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.

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:

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:

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?

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.