Tagged: Washington Nationals

(Nationals) Park(s) and Recreation

If you’re like me, if you aren’t watching baseball, you’re watching Parks and Recreation episodes, making every effort to find ways to incorporate the wisdom of Ron Swanson into your everyday life.

Or at least gifs of Ron—here’s my personal fave:

swanson_poop

Every so often, these worlds collide in a cacophony that is comparable only to a Mouse Rat concert, or perhaps the Pawnee/Eagleton Unity Concert—you pick. Well, it happened just now, in the form of song.

Ladies and gentlemen, I give you ‘5000 Sac Bunts in the Wind’, inspired by Twitter’s very own @nextyeardc, sung to the tune of ‘5000 Candles in the Wind’ by Mouse Rat (formerly Scarecrow Boat):

 

5000 Sac Bunts in the Wind

 

Down in southeast DC here’s the thing

You square to bunt and never swing

 

Come here with a hitting eye

Forget all that it’s time for the sacrifice

 

Bunt bunt Washington National

Outfield hits are way too casual

 

Bunt bunt Washington National

You’re 5000 sac bunts in the wind

 

It’s still a work in progress, but I think it has hit power. Expect it to quickly replace ‘Take On Me’ as the song sung during after the seventh inning stretch when it’s complete and become Bryce Harper’s new walk up song, replacing the other 73 he currently uses. It’s just a matter of time.

Although now that I think about it, Bryce is probably more of a Johnny Karate guy.

So, uh, last night: WTF? moments from the Nats-Padres game

Despite a discouraging loss at the hands of the Friars of San Diego (more specifically, former Washington National and the only two-time Tommy John surgery survivor position player Xavier Nady), last night’s game was full of entertainment. Let’s have a GIF’ed up recap of some of the highlights.

First, the kid pulling out her tooth on LIVE TELEVISION:

http://instagram.com/p/nMP41FDOx3

Christ on a bike. Moving on…

Second, the rare pitcher-to-field switch, this one featuring a finely mulleted Andrew Cashner. Let’s have a look at Cashner’s outfield patrolling prowess!

cash

Morse-esque with the range and grace out there. Inspiring.

My research on this phenomenon wasn’t exhaustive, but this sort of thing happens every so often, most recently in 2009, when Sean Marshall went all 1-to-7 on us.

But it doesn’t end there! More LF shenanigans ahoy, courtesy of Tommy Medica!

medica

Fucking majestic. Ibañez-ian.

To be fair, Medica is a first baseman making the switch to the outfield, but for now, let’s just revel in the derpy glory.

Fingers out and pointed everyone…

 ***

Tooth pulling courtesy of Tom Block.

Screen grabs courtesy of yours truly via the MASN broadcast.

 

 

 

How Do I Link Dump, v5.0?

Has it been a month already since I last posted? Oof. Que terrible.

Well, if you’re so inclined, check out some of the things I have written for Parts Elsewhere on the Series of Tubes, yes?

 

I have written a little more about injuries for Beyond the Box Score as of late. In particular, ulnar collateral ligament tears.

First, I revisited the Medlen/Strasburg debate, looking at whether leverage might have played a role in Kris Medlen’s re-injury, while Stephen Strasburg continues to truck on, almost four years post-Tommy John surgery.

I then take a page out of my old lab notebooks and consider whether tobacco use might play a role in UCL re-tears and poorer outcomes, surgically.

Hot off the presses, I also take a look at the role of the triceps muscle in the throwing motion, through the lens of Scott Kazmir’s recent triceps tightness.

 

For Gammons Daily, it’s been all about pitching.

For the Athletics fan in your life, I wrote about the move of Jesse Chavez from the bullpen to the starting rotation and what he might do differently pitch-wise in the new role.

Maintaining the California Love, I then had a look at Tyler Skaggs’ Uncle Charlie. Lookin’ good…

 

Nationals baseball more your thing? My condolences I have just the thing for you!

For District Sports Page, I’ve covered a numbers of things:

– defensive shifts and their effect on Nats hitters? Got it.

