The Hardball Times Dispatch

A splitter from Buchholz

Lucas Apostoleris — 2012-05-18T13:20:15+00:00

During Wednesday’s start against the Rays, Clay Buchholz began toying around with a new pitch in his arsenal: a splitter. Buchholz used it on an 0-2 pitch against the left-handed hitting Elliot Johnson, leading off the fifth inning.

Below is the spin chart from Buchholz’s most recent start, with his typical offerings—four-seam fastball: brown, two-seam fastball: grey, cutter: red, change-up: blue, curve: orange—as well as the new splitter, marked in purple.

Buchholz started using the splitter when he couldn’t get a feel for his change-up and used the pitcher’s count/bases empty situation to try it. You may have noticed on the spin chart another outlier (with a bit more velocity) that looks like another possible splitter, but apparently Buchholz threwonly the one splitter in that at-bat to Johnson (the other pitch was likely a slow sinker).

Big thanks to Brian MacPherson of the Providence Journal for answering my questions on Twitter and confirming my suspicions that Buchholz was throwing a split.

Read more great baseball stuff at The Hardball Times.

Strange Lowe

Harry Pavlidis — 2012-05-16T13:37:15+00:00

Derek Lowe tossed a shutout on May 15, 2012. Mark it down.

Three points of significance:

It was Lowe's first shutout since 2005 and the fourth of his career

He didn't strike out a single batter

He shelved his slider and cutter for the night

There's a connection between Lowe's pitch mix and whiff rate.

Check out the mix and whiff rates for Lowe's starts from 2008 through 2012, excluding the shutout in question (13,440 pitches).

pitch	% of pitches	miss/swing
Sinker	59	.12
Change	11	.26
Fastball	1	.13
Cutter	4	.16
Slider	25	.34

Note the swing-and-missy slider, thrown one of four pitches.

And May 15, 2012 (127 pitches).

pitch	% of pitches	miss/swing
Sinker	90	.02
Change	8	.00
Fastball	2	.00
Cutter	0	na
Slider	0	na

Strange, eh? Talk about pitching to contact.

Read more great baseball stuff at The Hardball Times.

A short story about two sinkers

Harry Pavlidis — 2012-05-01T08:21:15+00:00

Jason Hammel picked-up (and/or emphasized) a two-seam sinker this year for the Orioles. Across the continent, Brandon McCarthy—as we all know—likes sinkers, too.

While Hammel's has flown under the radar, it shouldn't for much longer. And perhaps McCarthy should look into the SI cover effect.

In 2012, there are 54 pitchers with 110 or more sinkers/two-seam fastballs thrown. That's 8437 total pitches, with a collective ground ball percentage of 55% and a whiff rate of .11 (that's misses divided by swings). We all understand that this a pitch a starter will often throw to contact, hence the low whiff rate, and the ball usually has some "sink" (hence the name) and thereby more grounders.

Hammel ranks 10th in whiff rate. Hammel's ground ball rate on his sinker is an impressive 69%, good enough for 6th place. Good job, Jason. There's a little bit of consternation to be found in Brandon's numbers. His ground ball percentage is just 37% and his whiff rate .05. Those put the SI feature subject 49th and 47th in these ranking, respectively. Combined, using these two metrics, you can put Hammel in the top five and McCarthy in the bottom five. Who had that picked before the season started?

Here are the full rankings. All pitch classifications are by the author and are shown on the Brooksbaseball.net Player Cards.

