Tagged: pace

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:
Batters Faced

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…








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