– discussion of some troubling velocity declines for some pitchers? Order’s up!

– Ross Detwiler and some discussion of why he fell short for the fifth starter role (for now)? Enjoy.

– Rafael Soriano? Yes, I have that as well, much to your chagrin.

– Drew Storen and his troubling walk rate during spring training…WITH PRETTY PICTURES? Ayup.

– STRASBURG OUTRAGE AFTER ONE GAME? Embrace it.

– Man crushin’ on Anthony Rendon’s swing? Alright, alright.

 

Adding to the DSP work, I have been invited to guest blog for MASN, which I am very excited to be a part of.

I started with a comparison of Stephen Strasburg and Tyler Clippard, went from there with a discussion of some quirky stats related to the aggressiveness of Nats hitters early in the season, and went with more velocity decline concerns, this time, with Taylor Jordan.

 

Lots of words. Lots to discuss. I hope you enjoy them. If you don’t, I welcome your comments (constructive ones, at least) on how to make the words better-er.

How Do I Link Dump, v4.0?

I’ve been a busy little woodland creature who displays an affinity for aquatic environs as of late.

Along with my usual Beyond the Box Score writing duties, which recently included a piece on the 2014 prospects of Cody Ross after his relatively gruesome hip injury, I have joined a couple of other teams as a contributor in the last couple of weeks.

As of last week, I am a part of the District Sports Page team and will be providing weekly content revolving around the more statistical aspects of Natsdom. My first article can be found here and asks the question: should Danny Espinosa scrap switch hitting?

The bloggering doesn’t stop there!

Today marked my maiden journey as a contributor to Gammons Daily. Check out my first piece on Brian Wilson, if that’s your thing. My contributions there will be a little less frequent than at DSP, but I am nonetheless very happy to be on board.

…and because I made gifs of Wilson pre and post Tommy John surgery, highlighting some mechanical tweaks that didn’t make it to the piece, I provide them here, for S’s and G’s.

2014 Wilson:

wilson5

…and 2012 Wilson, during his last outing with the San Francisco Giants, before surgery:

wilsonSF_3

Notice the difference in arm slot and the slightly less closed lead leg in 2014 compared to 2012?

Anyhow, it goes without saying I am very excited to be a part of both of the new sites and I hope you enjoy the content I provide at both. As you can imagine, with my responsibilities at the aforementioned places as well as at Baseball Prospectus and Camden Depot, my posting here at HDIB? will be less frequent. I plan on using HDIB? as a landing-place for posts, ideas, and other such things that don’t quite fit the M.O. of these places.

Happy reading and basedballing, everyone.

Pace II, Electric Bugaloo: Pacing the Pacers

A few days back, I had a look at the statistic ‘pace’ and briefly discussed how pace affected the Washington Nationals pitching staff. Overall, we found that the Nats staff is fairly quick compared to the rest of MLB, with relievers being more apt to dawdle. We also discussed very briefly what, outside of a pitcher’s internal clock, could factor in to the pace stat — things like home run rate, holding runners on, and what have you.

Let’s return to this topic of pace and more specifically to the topic and its effects on the Nats’ big three starters — Gio Gonzalez, Stephen Strasburg, and Jordan Zimmermann. We have already seen some of their cursory pace data, but let’s now look at these three with a different lens; let’s now look at the role and effect of the catcher on pace as well as some of their other pitching stats.

First, some brief materials and methods discussion. Again using FanGraphs data, I grabbed game log data for all three for 2013, including pace, plate discipline data (think contact and swing rates), as well as some other standard data (things like pitch counts, home runs, walks, FIP, and xFIP stats). From there, I matched the games to each of the big three catchers for the Nats last season — Wilson Ramos, Kurt Suzuki, and Jhonatan Solano, with the idea of doing some statistical voodoo and breaking down pitcher stats (pace, xFIP, et cetera) by catcher to see if there were any significant differences in how quickly or productively each of the Big Three pitched, depending on who was behind the dish.

