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?

 

 

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.

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.

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.

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.

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.

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

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,  Sosa, McGwire,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:

 

JOSE BAUTISTA

7^th 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).

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

 

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.

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 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.

 

Yoenis Cespedes’ bat flip came in the 4^th 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.

YOENIS CESPEDES

4^th 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

 

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 1^st 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 3^rd 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)

April 6^th – 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:

 

 

 

 

 

 

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%.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

**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:

 

 

 

 

 

 

 

 

 

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.