Pitches Seen: Baseball’s Boring Inefficiency

I think I might be the biggest fan of the world of the Ten-Pitch Walk.  I don’t know why, but I get overly excited when I see a player really battle for a long time, against everything the pitcher has, only to win the battle through patience.  Perhaps it’s because it’s so contrary to the spirit of what’s actually exciting about baseball; seeing players run around and field a batted ball.  It’s wholly a battle of attrition.  It’s the baseball equivalent of watching somebody run a marathon; you may not think the act itself is exciting, but it’s certainly an impressive feat in a vacuum.

So this has also lead to a fascination with pitches seen per plate appearance.  I’ve long wondered if certain teams place an emphasis on teaching their players to see more pitches per plate appearance.  It seems fairly self-evident that seeing more pitches is, in a microcosm, better than seeing fewer pitches.  You tire the pitcher out quicker, you see more data for your next at-bat to work with, and you give your team a chance to see what the pitcher has, and how he’ll react in different situations.  I hypothesized, purely based on colloquial wisdom, that the A’s would be good at this and the Blue Jays would be bad at this.  That’s not to say that one approach is better than the other, but just that some teams seem more patient than others.

Fortunately, FanGraphs has data available per hitter as to how many pitches they see.  I pulled that data out and found out each player’s average pitch per at bat since the year 2003 (the earliest we have this data, from what I can tell) and restricted the findings to active players only.  Then I ran some regressions to see if there was any correlation between pitches per at bat and useful batting stats.  Here’s what I found:

We see a slightly positive correlation between P/PA and wOBA.  It’s not really anything to write home about, but it’s more than negative.  It doesn’t seem immediately that seeing more pitches relates heavily to overall performance at the plate.  What about on base percentage?

Slightly better here, but still not great.  Seeing more pitches does have a little more correlation to getting on base, but there are plenty of aggressive swingers that don’t follow that model, so it means the correlation is loose at best.  What if we talk just about taking walks?

Here we have a real correlation.  .59 is a fairly strong correlation, and that makes sense.  The more pitches you see, the more likely you are to take a walk.  If you can successfully foul off anything in the strike zone, you will eventually walk (or the pitcher will die of exhaustion, either way, you win).  This is reasonably useful.  If you’re trying to find a way to make your team walk more, maybe you can invest in some players that see more pitches per plate appearance than normal.  This strong of a correlation makes me think about strikeout percentage too, though, because every pitch you foul off makes you closer (or just one whiff away) from striking out.

There is a positive correlation here, but not nearly as strong as between BB% and P/PA.  It’s stronger than the other useful stats like wOBA, but it’s interesting to know that seeing more pitches relates much more strongly to taking a walk than it is to striking out, at least on a grand scale.  There is some research to be done here to see what the odds are of a plate appearance as the pitch count increases, but I’ll leave that for another day.  My next thought was to see if there are, in fact, any teams that are better at this than other teams.  Here’s what we’ve got on a team level:

1 Red Sox 4.0506764011
2 Twins 4.0396551724
3 Cubs 3.9222196952
4 Yankees 3.9142662735
5 Pirates 3.9037861915
6 Astros 3.9028792437
7 Padres 3.9021177686
8 Mets 3.9009743938
9 Marlins 3.8916836619
10 Indians 3.8914762742
11 Athletics 3.8899398108
12 Phillies 3.8839715662
13 Blue Jays 3.8685393258
14 Cardinals 3.8634547591
15 Rays 3.8511224058
16 Rangers 3.8489497286
17 Dodgers 3.8480325645
18 Tigers 3.8314217702
19 Angels 3.8280856423
20 Diamondbacks 3.8161904762
21 Nationals 3.8146927243
22 White Sox 3.811023622
23 Giants 3.8038379531
24 Reds 3.8015854512
25 Orioles 3.8014611087
26 Braves 3.7944609751
27 Mariners 3.7358235824
28 Royals 3.7310519063
29 Rockies 3.7244254169
30 Brewers 3.6745739291

Well, my original hypotheses were not great ones.  The A’s and the Blue Jays, at 11 and 13, are both decidedly middle of the road teams.  I find it most fun in times like this to look at the extremes; in this case, the Red Sox and the Brewers.  The difference in pitches seen per plate appearance between these two teams is 0.38.  That may seem small, but it adds up.  If we assume the average pitcher faces 4 batters per inning, that’s an additional 1.5 pitches per inning, and 9 pitches by the end of the sixth, just purely by the nature of the hitters.  In a tightly contested contest, that may mean the difference between getting to the bullpen in the 7th rather than the 8th, or even the 7th rather than the 6th.

It should be noted that I limited this data set to 2014 (in contrast to the earlier data which was 2003 onwards) just so we could get a realistic look at roster construction, and to see if any teams are, right now, putting any particular emphasis in this area. The BoSox are carried by the very patient eye of Mike Napoli (4.51 P/PA), but hurt by the rather hacky eye of AJ Pierzynski (3.42 P/PA). Even on one team, that’s more than a pitch per plate appearance, which is pretty startling. The Brewers don’t have nearly the same difference; their best is Mark Reynolds with 4.04 P/PA and their worst is Jean Segura with 3.42 P/PA. As an aside, Chone Figgins is by far the best in this with a whopping 4.99 P/PA, though it was in just 76 PA. Kevin Frandsen brings up the rear with 3.16 P/PA in 189 PA. A lineup of all Mike Napoli‘s would see 24.3 more pitches than a lineup of Kevin Frandsens before the leadoff Napoli even comes up a third time. I would feel bad for that pitcher.