One more caveat to our methods here; for each pairing, I used the catcher who started as the catcher for each pairing. While there are a few games where the starter was relieved mid-game and a pitcher’s pace could possibly be affected by this change, I made the leap that the potential for this is negligible. Also, for the most part, catcher swapping was done later in the game, by the time our Big Three were pulled from the game; therefore, pitcher data should be consistent across catcher. While I could do my due diligence and break this all down by inning, the amount of manual manipulation of the data to do that is too much of a time suck, so here we are.

Good with that? A reasonable leap of faith taken? Moving along…

So. Our catchers. We have three, and here are their vital stats for this data set:

Ramos: 44 games caught

Solano: 4 games caught

Suzuki: 46 games caught

One quirk here as well — Solano only caught Strasburg, while Suzuki and Ramos both caught all of the Big Three. More on this later.

Now, some data, in the form of pretty charts!

First pace and pitches per inning for the Big Three; for the moment, I am filtering out Solano data.

Pitches Inning By Catcher Pace Inning By Catcher

Not too much here; overall, pace is pace for our Big Three, regardless of the catcher, with Suzuki getting pitchers to work a hair faster and hair more economically. I will save you the statistical gymnastics and tables, but there were no statistically significant differences in any of the pitching stats I grabbed across catcher.

The stats I looked at were:
O-Swing%
Z-Swing%
Swing%
O-Contact%
Z-Contact%
Contact%
Zone%
Pace
W
L
IP
Batters Faced
H
HR
BB
SO
BABIP
GB%
FIP
xFIP
Balls
Strikes
Pitches
Pitches/Inn
Pace/Inn
Strike%
xFIP – FIP

So with that aside out of the way, let’s keep looking data; here we look at the difference between xFIP and FIP (xFIP minus FIP) between catchers as well as strike rate:

xFIP-FIP By Catcher Strike% By Catcher

With the difference between xFIP and FIP, it was my thought it could possibly portend to some sort of measure for catcher-pitcher dynamic, given that in general, both should trend together tightly for a pitcher, since the only huge difference between the two is how they handle home runs in their calculation. With that in mind, more positive numbers are desired, since it means that the pitcher-catcher combo did better than expected, at least with FIP as our yardstick. Does it really mean anything? Probably not; however, it is interesting to see that all three starters did worse than expected, on average, with Suzuki catching and that Strasburg’s Ramos outings were a full run worse. Again, there might be something here, but I am doubtful. Moving on to strike rate, we again see no real grand deviations with differences in catcher considered. Not shocking.

Now, let’s take a look at the Solano data; let’s also remember that this is based on FOUR GAMES, so we really can’t say much about it, but we can at least admire the differences seen with Solano’s signal calling:

Pace, Strasburg  xFIP - FIP, StrasburgStrike%, Strasburg

Kind of quirky. With Solano catching, Strasburg improves across the board with the pitching stats of interest; again, it’s only four games, and there are a ton of variables and effects that we are not taking into account with the data as presented, but it is an interesting trend we see. Two things that are beaten into a pitcher’s head — work fast and throw strikes — that are purported to be the secret to success are seen with Solano, for Strasburg, in spades. Is it an effect of Solano and possibly some intangible rapport the pair has? Maybe. Could it be simple coincidence? Yep, could be that too. But it will be an interesting trend to keep an eye on in 2014.

Could all of this be luck?

BABIP By Catcher

It could be that too; these BABIP rates by catcher are interesting just as an aside, not so much as a predictor of success (BABIP stinks for that), but how different the rates are across catcher and it speaking to there possibly being an effect of pitch selection amongst each of the pairings. Another study for another day, I suppose.

While there are many aspects of the fulfilling the duties of a catcher that haven’t been considered here, by the looks of it, the Nats are fortunate to have a pair of catchers in place for 2014 — Ramos and Solano — who won’t be poisonous to the overall productivity of their Big Three starters and might even be positive influences on their staff.

Pacing Yourself

One of the many tangibly intangible things that a pitcher can do to endear himself to umpires, his defense, and their adoring TV viewer public is to work fast. Pitchers also benefit themselves by working fast, as one of the basic tenets of successful pitching is to interrupt a hitters timing — by controlling the pace of an at bat, the greater chance a hitters innate cadence and timing for each pitch can be oh so slightly interrupted.

