Does Movement Influence BABIP?

A couple weeks back, Pizza Cutter found an interesting oddity in that Troy Percival had consistently posted very, very low BABIPs. In response, Dave Studeman brought up Mariano Rivera–another pitcher with consistently low BABIPs–and how it has been somewhat proven that elite relievers can register atypical results with this statistic. Mentioned on a few other sites was the idea that movement may be a central cause for these lower batting averages on balls in play; due to said movement, the sweet part of the bat would fail to meet the ball as it normally would on more “standard” pitches.
Last week, we explored the relationship between fastballs 92+ mph and BABIP, examining how it differed at each mile per hour interval. 92 mph to 96 mph clocked in between .290-.310–the established general range of BABIP for pitchers–before dipping to .273 at 97 mph and shooting back up to .293 for all thrown 98 mph or higher. The 97 and 98+ groups were too small in their sample sizes to definitively fail the 5% hypothesis; we would need around 1,650 balls in play and, combined, had 1,032. Still, the combo of 97 and 98+ offered a .279 BABIP, perhaps suggesting that the .293 at 98+ was the anomaly, not the .273.
Today we will look at the movement within the same 92+ mph range in order to attempt to answer the question posed in the title. First, though, a pre-requisite of sorts with regards to movement: the relationship between horizontal and vertical components is not extremely known yet other than some telltale signs aiding in the classification of pitches. For instance, a two-seam fastball will have much higher horizontal movement than vertical movement; however, four-seam fastballs generally have lower horizontal movement and higher vertical movement.
I queried my database for all fastballs 92+ mph and separated the results into groups by movement rather than velocity intervals. The signs (+-) were reversed so that righties and lefties could be grouped together as well. First, here is a sample size grid of sorts, showing all balls in play for each horizontal group and vertical subgroup; note that the subgroups differ for each horizontal movement grouping so they will be called simply below average or above average as they were essentially determined by the average or a similar type of cutoff point. The reasoning for this is the aforementioned relationship between movement components; for fastballs, lower horizontal movement will usually correlate with higher vertical movement with the inverse also being true.

Horizontal

Below Vert BIP

Above Vert BIP

0-4 in

3,735

2,456

4-8 in

6,823

4,718

8-12 in

4,355

3,227

12+ in

408

335

BABIP takes a while to stabilize, moreso than many other statistics, so I wanted to have at least 2,000 balls in play for each sub-grouping, preferably more. From 0-12 inches of horizontal movement we have large enough samples to notice discrepancies. Greater than 12 inches, however, offers just 743 balls in play. While I definitely plan to explore this and the velocity articles later in the year when more data is available, for now, I am going to exclude the group with more than 12 horizontal inches.
Looking at the other three groups and their two subgroupings each, here are the Ball%, Strike%, HR%, and BABIP:

Horiz.

Vert.

B%

K%

HR%

BABIP

0-4

Below

35.9

45.6

0.53

.289

0-4

Above

34.9

49.8

0.48

.286

4-8

Below

35.8

43.7

0.64

.302

4-8

Above

35.8

48.2

0.58

.292

8-12

Below

35.6

41.4

0.54

.315

8-12

Above

36.5

45.6

0.58

.298

The percentage of balls essentially stays in the same general range while the strikes fluctuate. The subgroupings with above average vertical movement have much higher strike percentages than others. So, judging by this it seems before we even get to BABIP, that higher vertical movement in these larger groups result in a higher percentage of strikes.

The BABIPs for horizontal movement groups with below average vertical movement register: .289, .302, and .315. The BABIPs for horizontal movement with above average vertical movement clock in at: .286, .292, .298. Judging from these results it would appear that, yes, movement does have some type of effect on BABIP. Each horizontal group posted higher counts when they had below average vertical movement, and at every interval as well; .289 to .286, .302 to .292, and .315 to .298. Additionally, all pitches 92+ mph with 0-4 inches of horizontal movement, regardless of whether or not they fell above or below the vertical cutoff point, produced a BABIP lower than .290, which is generally the lower edge of the .290-.310 range we expect it to fall into.

Tomorrow I’ll come right back with the total number of unique pitchers and those comprising at least 1% and at least 5% of the sample, in order to see if the results are skewed in any way. For now, though, it appears that, regardless of your horizontal movement, having above average vertical movement will produce a lower BABIP at each horizontal interval.

