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:
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.
There are few relationships in baseball more steeped in the concepts of symbiosis and reciprocity than the one between a pitcher and catcher. Most of the limelight and publicity of the relationship is aimed primarily at the pitcher and what the catcher can do to benefit the hurler, and for tacitly obvious reasons. For one, the game’s flow and cadence is set by the pitcher – no pitch, no game; the eyes of the viewerdom are locked in and at the mercy of the shake of the pitcher’s head in agreement with his catcher, allowing the start of his delivery. Then there is the general persona of the catcher – behind the mask and the ‘tools of ignorance’, there is typically a player who is more in tune with and proud of his less tangible accomplishments on the field more so than his offensive numbers.
It’s those offensive numbers that I look to dissect a little further and shed a little more light upon; in a relationship that is biased towards the pitcher, I would like to set things in reverse and take a look at how the pitcher possibly helps the offensive fortunes of his catcher. In a time where pitch framing skills, game calling abilities, and catcher’s ERA are used (in varying degrees of frequency) to describe the efforts a catcher takes to enhance his pitcher’s productivity, little time is taken to look at the reverse situation – what does a pitcher do to help out his catcher’s productivity?
While I confess that the answer is much more complicated than the efforts I am about to lay out for you here, I at least hope to set the table for a more harmonious and reciprocating conduit of discussion on the efforts that the pitcher-catcher battery provide one another.
So what am I doing here?
With the Washington Nationals as my statistical muse, I did some number tweaking, to see if there was some sort of effect a pitcher had on the catcher’s offensive output. The idea arose out of a habit that many Nats hurlers have – doing a poor job of holding runners on. While having runners on base is always a precarious situation to find yourself in as the defensive team, the Nats power arms and their very deliberate deliveries exacerbate the issue for the likes of Wilson Ramos and Kurt Suzuki.
So the question is posed – with this additional burden of having to control the running game without much help from the pitcher on top of the usual catcher duties, does a catcher’s hitting suffer?
Before I jump into numbers, let’s get some methods to the madness clarified.
I looked at Washington’s two primary catchers – Ramos and Suzuki – using Baseball Reference for some offensive and catching stats. Because of a small sample size, I chose to leave out the third member of the catching corps for the Nats – Jhonatan Solano. I also did a couple of other things in the name of simplicity – I threw out any games that saw both players get innings and I also looked at each catcher’s offensive numbers for a game as a whole, meaning that I made the assumption that the effect of a starter’s innings had more weight on what his final boxscore would look like than a relievers. In short, I made the leap of faith to say the catcher’s day at the plate, good or bad, is more affected by the starting pitcher more so than the reliever(s). A big leap of faith? Yes. But given this cursory look at the relationship, one I’m comfortable in making, just to get the ball rolling. Last, for stolen base values, I tallied up only those that were on the starting pitcher. So for a game like the one Suzuki had May 19, one which saw him give up five stolen bases, but only two while starter Dan Haren was on the mound, I gave him ‘credit’ for two stolen bases. More on that in a bit.
Thoroughly confused by my caveats and methodology? I thought so; let’s go look at some numbers.
The tables I present show some offensive stats as well as stolen base numbers, broken down for each catcher by starting pitcher. I also include each player’s season averages as a comparison. Each table is sorted by OPS.
Here’s what it looks like for Ramos:
|W – L||BA||OBP||SLG||OPS||SB||CS||CS%|
|Haren||2 – 4||0.048||0.091||0.048||0.139||3||0||0.0%|
|Strasburg||3 – 3||0.222||0.300||0.278||0.578||4||1||20.0%|
|Zimmermann||1 – 0||0.333||0.500||0.333||0.833||0||0||0.0%|
|Gonzalez||4 – 1||0.474||0.474||0.737||1.211||0||1||100.0%|
|Jordan||2 – 0||0.444||0.444||0.778||1.222||3||0||0.0%|
|Detwiler||1 – 0||0.500||0.600||2.000||2.600||0||0||0.0%|
…and what it looks like for Suzuki:
|W – L||BA||OBP||SLG||OPS||SB||CS||CS%|
|Zimmermann||14 – 4||0.220||0.303||0.186||0.489||8||3||27.3%|
|Haren||1 – 9||0.194||0.256||0.306||0.562||4||0||0.0%|
|Detwiler||4 – 6||0.194||0.200||0.389||0.589||2||0||0.0%|
|Gonzalez||8 – 6||0.220||0.278||0.320||0.598||5||2||28.6%|
|Strasburg||3 – 5||0.241||0.267||0.414||0.680||6||1||14.3%|
|Jordan||0 – 2||0.500||0.625||0.667||1.292||0||0||0.0%|
|Karns||1 – 0||0.750||0.750||0.750||1.500||1||0||0.0%|
So what do we see? For both catchers, we see an interesting association with OPS and stolen bases – as swiped bags go up, down goes catcher OPS, our surrogate for offensive productivity in this exercise. Dan Haren outings seem to have a particular effect on both Ramos’ and Suzuki’s offensive numbers, while in a small number of starts, Jordan appears to do wonders for the bats of his backstops.
Looking at the stolen base numbers is a bit more tricky, simply due to sample sizes. However, we can grasp that with Strasburg on the mound, there appears to be more would-be basestealers, with the same situation being seen in Haren starts; however, Haren’s ability to help out his catcher by keeping runners close either by using an abbreviated delivery out of the stretch or by other means isn’t terribly successful (0% success rate for both catcher against base stealers). On the flip side, lefty Gio Gonzalez appears to do a respectable job of keeping runners honest, as most lefthanders tend to do.
Let’s take a quick look again at the relationship between OPS and SBs with the help of linear regression. If we do a quick regression of OPS against stolen bases, we get some interesting results. For Ramos, we get an R², or correlation of determination, of 0.34; for Suzuki, his R² comes out to be 0.58. While both are reasonably strong results, pointing to the potential that the running game has an effect on a catcher’s offensive game, Suzuki’s R² is surprisingly high. Yet, with such a small sampling of data, there is still a lot of possible noise in what we think might be signal. Even if we merge both player’s data, we get an R² of 0.36; nothing terrible, nothing great.
While these results are impugnable at best due to the way I assigned catcher at bats to a starting pitcher (who may or may not have been in the game when a particular at bat was taken), they do expose a couple of interesting points. One, Suzuki seems to be a little more affected offensively by the running game than Ramos; while neither has done an exceptional job of throwing out runners this season, by the looks of our data, Ramos seems to do a better job of not letting the defensive aspects of the game affect his offensive production. Also, each pitcher’s particular quirks have the potential to affect each catcher differently. Take, for example, Jordan Zimmerman. While known to be a quick worker and able to keep the running game at bay reasonably well by changing his looks to a runner and altering his cadence for each pitch, he appears to be downright poisonous to Suzuki’s hitting, as his .489 OPS clearly asserts. It is these idiosyncrasies that, while on the surface seems extraneous and negligible, go a long way towards defining the ultimate success of a particular tandem.
While the offensive contributions that a catcher brings to his team will always remain in the realm of ‘nice to have, but not at the cost of defense’, it is nonetheless an addition that can be accentuated by the efforts of his pitcher, especially with respect to the running game. This is by no means an exhaustive or painfully concise analysis of the relationship between an catcher’s offense while paired with a particular starter, but it is hopefully a reasonable start towards a better understanding the peculiar relationship between the pitcher’s arm and his catcher’s bat.