Even with some of these tiny advantages to working fast, some pitchers just…

feel…

the…

need…

to…

take…

it…

sssssssssllllllllllllllloooooooooowwwwwwwww.

One of the many side stories of this year’s World Series that exemplifies this was Clay Buchholz‘ pace to pitch. Glacier-like was the overarching sentiment as to how quickly a Buchholz outing went; given the age-old complaint that any Boston Red Sox game takes longer than most, this wasn’t a new revelation.

So was it all in our heads? And how do Washington Nationals pitchers do in this arena of pitching?

FanGraphs can help us out with this — with the pace statistic.

Pace tells us, on average, how many seconds a pitcher take in between pitches of an at bat. For 2013, the average time between pitches was 22.9 seconds, when you include all players who made an appearance as a pitcher (*waves at Skip Schumaker and John McDonald*).

Again looking at all pitchers, starter or reliever, but using an innings pitched cutoff of 10 innings and we find that the fastest pitcher was New York Yankee Vidal Nuno, at a 17.2 second pace; on the other side of the coin, Tampa Bay Rays reliever Joel Peralta was the slowest, at 31.9 seconds.

However, this doesn’t tell the whole story; while time between pitches is obviously a hindrance to a quick game, so are the number of pitches thrown. More pitches thrown, the more time in between pitches, the longer the inning is — simple math. So with that in mind, let’s pull out our abacuses, TI-82 calculators, and perhaps some scratch paper, and figure out who are the yin and yang of pitching pace.

So, we have pace, innings pitched, and total pitches, courtesy of FanGraphs. From there, we simply need to calculate pitches per inning, then multiply by pace, then divide by 60 to get a rough estimate of how long an inning is for a given pitcher, in minutes.

Doing all of this voodoo brings us to this:

Screen shot 2013-11-01 at 1.55.49 PM
*minimum of 10 IP

Cool? Kinda, I guess; to circle back to the Buchholz reference and close the book on that, he comes in at a 24.2 second pace, 15.04 pitches per inning, which gives him an average inning pitched in 6.09 minutes. This ranks him 54th among 146 pitchers with at least 100 innings pitched and fourth out of the five Red Sox starters with at least 100 IP. Yes, Clay is a quick worker, compared to his carmine brethren.

Enough of that, let’s talk WASHINGTON NATIONALS BASEBALL. Remember that? Barely, I know. Sarcasm aside, let’s have a look at how the Nats staff pans out with this whole pace thing. A priori, we are led to believe that Jordan Zimmermann is a very fast worker and Stephen Strasburg kind of plods along.

Any truth to this?

Screen shot 2013-11-01 at 1.55.37 PM

Well, yes and no; ZNN appears to be a quick worker, for sure — not too far behind the pace setter (pun intended) Nuno at 4.79 minutes per inning, but Strasburg isn’t too far behind him. So we have a wee bit of a misconception of Strasburg’s pace, even when pitch count is factored into a pitcher’s overall pace. We also find Nathan Karns conspicuously absent from our list; for whatever reason, Karns and two other players’ paces were not listed by FanGraphs.

Yunesky Maya? Yeah. Moving on…

The bottom part of the list — let’s lovingly dub them DAWDLERS — are primarily relievers. While we know that Tyler Clippard enjoys long walks around the mound, blowing into his pitching hand, licking his hand, and doing a number of sundry things on his pre-pitch checklist before dealing, we also see another quirk about the pace stat and an underlying component of it, courtesy of hitters, with the dawdlers — home runs.

Yes, the more home runs you give up, the more time you have to wait for the hitter to get around the bases before you can throw your next pitch. While I will save the rigorous statistical analyses for the effect of home run rate on pace for another day — and as we can see in the above table, it isn’t something that appears to be highly correlated — it is an unfortunate aspect of pitcher pace.