Heater Getting Hotter

Yesterday we looked at the averages of fastballs from different velocity groups as a means to compare certain pitchers to their like-throwing peers as opposed to an extremely broad group.  This way, we can compare Matt Cain’s movement to the average movement for all 94 mph fastballs to determine how effective it has been.
In doing so an anomaly surfaced: all velocity groups had a BABIP between .290-.310 except those thrown 97 mph.  Those heaters registered a .273 BABIP, nearly 20 points below the others.  Sure enough, fastballs registering 98 mph or higher jumped back to .293, leading many of us to believe something screwy, flukey, or any other adjective ending with the suffix “-y” slapped on its end, was taking place.  After exploring some logical possibilities, like a split-half reliability test, or a look at BABIP by count and location, the results either stuck or were inconclusive due to small sample sizes at work.
We had a really nice discussion in the comments section wherein more possibilities were tossed around.  The first of these suggestions involved testing the sample size via a Bernoulli Trial.  As was shown by commenter Adam Guetz, for an observed .273 when a .295 was expected, we would need approximately 1,650 balls in play.  For 97 mph pitches there were 707 balls in play, less than half of what is required, and just 325 balls in play for 98+ mph.  While the sample sizes of actual pitches thrown are large enough to conduct certain analyses, those of balls in play for anything 97 mph or higher were not.  Here are the BIP sample sizes:

  • 92 mph, 18.85 % BIP and 7,759 total
  • 93 mph, 18.05% BIP and 6,023 total
  • 94 mph, 18.05% BIP and 4,389 total
  • 95 mph, 17.04% BIP and 2,827 total
  • 96 mph, 17.26% BIP and 1,596 total
  • 97 mph, 16.69% BIP and 707 total
  • >98 mph, 16.11% BIP and 325 total

The samples from 92-96 appear large enough, but the combination of 97 and 98+ still comes a good 500 pitches below 96 mph on its own.  Another suggestion called for the total number of different pitchers as each interval as well as the number of those comprising certain percentages of the samples.  This way, we might be able to deduce that 97 mph pitches were skewed due to a small group representing the whole; for the lower velocities, which are more common, it is much more likely for the pitches to be more evenly divided amongst a larger group of pitchers.  Here are the number of pitchers for each group, those comprising 1% of the sample, and those comprising 5% of the sample:

  • 92 mph: 574 total pitchers, 8 at 1%, 0 at 5%
  • 93 mph: 485 total pitchers, 18 at 1%, 0 at 5%
  • 94 mph: 516 total pitchers, 21 at 1%, 0 at 5%
  • 95 mph: 337 total pitchers, 25 at 1%, 0 at 5%
  • 96 mph: 237 total pitchers, 28 at 1%, 1 at 5%
  • 97 mph: 160 total pitchers, 25 at 1%, 4 at 5%
  • >98 mph: 102 total pitchers, 18 at 1%, 8 at 5%

In the 97 mph group, the four pitchers with at least 5% of the sample combine to represent 23% of the total.  For 98+ mph, the eight pitchers with at least 5% of the sample combine to represent 56% of the total.
From these results it seems that 92-96 mph are safe from a drastic case of small sample size syndrome.  Anything abobe 97 mph, though, seems to be the opposite as they suffer from a small sample of balls in play as well as skewed results due to a small group of pitchers representing most of the total pitches. 
Another commenter, Dave Evans, pointed out that he received a significance of 0.55 when comparing 97 and 98+, meaning their BABIPs were not statistically significantly different; for significance, that value would need to be equal to or below 0.01.  This led me to group 97 and 98+ together, to enlarge the sample.  The result was 1,032 balls in play, 288 hits in play, and a .279 BABIP.  This suggested the possibility that perhaps it was not 97 mph that deserved the adjective+suffix “-y” treatment but rather 98+ mph pitches.  Granted, it is still a small sample, even moreso for BABIP, but perhaps we will find out, as more data becomes available, that 97 mph is the threshold, as Pizza Cutter noted, for “blowing it by the hitter.”
It will require several hundred more pitches in play to determine this with any certainty but I will be keeping very close tabs as the season progresses.  For now, though, we can effectively compare individual pitchers to the average movement components, B%, K%, and BABIP for their specific velocity, not an entire group, at least for heaters 92 mph to 96 mph.

