Quick F/x: Jorge Sosa is back

Do you remember Jorge Sosa? He was a pitcher for the Devil Rays and Braves back in the day. He has recently spent time with the Mets, Mariners and Astros, before joining the Nationals. Oh yes, the Nationals are desperate for bullpen help.

Now is the perfect time to debut the newest version of my Pitch F/x flight paths. Thanks to Harry Pav for creating the first template, and answering my questions last night when I was updating it.
sosa_fp_8-02-09.pngThe plate/strike zone are for reference, and should not be treated as perfect. However, they should be pretty close. Click on image to enlarge.

The fastball/slider combo is solid, and he’s still throwing in the low 90′s. He has never been much for controlling his pitches, so it’s no surprise the pitches are over the plate. However, the slider stayed low, so it’s not too bad.
Congrats, Nationals, you have yourself an average pitcher.

Pitch F/x: Chris Tillman’s Debut

Orioles prospect Chris Tillman made his debut last night, so let’s take a look at how he did. First, his line from his outing:


Baltimore IP H R ER BB SO HR ERA
Tillman 4.2 7 3 3 1 2 3 5.79

Now, let’s go to the Pitch F/x, in all its glory. Click on image to enlarge.
tillman_flight_7-29-09.png
Fastball - 60 at 93.35 MPH
Changeup - 18 at 81.07
Curveball - 15 at 79.38
Now, he has some good movement on his curveball, and his changeup is ok. His fastball is a little straight for my liking, but he may be able to get away with it if he continues throwing it at 93 MPH.
Expect to see plenty more from Tillman this year, as he seems to be up in the bigs for good. He’s already proved himself in the minors (posted a FIP of 2.95 in AAA this year), so the majors are his next task.

Pitch F/x: Chris Tillman's Debut

Orioles prospect Chris Tillman made his debut last night, so let’s take a look at how he did. First, his line from his outing:


Baltimore IP H R ER BB SO HR ERA
Tillman 4.2 7 3 3 1 2 3 5.79

Now, let’s go to the Pitch F/x, in all its glory. Click on image to enlarge.
tillman_flight_7-29-09.png
Fastball - 60 at 93.35 MPH
Changeup - 18 at 81.07
Curveball - 15 at 79.38
Now, he has some good movement on his curveball, and his changeup is ok. His fastball is a little straight for my liking, but he may be able to get away with it if he continues throwing it at 93 MPH.
Expect to see plenty more from Tillman this year, as he seems to be up in the bigs for good. He’s already proved himself in the minors (posted a FIP of 2.95 in AAA this year), so the majors are his next task.

Pitch F/x: Clayton Mortensen

Earlier today, Matt Holliday was traded to the St. Louis Cardinals for three prospects, one of which was pitcher Clayton Mortensen. Mortensen has actually pitched 3 innings in the bigs, so he has pitch f/x data to his name. Let’s take a look, and as always, click on the image to enlarge.

mortensen_move_6-29-09.png
Mort (I’m calling him Mort, deal with it) has a solid sinker with good sideways movement, and an inconsistent moving changeup. His slider is also highly inconsistent, and doesn’t move much anyway.
mortensen_loc_6-29-09.png
His location is his biggest issue, as the sinker stays up in the zone too often. He needs to pull it all together and become more consistent with his sinker and changeup location.
Resources
Thanks to Brooks Baseball for making the data really easy to find, and Harry Pav for tips on pitch types.

Zito Gaining Speed?

Warning: Low(ish) Content

When running some pitch f/x numbers for an upcoming piece, I noticed Barry Zito’s fastball has been rising in velocity.
When Zito was having success with the A’s, his fastball was around 87 mph. It had dipped before he got to the Giants, but it has started to rise again.
Average lines are calculated approximations (if that makes any sense).
zito_fa_speed.png
Click on image for a larger picture.

The numbers:
2007: 84.8 mph
2008: 85.1
2009: 86.3
One thing to note, is that the velocity of his fastball has been consistently dropping over his last few starts. Not a lot, but it is there. 
Not sure if this even really means anything, but with all the talk about Zito today, it’s a cool thing to look at.