Keeping with eyeballing trends, we also see with the Nats that relievers are a tick slower than starters, in general. While homers do play a role, the fact that relievers are in-game during typically higher leverage situations — a fancy way to say there are men on base with the game on the line — the need to change your timing to the plate and even throw over to first base on occasion to keep a runner close takes precedence over keeping your pace number low. Strategery at its finest.

So again doing some quick eyeballing, the Nats average a 6.03 minute inning; countering Cubans outliers and using the median, Nats pitchers come in at about 5.7 minutes per inning. Comparing that to the MLB overall (and again using a 10 IP cutoff) and an average 6.3 minute inning or 6.2 minute median inning, and we see that Nats hurlers are a tad quicker than the average team.

Here’s how the entire MLB pans out; note that this chart does not include data from players who swapped teams during the season and are thus noted as playing for ‘—‘ by FanGraphs.

Pace Per Inning By Team

So there you have it; the Angels are DAWDLERS on average, while the Braves and Cardinals, bastions of all that is unwritten and unheralded, are the quickest. Snark aside, both of those teams more than likely have an organizational mantra that predisposes guys to work fast, and if you look at their team pitching stats, it’s definitely not hurting their stock.

Pace; it’s not just salsa.

Courtesy of curlyw.mlblogs.com

Courtesy of curlyw.mlblogs.com

How Do I Link Dump, v2.0?

It’s been awhile since I’ve last posted here and while I would love to tell you I have spent the time away from HDIB? analyzing the L.O.M.B.O. data and results in preparation for a manuscript that will be submitted to the International Journal of Sport Grit and Want and Desire and Other Things No One Can Truly Measure But Dammit We Try — the IJSGWDOTNOCTMBDWT for short — alas, I have not.

I have however, been busy writing about the Shutdown. Put away the pitchforks and take a look at what Stephen Strasburg and Jordan Zimmermann have done before and after Tommy John surgery and having their innings limited post operatively across a number of stats and categories:

Part One — pitch usage, velocity, and movement

Part Two — pitch sequencing and command

For the Orioles fan in your life, I have lots of prose dedicated to Manny Machado and his MPFL injury and his prospects for a healthy return and what that means for the Orioles in 2014:

Injury discussion, part one

Injury discussion, part two

Looking to 2014, part of a series over at Camden Depot breaking down 2013 and 2014 by position.

I will be helping out Camden Depot with a couple more of these year in review write ups, focusing more on the O’s bullpen; you can also find more of my nerd math writings over at Beyond the Box Score, if you enjoyed the Strasburg/Zimmermann/TJ bits discussed.

Anyone know what IJSGWDOTNOCTMBDWT’s impact factor is? Might have to shop L.O.M.B.O. around…

Happy Feet – A Look at Bryce Harper’s Last Second Adjustment

While last night’s Washington Nationals game against the St. Louis Cardinals will be remembered for the almost no-hitter for rookie starter Michael Wacha (and rightfully so), there was an interesting side story in the 7th inning, courtesy of Bryce Harper.
 
Jimmy, roll the tape:

 
(Click to unleash the gif)
harper_wacha
 
Did you see it? Did you notice the last second hop/skip/jump in the box towards Wacha before he swung at a 96 MPH fastball?
 
Here’s a different angle of the swing, which really gets the point across:
 
(Click to unleash the gif)
harper_wacha2
 
First of all — wow. In a game determined by milliseconds, Harper takes something already tough to do and laughs in the face of it, making an adjustment as Wacha is throwing a pitch. A 96 MPH FASTBALL.
 
Second — why? My thought is that Harper was guessing changeup — Wacha’s biggest secondary pitch, and one that was especially good last night in his 8.2 innings of one-hit ball. His 32 changeups last night came in at a 62.5% strike rate and a 15.6% whiff rate. When you also consider that Wacha threw just three curveballs and one cutter, you get a better appreciation of Harper’s mindset in this at bat. Already down in the count 1-2 and knowing he wasn’t going to get anything breaking, he assumed he wouldn’t get something hard, so he set up in his usual fashion in the batter’s box, then scooted up to get to that assumed changeup before it darted out of the zone.
 