Breaking Down the Heater

Back on December 20th, John Walsh wrote a very interesting article at The Hardball Times, taking everything recorded by the Pitch F/X system in 2007 and, amongst others, calculating the average velocity, horizontal movement, and vertical movement for the four major pitches: fastball, curveball, slider, and changeup.  The results showed that the average fastball clocked in at 91 mph with -6.2 inches of horizontal movement and 8.9 inches of vertical movement.  The author acknowledged that he did not differentiate between four-seamers, two-seamers, and cutters, but rather lumped them all together in determining the averages; two-seamers and cutters differ in velocity and movement components from four-seamers.

While I plan on calculating the averages for all different sub-groupings of pitches at some point, what recently piqued my interest was finding the averages for different velocity groupings.  As in, what is the average horizontal movement for all 94 mph fastballs?  Or, the BABIP for 98 mph fastballs? 
With that knowledge we could effectively compare certain pitchers to the means of their velocity grouping rather than overall averages of every grouping.  Instead of comparing, say, Edwin Jackson’s 94 mph fastball to a group including those who throw slower, we can compare him to his “peers.” 
I started at 92 mph and queried my database for groupings (92-92.99, 93-93.99, etc) all the way up until 98+ mph.  I figured 92 mph would be a solid starting point since the sample size would be extraordinarily large–large enough for four-seamers to overcome the two-seamers and cutters that may inevitably sneak in.  Anything 98 mph or higher was grouped together to ensure a large enough sample since, as you will see below, the higher the velocity, the smaller the sample:

Velocity

Sample

%

92 mph

41,157

31.4

93 mph

33,368

25.5

94 mph

24,315

18.6

95 mph

16,586

12.7

96 mph

9,245

7.1

97 mph

4,236

3.2

>98 mph

2,018

1.5

All of the sample sizes here were large enough for analysis.  Even though the 98+ group appears to be 1/20th the size of the 92 mph group, that speaks more for the latter than against the former.
Next, how do the movement components look for each group?

Velocity

Horiz.

Vert.

92 mph

-6.34

9.24

93 mph

-6.28

9.51

94 mph

-6.16

9.80

95 mph

-5.98

10.07

96 mph

-5.84

10.23

97 mph

-5.89

10.41

>98 mph

-6.03

10.38

It should be fairly apparent that the tendency is for horizontal movement to decrease and vertical movement to increase as the velocity increases, at least through 96 mph.  At 97 mph, both movement components increase.  At 98+ mph, the vertical movement stays stagnant while the horizontal movement jumps quite a bit.
The next area to discuss includes B%, K%, HR%, and BABIP:

Velocity

B%

K%

HR%

BABIP

92 mph

35.9

44.6

0.65

.302

93 mph

36.3

45.1

0.55

.303

94 mph

35.5

45.9

0.55

.292

95 mph

35.8

46.4

0.76

.303

96 mph

35.2

47.0

0.54

.291

97 mph

36.1

46.8

0.41

.273

>98 mph

33.9

49.3

0.69

.293

The percentage of balls doesn’t move too much until its dip of over two percentage points at 98+ mph.  The amount of strikes, however, seems to increase.  There is no real discernible pattern in the home run percentages; the most came on 95 mph heaters while the least came on those registering 97 mph.

Speaking of the 97 mph group, notice anything odd?  Perhaps that their BABIP is .273, a full eighteen points below any other group?  Prior to getting the results I expected each group to fall somewhere in the .290-.310 range; that all of them did except the .273 struck me as very peculiar.

I spoke to several other analysts, all of whom initially mentioned small sample size syndrome, only to redact the assessment after learning the sample sizes in question.  The dropoff in home run percentage was tossed around, as well, since less home runs means more balls in play to be counted in the BABIP formula.  This is a “could be,” though, rather than a “definitely why.”  As was mentioned in these discussions, too, it could be nothing; perhaps there were more warning track flyballs that just missed leaving the yard as opposed to weaker hit balls.

Now, while the 4,236 pitches at 97 mph constitutes a large enough sample to analyze, the balls in play were not large enough yet to break into individual counts or locations.  When they do get big enough this could serve as a means of explanation; perhaps something in either or both does not jive with the other velocity groups.  Of those with significance, however, there was a .263 BABIP on 0-0 counts, and a .286 BABIP on pitches in the middle of the strike zone.