Another straight, effective fastball

If you don’t know him already, you should try and learn a thing or two about Mark DiFelice. In a nutshell, he’s a reliever for the Brewers who, after a long career in various levels of the minor leagues, has been mowing down hitters with nothing but an 82-mph fastball. No knuckleball or gyroball or anything like that, just batting practice fastballs that make guys like Hanley Ramirez look foolish.

In a post at FanGraphs, Dave Cameron presents this pitch f/x graph from one of DiFelice’s games:

difelice.jpg
Gameday classifies those pitches as sliders and changeups because, well, major league pitchers just don’t throw nothing but fastballs at 82 miles per hour and get away with it. DiFelice isn’t just getting away with it, he’s been more than 2 linear weights (LW) runs above average per 100 pitches with it despite throwing it almost every single time. The average horizontal movement of that “thing” is between +1.4 and -2.9 inches, so it’s pretty straight. But part of what makes it effective is that, compared to the average major league fastball, it’s not straight at all. Major leaguers have fastballs that, on average, tail about 5 inches to the arm side. Don’t believe me that it’s a fastball, DiFelice says so himself in that Yahoo link above.

I’m not going to go further in analyzing DiFelice, because my pitch f/x abilities are severely limited in that regard, and Dave Cameron already did a good job of it. What I noticed today was that there is another mostly unknown pitcher who has a similarly puzzling fastball.

That pitcher is David Robertson, a reliever for the Yankees. Robertson was known in the Yankees system as a guy with a devastating curveball and an average fastball. Radar guns confirmed this in the major leagues, when people saw the 90-91 mph fastball and his big looping curve racking up the strikeouts. I checked out his player card today and was surprised to see that 80% of his pitches this season have been fastballs. Not only that, his fastballs are registering 1.47 LW runs above average per 100 pitches. That puts him in the same company as Jonathan Broxton. Take another look at that graph above for DiFelice, and then look at this graph for Robertson’s game on June 12th against the Mets:

8241_P_0_200906120_game.jpg
Ignoring the colors, look at the cluster of dots in the middle of the graph. Yes, I realize that DiFelice’s ball drops a lot more than Robertson’s, but the horizontal movement is almost exactly the same. Robertson’s fastball is more like a cutter than anything else, and that’s probably why it has been so effective at around 90 mph, despite throwing it 4 out of every 5 pitches.

Ask any Yankee fan how Robertson has been able to have a strikeout rate of over 13 per 9 innings this year and over 11 per 9 innings in his career, and he’ll probably tell you it’s because of that curveball. And it might be because of it–after all, that huge curve might be in the back of a hitter’s mind, causing him to miss the fastball. If you want to surprise him, tell him just how effective Robertson’s “just average” fastball has been, and you’ll end up looking real smart.

Chien-Ming Wang was broken. Did they fix him?

Chien-Ming Wang returned to the major league mound on Friday night in relief of the struggling AJ Burnett. In three innings of work, he allowed 2 runs on 6 hits (one home run) and one walk. That outing lowered his season ERA by 9 1/2 points… to a cool 25.00. Yikes.

Wang had been rehabbing what was diagnosed as essentially weakness in his hips, which reportedly resulted from a foot injury he sustained last season in Houston while running the bases. He had been doing relatively well in rehab starts in AAA, and the Yankees needed a long reliever for the game after burning the bullpen the night before. I mentioned above Wang’s results for the night, but let’s take a look at how he got there, through the eyes of pitch f/x.

His average fastball was a strong 92.5 mph, averaging about 10 inches of horizontal movement and 6.8 inches of vertical “rise.” How does that compare to the beginning of this season, when he was getting bombed, and to 2008, when he was pretty good? Here’s a grid, for easier comparison:

Game    Avg. Velocity     Max. Velocity     Horiz. Movement (in.)   Vert. Movement (in.)
2008            91.8                  ~95                          11                                  4.9
2009            90.7                  ~93                         9.3                                  5.7
Tonight        92.5                    95                          10                                  6.8

Alright, so we see that his velocity tonight was back where it should be, and his horizontal movement is creeping back up. But what’s up with the vertical movement? He’s a sinker-ball specialist, remember. The lower the value of the vertical movement, the more the ball is “sinking.” Curveballs, which have a sharp downward break, are in the negatives. Tonight, 50% of his balls in play were groundballs, might that have been a fluke?