Here’s a plot of the pitches he saw in the at-bat; as you can see, Harper was one step ahead of Wacha as far as getting that changeup, eventually striking out on the pitch:
 

 
Pretty darn impressive. To not only be able to move the feet, keep your swing mechanics intact while doing so, look for and react to a changeup, but then get a high-90’s fastball, and still be able to catch up to it enough to just foul it off is all sorts of amazing and impressive. But you know what? It’s been done before.
 
Remember Hal Morris?
 
halmorris
 
While his church league softball batting stance wasn’t as egregious as Harper’s, Morris also employed a foot shuffle before getting the bat through the zone. With a .304 batting average, relatively low 12.3% strikeout rate, and 14.6 fWAR over a 13 year career, Morris was surprisingly effective with the approach. Also to note is while Harper all but leaped towards Wacha with his swing, Morris’ was more of a shuffle up towards the plate, with a small step towards the pitcher; small difference, but an important one.
 
While the end result wasn’t terribly desired — Harper ended up striking out — it nonetheless was an interesting look at Harper’s approach and how he is able to not only make adjustments, but make them in real-time; a very rare feet feat.

Spanning Is the New Santangeloing: A Brief Review of Washington Hit Streaks

There have been many impressive offensive feats performed in the last few weeks by the Washington Nationals in the midst of their hot streak, which now has them four and a half game out of the last Wild Card spot, currently held by the Cincinnati Reds. Arguably the most impressive of said feats is the 24 (and counting) game hit streak by center fielder Denard Span, which is good for fourth in Washington Nationals/Montreal Expos history, right behind just-retired Vladimir Guerrero and current teammate Ryan Zimmerman, and has propelled the Nats to a 19-7 record during the streak.   Here’s how Span’s streak looks:
 

Rk Strk Start End Games AB R H 2B 3B HR RBI SO BB SB CS BA OBP SLG OPS Tm
1 Denard Span 2013-08-17 2013-09-13 24 101 16 38 5 2 2 7 12 6 2 1 .376 .413 .525 .938 WSN
Provided by Baseball-Reference.com: View Play Index Tool Used Generated 9/14/2013.

 
Not too shabby, eh?   Now, let’s take a walk down (recent) memory lane and take a look at how Span’s production — batting average (BA) and on base plus slugging percentage (OPS) — during his hit streak compares to similar Nats (as in Washington, not Montreal – sorry Canada!) hitting streaks. Moving forward, I am only considering hit streaks of 15 games or more, courtesy of Adam LaRoche, Cristian Guzman, Span, Ian Desmond, HDIB? great Nick Johnson, and Zimmerman:
 
WSN 15+ Game Hit Streaks By BA and OPS
 
Not surprisingly, Span’s batting average is reasonably high, with his OPS reasonably low compared to his fellow Nats streakers, which makes sense, given Span’s lack of power and so-so (for a top of the order hitter) on base percentage. Fair enough.  
 
Something’s missing.  
 
I seem to recall an 18 game hitting streak in there somewhere, in the annals of Washingreal history.  
 
Ahh, yes, F.P. Santangelo told me many many times over the course of Span’s hitting streak once he had an 18-game hitting streak.  
 
Ribbing aside, let’s take a look at Nats 15+ game hitting streaks — along with Santangelo’s 18-gamer — again by batting average and OPS:  
 
WSN 15+ Game Hit Streaks By BA and OPS (and FP)
 
OK, cool — we see some interesting trends here, namely, these guys are going out of their minds not only with their batting averages, but their overall power. Now, let’s break down OPS into its constituent parts — on base percentage (OBP) and slugging percentage (SLG) and add that to AVG and OPS and then look at these streaks in comparison to each player’s career averages for these four stats, yes?  
 
Difference Between Hit Streak and Career AVG, OBP, SLG, and OPS
 
With this, we see that while Santangelo’s hitting streak was impressive, it is definitely the outlier in comparison to the other streaks; his streak production was in such great contrast to what he normally accomplished hitting-wise, even when compared to his fellow streakers. Conversely, Span’s hitting streak, as well as Johnson’s, more closely trend with their career averages.  
 