Pizza Cutter, or “The Master of Statistical Reliability” as I like to call him (yeah, a nickname for a nickname), suggested that BABIP is one of those stats that is super-unreliable, even with my large sample of pitches.  I did a split-half reliability test, randomly splitting the sample in half, and calculating the BABIP of each half.  For those unfamiliar, this serves to test the reliability of the sample; if it truly is large enough then no matter how we cut the sample in half we will have fairly convergent results.  If the results were wildly divergent then we are dealing with an unreliable sample.  The BABIPs of the two groups were .271 and .275, which essentially threw that idea out of the window.

Something interesting to consider was how, in each of these tables, all patterns seemed to stop when they reached 97 mph or higher.  The horizontal movement increased instead of its decreasing trend; vertical movement decreased after its increase at 97; the percentage of strikes ceased increasing; and home runs reached their low.  Could be something, could be nothing, but interesting nonetheless.

For now I am going to chalk this BABIP drop as an extreme random statistical variation and hope that you loyal readers out there might chime in with some more ideas to investigate.  Otherwise, though, when gauging the movement components, percentage of balls/strikes/home runs, or even BABIP, we can compare individual pitchers to their “like-minded” averages by velocity grouping.  If I get enough feedback involving different aspects to measure regarding these fastballs we will look at that soon, in the next day or two.  Otherwise, next week I have something similar to this, looking at BABIP by movement.

Juuust A Bit Outside

On Thursday we took a look at the pitchers with the highest percentage of Pitch F/X-recorded pitches right down the middle of the plate.  I listed the top thirty out of the 165 pitchers with significant numbers and found that Ted Lilly of the Cubs has thrown the highest percentage; on top of that, the next pitcher on the list found himself relatively far off.  Today we are going to look at the opposite: The pitchers with the highest percentage of pitches outside the zone.
Now, outside the zone calls for four general parameters: very high, very low, outside to the left, and outside to the right.  I feel like I’m typing the Cha-Cha slide.
For now I am going to focus on the left/right parameters outside the strike zone, and we will explore high/low a bit later in the year as I have other ideas centering around those parameters.  As discussed previously, the strike zone on a general pitch location chart goes from -0.83 to 0.83 on the horizontal axis and 1.6 to 3.5 on the vertical axis.  To track pitches down the middle the axis numbers were set much smaller.  To track pitches outside the zone the horizontal axis numbers branch out in different directions.  For pitches outside to the left I set my database to give me all pitches with a PX (horizontal location in the data) less than -1.55 as well as greater than +1.55.
This provided me plenty of pitches to analyze but keep in mind that the data was not insanely consistent last year with regards to who gets recorded and where the recording takes place.  This year it has become more consistent and uniform but there may be data discrepancies due to some players having insufficient data.  For instance, Player A might be known to throw a ton of pitches out of the zone but, because the Pitch F/X system did not track many of his starts, he might not qualify. 
To help ensure the pitchers in the below leaderboard did not fall into this statistical fallacy, a minimum of 240 raw pitches was set.  That certainly whittled the list down.  The total tracked pitches were then recorded for all remaining pitchers, and they were then sorted by % instead of raw total.  Here are the top ten:
1) Livan Hernandez, 15.83%
2) Derek Lowe, 12.43%
3) Jake Peavy, 12.39%
4) Chad Gaudin, 11.56%
5) Braden Looper, 11.56%
6) John Smoltz, 11.40%
7) Jamie Moyer, 11.20%
8) Justin Germano, 11.01%
9) Jeff Francis, 10.73%
10) A.J. Burnett, 10.71%
I did not necessarily predict that Livan would be atop this leaderboard but, at the same time, it was not very surprising to find his name there, with a significant lead over the next pitcher nonetheless.  Moyer didn’t surprise me either as he’s a notorious “junkballer.”  Here are 11-20:
11) Jarrod Washburn, 10.59%
12) Carlos Zambrano, 10.05%
13) Shaun Marcum, 10.03%
14) Tim Hudson, 9.78%
15) Javier Vazquez, 9.66%
16) Kevin Millwood, 9.63%
17) Jose Contreras, 9.54%
18) Miguel Batista, 9.33%
19) Roy Halladay, 9.29%
20) Vicente Padilla, 9.14%
Something really interesting here is the emergence of Burnett, Marcum, and Halladay.  I noted in the comments on Thursday that, of pitchers with significant data, Burnett, Marcum, and Halladay were in the bottom ten of percentage of pitches thrown right down the middle; here they are in the top twenty of pitches thrown outside the zone.  I noted at Fangraphs a week or two ago that the Blue Jays rotation, arguably the best in the bigs both last year and this year, consisted of three guys (McGowan, Marcum, Litsch) who threw four or five different pitches at least 10% of the time, somewhat of an extreme rarity.  Additionally, Halladay has a potent three-pitch combo, and Burnett has a plus-fastball and plus-curveball.
Put together it seems like the Blue Jays pitchers are spreading their pitch selections quite liberally, rarely making mistakes in throwing the ball right down the middle, and not worrying about being outside the strike zone.  Perhaps this means nothing with regards to their performance, but it is interesting nonetheless that a rotation like this appears in the leaderboards in three different areas of selection/location.
As we get deeper into the season enough data will be compiled to look at both down the middle and outside pitches solely for 2008, when the data is tracked in each park.  For now, though, we’ll have to settle with Ted Lilly and Livan Hernandez.  If only those two faced each other this year.