Let’s look at some individual games and find out. One of his best games in 2008 happened on May 2nd against the Mariners. He had 5 K’s, 2 walks, in 6 innings, allowing one run. More importantly (for our purposes), the M’s had 10 groundballs versus only 2 flyballs. In that game, Wang had 14.5 inches of horizontal movement, which is just fantastic, and 4.5 inches of vertical movement, which is also great. Going back a month earlier to opening day (which I was at), Wang threw 7 innings with 16 (!) groundballs, versus only 3 flyballs. In that game, the numbers were almost identical to the gem against the Mariners. He had 13 inches of horizontal movement and 4.5 inches of vertical movement, both of those of course being fantastic for a sinker/2-seamer.

So it would seem that it’s the vertical movement that’s causing him problems. here’s the part that completely threw me when looking into this: Chien-Ming Wang’s vertical movement on his 4/8 and 4/18 starts of this season were 5 inches and 3.5 inches, respectively. Remember, in those games he got absolutely bombed to the tune of 5 innings and 15 runs. His ball was sinking, but it was still getting rocked. Here was my reaction upon seeing this.

My next stop was the location graphs available on Brooks Baseball. If you look at Friday’s game, you’ll see that there were very few balls just below the strike zone. Compare that to his opening day start from 2008, and you see some difference. The Friday start isn’t nearly as down in the zone. These two starts, both from earlier this season, are even worse. Ok, so that was a lot of links all at once. If you got confused with keeping track of what’s what, or you just don’t want to click through, here’s what it means: Wang isn’t keeping the ball down this year nearly as well as he did in 2008. While his ball is sinking, it’s not being thrown down in the zone enough to be effective.

Finally, here is what I believe to be the main reason for his problems. There are 5 links below, all of them are from games already referenced in this post. What the images show is release point data, from pitch f/x. Simply put, the 2008 release points are from good games, the 2009 release points are from bad games. Try opening each one in a different tab and switch back and forth to more clearly see the difference.

4/1/2008   5/2/2008   4/8/2009   4/18/2009   snippet from beat writer Pete Abraham on April 14th, after Wang allowed 8 runs in an inning of work the day before: “Wang seemed stunned. He said the issue was where he released the ball, which was off to the side instead of over the top. A
sinkerball pitcher wants to stand tall on the mound and throw the ball
on a downward plane. Otherwise the ball floats over the strike zone and you see what happens
.” Despite having success in 2008 with a lower release point than he had in the early stages in 2009, he wanted to raise his release point even more.

To answer the question in the title, “did they fix him?” As it stands right now, I don’t think they did.

Chien-Ming Wang’s release point has been discussed previously at River Ave. Blues.
–Thanks to Brooks Baseball and FanGraphs for pitch f/x data.

The Return of Rich Hill

Orioles starter Rich Hill made his long-awaited return to the Major League mound on Saturday. Hill was last seen in the majors early last May when he was walking approximately everybody who came to the plate. OK, so maybe it was 20.2% of batters (8.24/9IP), but that’s still obviously unacceptable. This came as a surprise to everybody, as he had walked under 3 per 9 innings just one season before, and 3.53 per 9 in 2006.

There seemed to be two possible explanations. One line of thinking was that Hill had reverted to his pre-2006 walk-happy tendencies. In the minor leagues he had some problems throwing strikes, but seemed to solve them over time. The other theory was that he simply had contracted Steve Blass disease. So Hill was sent all the way down to the Arizona Rookie League at one point, but also spent time in the Single-A Florida State league, making 3 starts, and the Pacific Coast League (AAA), throwing 26 innings over 7 starts.