What does this mean? Probably nothing; while it would be easy to say that the differences between streak averages and career average is some reflection of each player’s inherent hitting talent, that is a bit of a slippery slope and something that the data as presented can’t really speak to. Variables such as opponent defense and even pitching match ups all cloud the data enough to not warrant too many brash statements made about the data here. What is interesting are Zimmerman’s streaks and how he went about each — while some were driven more by his ability to make contact and not much else, others were marked by his ability to generate runs with his swings.  
 
Taking one more step back historically, how does the Washingreal data compare to other teams?  
 
Let me tell you, with the help of Baseball Reference’s Play Index. Looking at the modern era — 1916 to current day — I provide below the number of 15 game or more hitting streaks for each organization. I then averaged them over the years of interest to give an idea how frequently over the franchise’s modern era a big hitting streak occurs:
 

Team Yrs 15+ H Streaks Strk/Yr
STL 93 170 1.83
BOS 94 170 1.81
DET 96 171 1.78
CLE 94 166 1.77
MIN 97 162 1.67
NYY 95 155 1.63
COL 20 31 1.55
TOR 36 54 1.5
BAL 96 142 1.48
SEA 35 49 1.4
TEX 52 72 1.38
LAD 97 132 1.36
MIA 20 27 1.35
SFG 96 127 1.32
CHC 96 125 1.3
OAK 97 125 1.29
CWS 97 123 1.27
CIN 97 121 1.25
LAA 52 63 1.21
ARI 15 18 1.2
KCR 43 51 1.19
ATL 93 109 1.17
MIL 44 51 1.16
PHI 97 111 1.14
WAS 42 42 1
HOU 51 50 0.98
SDP 44 43 0.98
NYM 51 42 0.82
TBR 15 8 0.53

 
Not that Span’s streak wasn’t impressive enough, but the data provided, especially the table above, confirms how special the streak is to the organization; these kinds of streaks, while seen more frequently in the last few years, thanks to Zimmerman, haven’t been a hallmark of Washingreal hitters, to say the least. Between that and the context of Span’s streak — in the middle of a wild card run in the waning days of the season — only adds to the enjoyment of the streak and its importance to the success of the Nats’ 2013 season.

Wasting Away: Adam LaRoche’s Slimming Offense

The 2013 season has been a lost one for the most part for Washington Nationals first baseman Adam LaRoche. Lost, not only from a production perspective, but also when it comes to his weight, as it was brought to light that his medication regimen for his attention deficit disorder has caused a tremendous amount of weight loss in the last month or so, causing him to cut back on his pregame activities in order to save energy.

With the alterations in medication and batting practice routine seems to have come a bit of a power outage for LaRoche — since August 1, ALR has hit four home runs in 130 plate appearances and in general, has produced scant power. In spite of the drop in homer production, LaRoche still appears to be relatively productive — does the sudden weight drop drastically affect LaRoche’s numbers this year, or compared to his previous years averages?

With an assist from Fangraphs, we can find that out. First, two tables. The first looks at ALR’s numbers in August in three different ways — across his career, his career best 2012 season, and this season — the second is the same thing, just for September and October. I picked these due to them roughly paralleling the length of time LaRoche has felt that he was losing weight and possibly energy due to his meds.

Is 2013 such a huge anomaly for LaRoche?
 