Right Down the Middle

Last week I took a look at the relationship between pitches and home runs, checking to see if there were any noticeable discrepancies between those that sail out of the stadiums and those that do not.  The results showed that fastballs turned into souvenirs when they came in with lesser velocities and movements as well as with poor location; breaking balls were hit out when they hung in the zone.

While conducting these analyses I became very interested in pursuing the idea of mistake pitches and balls thrown not just in the zone but right down the middle.  Of all the balls that were hit for home runs from the top home run surrendering pitchers this year, at least 80% were smackdab in the middle of the plate.  Since this piqued my interest I decided to check out which pitchers threw down the middle most often.
The strike zone, in Pitch F/X terms, is generally -0.85 to 0.85 on the horizontal axis and 1.6 to 3.5 on the vertical axis.  I went smaller, looking at pitches in the middle of that zone, as evidenced by this picture:

strikezone.JPG


Probing my database for pitches in the smaller box–what I would consider to be down the middle–I found a ton of pitches.  Keep in mind, though, that the results below are from pitches tracked by the Pitch F/X system; there are some pitchers that might have a higher total or percentage but did not have the luxury of having their relevant data recorded.
I found 165 pitchers with a significant number of pitches down the middle.  Luckily, in terms of using neat/even numbers in a list, the top 30 percentages happened to consist of everyone with at least 14% of their pitches thrown down the middle.  Here are the top ten:
1) Ted Lilly, 18.6%
2) Paul Byrd, 16.7%
3) Josh Beckett, 16.3%
4) Micah Owings, 16.1%
5) Tim Lincecum, 15.9%
6) John Danks, 15.8%
7) Felix Hernandez, 15.7%
8) Greg Maddux, 15.5%
9) Joe Blanton, 15.5%
10) Justin Verlander, 15.4%
Lilly threw just about two percent more pitches down the middle than his closest competitor whereas #2-#10 were separated by a total 1.3 percent.  Numbers 11-20:
11) Andy Sonnanstine, 15.3%
12) Kevin Millwood, 15.2%
13) Cole Hamels, 15.1%
14) Aaron Harang, 15.0%
15) Brian Bannister, 14.8%
16) Daisuke Matsuzaka, 14.7%
17) Vicente Padilla, 14.7%
18) Matt Cain, 14.7%
19) Javier Vazquez, 14.7%
20) Randy Wolf, 14.6%
And the last group with at least 14% of their pitches down the middle:
21) Brad Penny, 14.5%
22) Roy Oswalt, 14.5%
23) Johan Santana, 14.4%
24) Nate Robertson, 14.3%
25) Ervin Santana, 14.2%
26) Miguel Batista, 14.2%
27) Jon Garland, 14.1%
28) John Lackey, 14.1%
29) CC Sabathia, 14.1%
30) Jarrod Washburn, 14.0%
Unfortunately, just as David Appelman found a couple of years ago, there is not much correlation between pitches thrown down the middle and, well, anything else at all.  I thought there might be something significant between down the middle pitches and line drives–it’s been theorized before that line drives might correlate quite well with mistake pitches–but, alas, there was not; at least not yet.
Additionally, I would like to explore this at the end of this season, or perhaps further into the year, when all pitchers would have the same (or close to it) amount of data recorded.  For now, though, at the very least, it’s somewhat interesting to see which pitchers throw the most down the middle.
On Saturday we will look at the opposite, pitchers who throw the most OUT of the zone and then compare the results (Balls, Called K, Swing K, etc) between pitches down the middle and those out of the zone.