The talk about teams possibly emphasizing this data made me wonder if I could make a huge difference if I compiled a team solely to do this; just make sure the pitchers throw a ton of pitches.  With that, I present to you the 2014 All-Stars and Not-So-All-Stars in this area, with a PA minimum thrown in to eliminate Figgins-like outliers:

All-Stars P/PA wOBA
C A.J. Ellis 4.344444444 0.311
1B Mike Napoli 4.353585112 0.371
2B Matt Carpenter 4.20647526 0.362
3B Mark Reynolds 4.179741578 0.341
SS Nick Punto 4.033495408 0.293
LF Brett Gardner 4.305959302 0.332
CF Mike Trout 4.219285365 0.404
RF Jayson Werth 4.399714635 0.364
DH Carlos Santana 4.297962322 0.356

 

Not-So-All-Stars P/PA wOBA
C A.J. Pierzynski 3.33404535 0.32
1B Yonder Alonso 3.603264727 0.318
2B Jose Altuve 3.266379723 0.321
3B Kevin Frandsen 3.41781874 0.296
SS Erick Aybar 3.415445741 0.308
LF Delmon Young 3.450895017 0.321
CF Carlos Gomez 3.517879162 0.321
RF Ben Revere 3.544046983 0.296
DH Salvador Perez 3.366071429 0.331

Despite the fact that there isn’t a strong correlation between wOBA and P/PA directly, it’s worth noting that the P/PA All-Stars are significantly better than the Not-So-All-Stars. Their difference in wOBA is .328 as compared to .314. The Not-So-All-Stars certainly present a fine lineup though; the All-Stars just have the benefit of having Mike Trout in their lineup. It’s nice to know that this is one other area that Mike Trout simply is amazing at, confirming the obvious. The All-Stars have a collective P/PA of 4.26, while their counterparts sit down at 3.43. That’s .83 pitches per plate appearance, which over the course of two turns through the lineup is 14.94 pitches; that’s definitely something notable.

So, it appears this is a demonstrable skill with some value, though not a ton. We can see that some teams are better at this than others, and we see some positive benefit from this, most notably in walk rate. While we see plenty of players on both sides of the scale who are excellent ballplayers, the data does seem to suggest that seeing more pitches is better than not doing so, though only marginally on a league wide scale. When we isolate leaders in this area vs. those more aggressive, we can see some startling differences though, suggesting that perhaps there is an advantage to be gained here.




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A's fan and QA guy. I write here and there at https://medium.com/@Zebedee18 about the intersection between Baseball and Christianity.


10 Responses to “Pitches Seen: Baseball’s Boring Inefficiency”

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  1. Steve says:

    Good stuff, Andrew.

    I find myself obsessing with pitch count and pitches seen, too. I imagine a 12-pitch AB in the sixth, bringing a SPs count from 70 to 82 having a huge effect on the game. I love to see someone slap away 3 tough sliders with 2 strikes, and follow it by ripping the next fastball he sees.

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  2. Greg says:

    Interesting to see Jose Altuve near the top of the “Not so Stars”. Obviously he knows when to swing since he is killing it this year but maybe what’s good for Jose is bad for the woeful Astros who probably need to be hitting against bullpens more often? I’m agreeing with Steve also. What do you get when you foul off a pitcher’s best pi

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  3. Greg says:

    pitch three times? You get something less than his best pitch!

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  4. DavidKB says:

    I totally agree on ten pitch walks. I was at a Giants game a couple years ago when Andres Torres went down 0-2, then battled back to earn a ten or twelve pitch walk (to lead off the inning as I recall). I cheered loudly, then looked around to see that no one else was very impressed. Oh well, I guess it’s not for everyone :)

    As for the substance of the article, it’s got me thinking about some different ways to look at pitch count. You may just inspire me to my first stab at content creation.. we shall see.

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  5. Matt says:

    I just want to ask because I didn’t see anything about it but when you commented about BB% correlating highly with P/PA did you adjust for minimum pitches etc. Absolutely not saying this in a negative way but obviously P/PA will correlate highly with walks because you have to take more pitches to get a walk. Would be interesting to see the data (if you didn’t already do it) with BB% and P/PA in AB’s with the minimum 4 pitches needed for a walk and see who walks more and strikes out more as the pitches seen rises.

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    • Andrew Patrick says:

      No I did not. It’d be interesting though.

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      • Matt says:

        ya… I didn’t think about where you got your statistics before I asked that lol. Realizing now that you were going off P/PA stats instead of the incredibly laborious and difficult task of actually being able to sort out at bats under 4 pitches. That project would take a very long time and lots of work.

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  6. Peter Jensen says:

    ya… I didn’t think about where you got your statistics before I asked that lol. Realizing now that you were going off P/PA stats instead of the incredibly laborious and difficult task of actually being able to sort out at bats under 4 pitches. That project would take a very long time and lots of work.

    ? All it takes is Retrosheet and a single query, about 20 minutes total time.

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    • Matt says:

      Ah will have to look at that, I took a peek at retrosheets very briefly and didn’t see that. I am just starting to really learn about the research side of things.

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