Regardless of where he went, though, he couldn’t find the plate, walking 28 in 26 innings in AAA, and 44 in 47.2 combined minor league innings. Rich Hill has had success so far this season after being traded to the Orioles and working in AAA. His control wasn’t spot on like it was in 2007 with the Cubs, but it was improving while he maintained his strong strikeout rate. When Hill returned to the major leagues on Saturday, he walked just two batters in 5.2 innings. So it would seem that Hill has improved somewhere. Usually, this is attributed to some kind of mechanical adjustment, either “staying closed,” or finding a consistent release point. I’ve been looking at his release points through pitch f/x to see if the latter is true. Take a look at the following two plots. One is his release point plot from his start on Saturday, the other is from his final MLB start in 2008 where he walked 4 batters in just two thirds of an inning.
release2.jpgrelease.jpgThe dots on graph on the bottom are pretty spread out compared to the graph on top. These are release points, remember, so it looks like his release point for that game was all over the place. That would seem to explain his control problems, right? Wrong. The graph on the bottom, which has the points plotted all over the place, is his game from this May 16th, in which he has just two walks and threw 61% of his pitches for strikes.

So what’s going on here? Has Rich Hill solved his control problems, or hasn’t he? This is where FanGraphs Plate Discipline comes in handy. Hill threw just 40% of his pitches in the strike zone, compared to 56% in the 2007 season. That’s just atrocious. His release point issues haven’t been fixed, and while that didn’t manifest itself in this start, I wouldn’t bet on him having sustained success this year unless he fixes it. Here’s his release point plot from a start against the Brewers in 2007 where he had 9 strikeouts, no walks, and threw 74% of his pitches for strikes:

release07.jpg
See how dense that plot looks around the center of his release point? There are a few outside the center of course, that will happen even with Greg Maddux pitching, but the vast majority fit inside a little 4×4 box in the center. He was extremely consistent that game and it obviously paid off with a dominant performance. I would be wary about picking him up in fantasy leagues until he shows improved consistency with his release points. And Orioles fans should reserve judgment for the time being–I’m just not sold that he’s back to being the very good and occasionally dominant Rich Hill that we saw two and three years ago.

Release point plots courtesy of Brooks Baseball.

Note: I put this up around 12:30 a.m. tonight (morning I guess). Within 90 minutes of me posting this, Harry Pavlidis of The Hardball Times (and Beyond the Box Score and Cubs f/x) posted an article with the same exact title. Great minds think alike. Harry focused on his stuff, while I focused on his control and release point. His article can be found here, I suggest reading that as well.