Gopher Balls

On Thursday I took a look at Johan Santana in order to see if there were any commonalities amongst his home runs surrendered, via Pitch F/X, when compared to other results.  The results showed that, on his home run balls, not only were the pitches right down the middle of the plate but they came in at slower velocities with less movement.  Pythagoras may have never postulated the following but my baseball intuition suggested that:
Down the Middle + Slower Velo + Less Movement = Home Run
It might not be true on every occasion but you would think the above formula rings true more often than not.  A home run–unless at Citizens Bank Park or Coors Field–generally signifies the batter getting “good wood” on the ball; achieving that is intuitively more likely on worse pitches, IE, pitches with less movement, poor location, and slower velocity.
Brian Cartwright of Seamheads commented on the Johan article with regards to curiosity over whether or not the same formula would ring true for other pitchers: Would their home run balls primarily come from less velocity, less movement, and poor location?  Taking the top 20 pitchers in home runs allowed I took to the spreadsheets to find out.
For starters, here’s a look at the overall averages of this group, in velocity and vertical movement (horizontal movement switches signs for lefty/righty) on their gopher balls:

HR Pitch Velocity Vert. Move.
FA 89.87 9.84
SL 82.12 4.09
CU 74.87 -3.62
CH 82.06 5.44

And, when we remove the home run pitches from the aggregate of all pitches thrown by these 20 pitchers, we get these results:

Pitch Velocity Vert. Move.
FA 91.41 9.63
SL 82.29 2.49
CU 75.01 -5.29
CH 82.28 5.44

Looking first at velocity we see that fastballs are around 1.5 mph slower on home runs than all other pitches. Sliders, curveballs, and changeups appear to have increased but since each of them individually comprised very small portions of the overall pitches resulting in home runs, I’d feel more comfortable saying they have sustained velocity. Fastballs are slower on gopher balls, the other three pitches come in at the same speed.
In terms of movement, fastballs have ever so slightly increased movement (0.21 inches) on gopher balls; sliders and curveballs have lost significant movement; and changeups have sustained their movement regardless of whether the result is a home run or the rest.
Put together, their fastballs have been 1.5 mph slower but the movement is essentially the same. Sliders and curveballs are coming in at virtually the same speed on the gopher balls but significantly less movement; IE – same speed but straighter. Changeups, however, have sustained velocity and movement; there has literally been no difference between the changeups hit for home runs and resulting in all other possibilities.
Lastly, for now, here is a location chart of where all of the fastballs have crossed the plate that then sailed over the bleachers for home runs:
fastballshr.JPG
Again, it appears that a high percentage of these fastballs have been right over the middle of the plate. Though these results do not necessarily agree 100% with the Johan results, it seems that the top 20 home run surrenderers are throwing their fastballs slower, with similar movement, right over the heart of the plate.

You Don't Mess With the Johan

This offseason the biggest transaction clearly had to be Chad Durbin signing a 1-yr, < 1 mil deal with the Phillies.