Season Split BB% K% LD% AVG OPS ISO BABIP wRC+
2012 Aug 8.7 % 14.3 % 18.8 % 0.239 0.691 0.150 0.247 87
2013 Aug 12.5 % 20.2 % 27.5 % 0.233 0.737 0.167 0.262 96
Career Aug 9.9 % 21.1 % 19.1 % 0.294 0.891 0.232 0.332 132

 

Season Split BB% K% LD% AVG OPS ISO BABIP wRC+
2012 Sept/Oct 9.8 % 18.7 % 22.7 % 0.324 1.057 0.342 0.333 182
2013 Sept/Oct 23.1 % 15.4 % 25.0 % 0.316 0.883 0.105 0.375 153
Career Sept/Oct 8.4 % 24.3 % 23.2 % 0.293 0.888 0.243 0.350 128

 
By the looks of it, not really — in fact, ALR appears to be righting the ship, if the handful of Sept/Oct at bats for 2013 are anything to hang your hat on. While the sample size is limited, his 2013 walk rate (BB%), strikeout rate (K%), and batting average on balls in play (BABIP) betters his career and 2012 averages, to name a few. Looking back at the August numbers, while he did definitely a drop in numbers due to the weight loss, it wasn’t anything precipitous.

Looking at the raw data, it nothing really jumps out at you — yes, LaRoche is having a rough stretch, but nothing that hasn’t been seen previously in career. Let’s now look at his productivity with some help from PITCHf/x and Brooks Baseball.

With this first graph, we look at ALR’s plate approach across his career in the hopes of answering whether his recent weight woes has made him change his approach at the plate:
 

 
In general, 2013 has seen LaRoche be a little more even keeled with his approach, but as of late, has shown some passiveness, especially with breaking and offspeed pitches. Is this in response to some sort of innate admission that he doesn’t have the energy/power to get to a particular pitch or a part of the strikezone to drive the ball? Or, perhaps it’s a reflection of pitchers pitching him differently, trying to take advantage of a weakness in his approach or swing.

The next two charts can help answer some of this. The first is a look at the percentages of pitches ALR has seen over his ‘career’ by month. I write ‘career’ simply because it only includes seasons where PITCHf/x data were available. With that disclosed, how are pitchers working LaRoche?
 

 
There aren’t too many huge trends popping up across LaRoche’s career. However for this season, we do see a good jump in the number of hard pitches — fastballs (twoseam, fourseam, and cut) and sinkers — along with a concomitant drop in breaking pitches in August and September versus earlier months.

How has LaRoche responded? This next chart shows if he is missing pitches via whiff rate:
 

 
While he seems to be handling hard stuff just fine and making good contact (low whiff rate), LaRoche does seem to be swinging and missing quite a bit as of late on the increased number of offspeed pitches sees. With the weight loss, he also seems to be whiffing a little more as of late on breaking pitches, but overall, does seem to be doing a decent job of handling the offspeed pitch in 2013. In general, LaRoche does appear to do a better job of connecting with pitches of any type as he ages.

OK, so he’s getting his cuts and save for some rough going with offspeed offerings, is putting the ball in play. Where’s the ball going, now that he’s had to ration his energy and swings?

First, I provide ALR’s spray chart for 2013 up until roughly when he noticed the weight loss — right around August 5th:
 

 
…and here’s what he’s done since then, a waist size or two smaller:
 

 
It appears he isn’t hitting the ball quite as far as of late, and seems to not be going the other way quite as frequently as he was before the weight loss. Also, it doesn’t appear that he is putting a good swing on breaking pitches in recent weeks and isn’t driving the ball as far as he once was.

Two more hitting charts. These look at ALR’s isolated power, pre- and post- weight loss, as it relates to his zone profile:
 

 

 
Here, we see that LaRoche’s ‘sweet spot’ for generating runs has shrunk as he has — in the last few weeks, if it isn’t in the traditional left-handed hitter’s hit zone of down and in or right down the middle of the plate, he isn’t able to drive the ball and generate some of the much-needed runs that the Nats offense in general has been lacking all season long.

While LaRoche does seem to be pulling out of his slump, what we see is that, while concerning due to where the power outage arose from, a late season drop in productivity isn’t anything new to ALR historically. Beyond that, we see that LaRoche has struggled to maintain his typical power numbers in spite of doing an admirable job of maintaining his approach and not becoming overly aggressive with his swing. Despite the lack of power, LaRoche is still making good, albeit weaker, contact.

With a medication change or tapering of his current dosage, LaRoche should be able to finish the season strong and hopefully give the Nats offense the lift it needs as it continues to knock on the door of a playoff berth.