Recapping the BIP

Before even getting into the meat of this article, no, the title does not refer to Bip Roberts… so I’ll understand if hardcore fans of his are now turned off.  What the title does refer to, however, is balls in play and how they pertain to the statistics BABIP, FIP, and ERA.  I have written a lot here and on my other stomping grounds of late about how some of these statistics are affected and, seeing as it is a holiday weekend with not much interweb usage, it seemed like the logical time to recap everything into one neat package.  For starters, what are these three statistics?
BABIP: Batting Average on Balls In Play is a statistical spawn of the DIPS theory discovered by Voros McCracken at the turn of the century.  Essentially Voros found that pitchers have next to no control over balls put in play against them, which is why certain pitchers would surrender a ton of hits one year and much less the next.  From a control standpoint, the goal of the pitcher would be to get an out.  Once a ball is put in play, unless it is hit right back to the pitcher many defensive aspects have to coincide for an out to result.  Take a groundball for instance, one between shortstop and third base: both fielders have to understand whose territory the ball occupies and that fielder has to have the proper range in order to field it, all in a very short amount of time. 
There are plenty of other variables as well but what should be clear is that the pitcher has no control over them.  He may have control over sustaining a certain percentage of balls in play each year but the hits that result are almost entirely out of his hand.  In fact, the only aspects of pitching over which he has any type of control are walks, strikeouts, and home runs allowed.  Everything else is dependant on the fielding and luck.
BABIP is calculated by dividing the Hits minus Home Runs by the Plate Appearances excluding Home Runs, Walks, Strikeouts, and Sacrifice Flies.  If Player A has 30 hits out of 90 at-bats he will post a .333 batting average.  But if 8 of those 30 hits are home runs and 8 of the outs are strikeouts, in BABIP terms he would be 22 for 74, or .297.  This explains that, of all balls put in play–any hit or batted out other than a home run–29.7% fell in for hits.
FIP: a creation of Tom Tango’s, Fielding Independent Pitching takes the three controllable skills of walks, strikeouts, and home runs allowed, properly weights them, and then scales the result similar to the familiar ERA.  The end result explains what a pitcher’s skillset suggests his ERA should be around.  Someone with an ERA much lower than their FIP is usually considered to be lucky while the inverse is also true.  The statistic is kept at Fangraphs and ERA-FIP was recently added as well in order to allow readers a glimpse at those under- or overperforming their controllable skills.
ERA: arguably the most popular pitching barometer, ERA can be calculated by multiplying the earned runs of a pitcher by nine and dividing that product by the total number of innings pitched.  While not a terrible stat it suffers from some pretty drastic noise.  For starters, what are earned runs?  The surname ‘earned’ implies there are other runs that can be given up and that these must satisfy a specific criteria.  For instance, if a fielder botches a routine play with two outs, and the pitcher then gives up seven runs, none will be earned because the inning was extended by the poor play of the fielder.  This gets into all sorts of questions regarding exactly what an error is and how that factors into a pitcher’s performance.
Earned runs are also a direct result of hits, which have been proven to be largely accrued through chance via the DIPS theory.  So, if pitchers cannot control the percentage of hits they give up on balls in play, then fluctuations in hits can either inflate or deflate an ERA regardless of the pitcher’s skill level.  Therefore the FIP is more indicative of performance level because it only measures the three aspects of pitching he has control over which should not suffer from much fluctuation at all, as Pizza Cutter showed not too long ago that these skills were some of the quickest to stabilize.
Controlling BABIP
At Fangraphs we occasionally call upon a statistic we titled xBABIP, which refers to what the BABIP of a pitcher can be expected to be given his percentage of line drives.  Dave Studeman found a few years back that the general range of BABIP could be predicted with very good accuracy by adding .12 to the LD%; if a pitcher surrendered 22.1% line drives his xBABIP would be ~.341.  Using this for predictive purposes would not be correct due to the fact that the general baseline for pitchers is .300.  What we can do is evaluate performance at a given time and attribute line drives to a rather high or low BABIP.  For instance, saying that Player B’s BABIP of .275 as of today primarily due to his ultra-low 14-15% LD rate would be correct; saying that it will continue like this would not.  The line drive percentage may change as the season goes on.  In summation, we can use something like this when evaluating the past for pitchers but not the future.
David Appelman showed not too long ago that, in 2007, 15% of flyballs fell in for hits, 24% of grounders turned into hits, and a whopping 73% of line drives also followed suit.  Due to this, the ideal xBABIP calculation would be .15(FB) + .24(GB) + .73(LD).
I have done studies here recently, and Jonathan Hale at Baseball Digest Daily has done others in the past as well, that show how aspects like velocity, movement, and location can all affect the BABIP of a given pitcher.  It also been shown, again by Studeman, that elite relievers have the ability to consistently post lower BABIPs than others.  More studies have shown that pitchers, if any, have very weak control over their BABIP but instead of deeming it control I would be more inclined to say that these pitchers are merely taking advantage of “cold spots.” 
If just 15% of flyballs result in hits and such a large number of line drives do, then we could intuitively expect someone with consistently low LD rates and higher FB rates to post lower BABIPs.  From a movement perspective, I found that those with above average vertical movement in different horizontal movement subgroupings post lower BABIPs as well.  Higher vertical movement usually correlates to flyballs, and voila, flyballs have the lowest percentage of hits.
This was just a recap of the three statistics and explanations pertaining to their usage.  Based on this, if we see someone like Carlos Zambrano, whose ERA consistently beats his FIP, based on consistently posting lower BABIPs, we could somewhat safely assume that he might not be controlling anything persay but rather taking advantage of all the aspects proven to result in lower BABIPs.  His controllable skills may not be as good as his ERA would suggest but movement, velocity, and location may have combined to greatly aid his efforts.

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.

Follow

Get every new post delivered to your Inbox.