You Don’t Mess With the Johan

This offseason the biggest transaction clearly had to be Chad Durbin signing a 1-yr, < 1 mil deal with the Phillies.  Other than that, the only move in the same vicinity of importance involved Johan Santana joining the New York Metropolitans.  Arguably the best pitcher in the game, Santana seemed poised to revive a team on the heels of a terrible 2007 finish.  Additionally, joining the Mets rotation gave them four quality starters.
Johan’s 2007 campaign was less-than-Johan; his established performance level had been so high that anything less would be uncivilized uncharacteristic of our expectations.  Despite still posting solid numbers–don’t let the 15-13 record fool you–they were not as good as his Cy Young Award worthy numbers from 2004-2006.
This year, the Mets are off to a slow start but Santana has performed well by most accounts.  Oddly though he has barely been seen on mainstream media outlets.  While his numbers the last three years, in small market Minnesota, were all over Sportscenter, I actually had to look up this year’s numbers for this article… which is odd considering I’m a fan of an NL East team and he plays in the media center of the world.
One knock on his performance last year into now is his pension for surrendering longballs.  Saber-friend Cyril Morong recently e-mailed me to see if I had see anything with Johan, statistically or Pitch F/X-ically, that might explain what has been different.  Unfortunately, we don’t have the Pitch F/X data for Johan in his 2004-2006 prime, but tendencies or discrepancies can still be determined with the numbers available.
2007-2008 Statistics
For starters, take a look at his stats last year:
33 GS, 219 IP, 183 H, 52 BB, 235 K, 15-13, 3.33 ERA, 3.82 FIP, 1.07 WHIP, 4.52 K/BB
And how they stack up with the numbers so far into the 2008 season:
12 GS, 81.2 IP, 78 H, 20 BB, 71 K, 7-3, 3.20 ERA, 3.95 FIP, 1.20 WHIP, 3.55 K/BB
Selection/Frequency
Next, here are his frequency of pitches in 2007, split by handedness, thanks to the triumvirate of Mike Fast’s analysis a few months ago, Josh Kalk’s player cards, and my own research:

LHH

%

RHH

%

FA

64.1

FA

59.2

SL

26.9

SL

8.2

CH

9.0

CH

32.6

And in 2008:

LHH

%

RHH

%

FA

50.5

FA

61.0

SL

44.2

SL

10.8

CH

5.3

CH

28.3

It should be very clear that Johan is throwing less fastballs and significantly more sliders to lefties so far.  His approach to righties has not changed much, with the exception of a slight increase in FA and SL; the CH is being used about four percent less.
Velocity/Movement
How about his speed and “bite”?  Have they changed?  Compare his 2007 numbers to those right now:

2007

Velo.

Horiz.

Vert.

FA

92.8

7.58

11.22

SL

84.9

2.96

4.81

CH

81.8

8.47

7.62

2008

Velo.

Horiz.

Vert.

FA

91.9

6.14

9.04

SL

84.3

-0.26

4.45

CH

80.8

6.29

7.14

He’s throwing slower–albeit slightly–on each of his pitches.  Just as important, he is throwing with much less movement.
Hits vs. Swing K + Foul
Thinking there might be something different between his velocity/movement/selection on pitches resulting in hits and those whiffed at or fouled off I filtered the info and found the following:

Hits

%

Velo.

Move.

FA

56.8

91.4

7.25/9.47

SL

15.3

84.5

4.27/5.63

CH

27.9

80.6

5.32/5.82

F/SwK

%

Velo.

Move.

FA

52.7

91.7

6.22/10.36

SL

15.5

84.6

3.79/6.04

CH

32.3

80.4

5.60/6.51

He seems to have more movement on those fouled off or missed but very similar velocities.  In terms of specific pitches, those missed or fouled off have consisted of more changeups at the expense of fastballs; ~57% of his hits surrendered, captured by the dataset, came on fastballs.
HR Specifics?
Lastly, I looked at the home runs surrendered, those captured by the Pitch F/X system, to see what happens on those pitches.  First, his velocity and movement (note – of the data I have, no HR have come on sliders):
Fastballs: 90.7 mph, 5.59/10.21 movement
Changeups: 80.4 mph, 3.70/6.29 movement
His fastball velocity is, by all accounts, lower on home runs than anywhere else mentioned throughout this article.  His FA movement is very similar to that of those fouled off or missed.  His changeup has come in a bit straighter as evidenced by the decrease of close to two horizontal inches in movement. 
Here’s a location chart of where these gopher balls have come in:
santanahr.JPG
Notice anything?  Maybe that most, if not all, are right over the middle of the plate?  Pitches with decreased velocity and movement, coming in right over the middle of the plate are not necessarily surprising in the sense of leaving the yard off the bat.
Overall For Now
This isn’t meant to be a definitive look but rather something to keep in mind going forward.  It appears that there truly isn’t much of a discrepancy in velocity but he had much more movement last year; additionally, his movement was higher on pitches fouled at or missed than on hits.  On home runs, the slower fastball velocity, threesomed with the less movement and poor location seem to make the most sense as likely causes.

