotations’ traditional stats look even better if you use June 16th as the starting point:

Pitcher IP H K BB W ERA WHIP
Corey Kluber 84 61 82 16 5 3.11 0.92
Danny Salazar 71 46 69 23 5 2.79 0.97
Carlos Carrasco 77.1 56 77 13 3 2.91 0.89
Trevor Bauer 68.1 69 63 24 4 5.80 1.37
300.2 232 291 76 17 3.59 1.03

 

So let’s take a look at the Indians’ defensive alignment by month (Player listed is the player who received the most innings played at the position).

 

POS April May June 1 – 8 June 9 – 15 June 16 – 30 July August
C Perez Perez Gomes Gomes Gomes Gomes Gomes
1B Santana Santana Santana Santana Santana Santana Santana
2B Kipnis Kipnis Kipnis Kipnis Kipnis Kipnis Ramirez
3B Chisenhall Chisenhall Chisenhall Urshela Urshela Urshela Urshela
SS Ramirez Ramirez Aviles Aviles Lindor Lindor Lindor
LF Brantley Brantley Brantley Brantley Brantley Brantley Brantley
CF Bourn Bourn Bourn Bourn Bourn Bourn Almonte
RF Moss Moss Moss Moss Moss Moss Chisenhall

If you’ve paid attention to the Indians at all, you know they’ve made some trades and called up a couple prospects.  But just how different is the new defense?  Well, we only have a small sample with the current configuration, but it appears to be A LOT better. If BABIP wasn’t enough of an indicator, and it’s not, because there has to be some regression to the mean – it can’t stay that low – here are some numbers from the players who were playing the most in May compared to the players who are playing the most in August (again, numbers represent full season stats):

 

MAY PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Perez .994 2.0 38.5 4
1B Santana .997 -6 0.0 0.7 1.2
2B Kipnis .988 4 4.5 3.6 7.0
3B Chisenhall .963 7 3.1 3.3 10.5
SS Ramirez .948 -2 -2.4 -5.2 -21.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Bourn 1.000 4 -7.2 1.1 -5.8 -11.4
RF Moss .975 -4 1.7 -2.5 -1.1 -1.8
AUG PLAYER FLD% rSB CS% DRS RngR Arm UZR UZR/150
C Gomes .996 0.0 35.0 1
1B Santana .997 -6 0.0 0.7 1.2
2B Ramirez 1.000 1 1.1 2.8 23.2
3B Ursehla .973 2 4.5 6.0 15.7
SS Lindor .967 6 6.0 4.9 14.9
LF Brantley .992 1 0.3 -2.1 -1.4 -3.3
CF Almonte 1.000 2 0.4 -0.2 0.9 10.0
RF Chisenhall 1.000 4 1.6 0.5 2.3 27.3

What’s interesting is that the biggest difference in the infield is Francisco Lindor (Giovanny Urshela has been very solid, but Chisenhall was pretty similar this season at 3B).  I’m sure someone at fangraphs could churn out a really cool article (if someone hasn’t already) that shows us a quantifiable difference an above average to well above average shortstop makes for a team even if you just keep the rest of the infield the same, as the control.  The 2015 Tigers come to mind – a healthyJose Iglesias has made a difference for a team that still features Nick Castellanos at 3B and Miguel Cabrera at 1B.  Teams are willing to sacrifice offensive contributions if a SS has elite defensive skills (Pete Kozma, Andrelton Simmons, Zack Cozart to name a couple off the top of my head).  Lindor, to this point, has been an above average offensive player, too, so this could be special.

At this point the Indians are in last place and are out of contention.  Abraham Almonte is their starting center fielder and with Kipnis back from the DL, Jose Ramirez is not playing 2B, but is instead getting reps in left field while Michael Brantley DHs due to his ailing shoulder.  Perhaps all this means is that they don’t have better replacements; OR PERHAPS they’re planning to establish a more defense oriented squad next year…

Now there’s no doubt that this research has led to some frustrating conclusions.  With Gomes behind the plate, the K rate and GB rate of the staff has trended in the wrong direction in regards to ERA indicators; so is the difference in the batted ball profile plus an improved defense enough to make up for these facts?  This small sample size thinks so, but it could 100% just be noise.  However, there are clubs that are succeeding by using similar tactics right now:

Team ERA FIP ERA-FIP GB% (rank) SOFT% (rank) OSWING% K-BB% (rank)
Royals 3.57 3.93 -0.36 42.1 (29th) 18.1 (16th) 30.9 (19th) 10.5 (26th)
Rays 3.63 3.79 -0.16 42.4 (28th) 18.7 (13th) 31.2 (17th) 14.8 (7th)
Indians (as a reference) 3.85 3.65 0.20 44.7 (17th) 18.2 (15th) 33.3 (2nd) 16.9 (1st)

Granted, the Royals and Rays have the 1st and 2nd best defenses in baseball, and their home parks play differently than the Indians, but they also don’t boast the arms the Indians do.

The Indians have their noses deep in advanced metrics and having rid themselves ofSwisher, Bourn, and Moss during 2015’s trading period has allowed them to deploy a better defensive unit which has amplified their biggest strength – their starting pitching.  Furthermore, their unwillingness to move any of their top 4 starting pitchers also leads me to believe they see next year as a time for them to compete.  I’m not going to speculate what moves the Indians will make in the offseason, but I hope they stick with this defense oriented situation they have gone with recently because it’s been working and because I own a lot of shares of Kluber, Carrasco, and Salazar in fantasy.  Plus, we all like to see teams go from worst to first – or, in order to tie a neat little bow on the article, go from the trail of tears to the trail of no fears.