The Most Important Pitch: A Look at Greg Maddux and 1-1 Counts

There are twelve possible ball-strike counts in a given plate appearance.  Ranging from the initial 0-0 to the dramatic 3-2, these counts shift in favor of either the batter or the pitcher.  A 2-0 count favors the hitter; if the pitcher misses the count will run to 3-0.  Along similar lines, an 0-2 count favors the pitcher because the batter will theoretically be more likely to swing at junk in an effort to protect himself.
Of all twelve, Greg Maddux considers the 1-1 count to be of utmost importance.  Though some may spot the identical numbers and deem the count neutral, the linear weights run expectancy shows it favors the pitcher.  Missing on a 1-1 count shifts the momentum back towards the hitter whereas a successful 1-1 pitch can move the count’s favor further in the direction of the pitcher.  The 1-1 count brings with it a run expectancy of -0.012 from the batter’s perspective; a ball shifts it to +0.037 whereas a strike causes a jump to -0.079.  Maddux is right.
This is the third and final (for now) look at Greg Maddux’s theory and selection in certain situations using Pitch F/X data.  Previously, we have looked at Maddux’s “playbook” vs. Bengie Molina as well as his selection, location, and results in 0-2 counts, in which he does not like to throw waste pitches.  Here we are going to conduct a similar analysis to the 0-2 article but with regards to his 1-1 counts.  Be sure to note that not all of his starts were recorded by the Pitch F/X system last year.
Results
Maddux primarily throws his two-seam fastball, a changeup, and a cutslide. Though “slutter” sounds funnier for the combo cutter/slider, this blog has a PG rating… though nowadays even PG allows naughty words and innuendos.. anyway, back to baseball. Here is a breakdown of Maddux’s pitches and results to lefties and righties:
maddux11countresults.JPG
Since he has thrown more pitches to righties, seeing the percentages of pitches thrown to each batting handedness can help show discrepancies in either approach or selection. To righties, Maddux has thrown 58.9% fastballs, 25.2% changeups, and 15.9% cutslides; to lefties, 55.3% fastballs, 32.5% changeups, and 12.3% cutslides. Clearly, he uses the cutslide sparingly. Maddux has thrown three percent less fastballs to lefties, as well as three percent less cutslides; the difference has been made up with over six percent more changeups.
Location
Here is a location chart of his fastballs thrown to both lefties and righties, with lefties always on the left:
madduxfa11count.JPG
The biggest difference between results here is the amount of called strikes. When facing righties, Maddux has gotten many called strikes on 1-1 counts whereas he has just four when pitching to lefties. Though he clearly favors the outside corner to both types of batters, lefties have made contact with the corner pitches while righties seem to be more inclined to take the pitch. Due to his fastball having the tailing movement, righties tend to think pitches like this are outside; when it tails back to the outside corner for a strike it catalyzes many glances back at the umpire.
Here is a location chart of the changeups thrown:
madduxch11count.JPG
The results of his changeups thrown to each batting handedness do not differ too much; even if they did it is too small of a sample to garner anything worthwhile from. Despite this, the visualization helps us see that he has thrown a higher percentage of changeups in the strike zone and down the middle to righties; to lefties he continues to hit the outside corner. Regardless, the pitches that have worked the best for him in these situations have been changeups to lefties and all offspeed pitches to righties. Essentially, throwing it in the general vicinity of down the middle has not yet hurt him in 1-1 counts in the Pitch F/X era.
Location Results
No, I didn’t just combine the headings of the previous two sections no matter how much it may seem like that. Maddux’s fastball has not been particularly effective to lefties or righties in these counts. Therefore, I want to look at the nine zone sections–up and away, down and in, etc–and see what types of results his fastballs have produced. Unfortunately, small sample size syndrome has forced me to combine the nine sections into three: away, middle, in. Here are the results:
lhhrhh-locationresults.JPG
These are not large samples either but we can still discern some potential strategies to watch for over the remainder of the season. He has had his most success with the fastball away, to both types of hitters, even though righties have still done well with the balls in play. I hate even attempting to draw conclusions from these small samples, but based on the non-BIP results and the BIP results, it seems Maddux’s best chance at getting the 1-2 as opposed to the 2-1 would be to stick to his offspeed stuff (cutslide or changeup) but if he had to throw the fastball, make sure it is away to lefties and, more specifically, down and away to righties.

Waste This: An Analysis of Greg Maddux's 0-2 Pitch Selection

Last week we took a look at a bunch of plate appearances between Greg Maddux and Bengie Molina, in an attempt to see if there were any discernable patterns or tendencies on the part of either participant.

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