Pitcher	Sinkers	GB %	Whiff
Drabek, Kyle	136	74%	.240
Masterson, Justin	204	70%	.253
Niemann, Jeff	114	74%	.204
Wilson, C.J.	132	68%	.182
Hammel, Jason	143	69%	.164
Westbrook, Jake	261	77%	.140
Gonzalez, Gio	114	75%	.116
Kuroda, Hiroki	155	67%	.123
Cahill, Trevor	207	63%	.158
Detwiler, Ross	134	66%	.119
Saunders, Joe	154	66%	.111
Garcia, Jaime	147	61%	.127
Doubront, Felix	141	58%	.159
Gee, Dillon	124	57%	.164
Samardzija, Jeff	125	58%	.141
Wolf, Randy	129	54%	.167
Blackburn, Nick	121	54%	.167
Rodriguez, Wandy	154	50%	.212
Harrell, Lucas	248	63%	.103
Medlen, Kris	125	60%	.111
Greinke, Zack	139	62%	.102
Miley, Wade	123	52%	.154
Veras, Jose	112	47%	.171
Price, David	193	63%	.088
Harrison, Matt	148	53%	.122
Alvarez, Henderson	230	65%	.061
Pelfrey, Mike	124	59%	.092
Cueto, Johnny	162	58%	.096
Richard, Clayton	242	54%	.110
Arrieta, Jake	127	36%	.200
Porcello, Rick	192	59%	.088
Hughes, Jared	134	57%	.098
Lee, Cliff	118	64%	.042
Moyer, Jamie	117	57%	.087
Bedard, Erik	110	43%	.121
Marquis, Jason	165	61%	.049
Wainwright, Adam	120	50%	.102
Swarzak, Anthony	151	45%	.116
Lowe, Derek	268	55%	.058
Kendrick, Kyle	113	32%	.119
Holland, Derek	141	38%	.108
Capuano, Chris	185	40%	.099
Volstad, Christopher	151	48%	.083
Pavano, Carl	216	46%	.074
Weaver, Jered	211	41%	.086
Guthrie, Jeremy	112	52%	.020
Dolis, Rafael	125	50%	.038
Hunter, Tommy	134	44%	.066
Millwood, Kevin	163	39%	.081
Leake, Mike	128	31%	.094
Colon, Bartolo	252	48%	.036
McCarthy, Brandon	156	37%	.050
Haren, Dan	137	36%	.048
Hernandez, Felix	170	37%	.016

Read more great baseball stuff at The Hardball Times.

Shark attack

Harry Pavlidis — 2012-04-09T14:48:15+00:00

Shark
Spellcheck
F7
Jeffrey Alan Samardzija
Starting pitcher

Call him what you want, but Jeff Samardzija's impressive spring training carried over into the regular season. In just his sixth big league start, and first since the end of 2010, the former Notre Dame wide receiver was one Starlin Castro throwing error away from a complete game. It was his first time past the sixth inning.

He's been through many changes, but the raw athlete seems to have emerged fully formed. Dead set on starting in 2012, he forced his way into a crowded rotation situation and went from dark horse to middle of the rotation in a matter of weeks.

Going back to the start of 2011, Samardzija had lost some of the impressive velocity he had shown earlier in his career.

As his mechanics settled down and became more consistent, he gained about three mph and improved his numbers along the way.

2011 splits

         1st    2nd
Kp9      9.1    8.7
Bp9      5.9    3.9
K/B      1.5    2.2
ERA     3.48   2.23
babip   .255   .258

You may have noticed the jump in speed from Samardzija's first PITCHf/x outing of the year and his most recent. That first outing came in spring training, and was after good reports were already coming out of Mesa. Now, the lack of speed was no concern, too early in the year for that. But there was something in the PITCHf/x that jumped out and lent credence to the "different pitcher" meme that was starting to percolate.

Let's compare the last 119 pitches Samardzija threw last year (back to Sept. 9) to the 108 he threw against Washington on Easter Sunday.

	Sept. 2011	Easter 2012
Fastball	28 @ 97 mph	34 @ 96 mph
Sinker	52 @ 97	25 @ 96
Slider	26 @ 88	23 @ 86
Splitter	13 @ 89	20 @ 87
Cutter	n/a	6 @ 92

While Samardzija was a notch slower over the course of his start relative to last September's relief appearances, it was just a single mph and his fastest inning was the ninth, where he averaged over 97 mph with his four-seam.

There's a little more separation between the hard stuff and the slider/splitter now and he's added, or re-added, a solid 92 mph cutter. The presence of the clean cutter was the most noteworthy aspect of his Cactus League PITCHf/x data, and it was nice to see him to continue to mix it in. The fastball/sinker mix will vary for many pitchers, including Samardzija. Variation can come from the number of left- vs. right-handed batters (if the pitcher has a platoon preference) or the direction of the wind (if the pitcher plays in Wrigley Field). Sometimes it's a matter of feel or even the catcher.

Who knows,but he threw quality 97 mph sinkers in his Easter start while not leaning as heavily on them as he has sometimes in the past.

Read more great baseball stuff at The Hardball Times.

Umpire statistics

Dan Brooks — 2012-04-03T06:28:15+00:00

We (Harry and I) have released PITCHf/x based statistics for every umpire that has called a PITCHf/x enabled game, provided that they called enough pitches to accurately represent their called strikezone. We sincerely appreciate you taking the time to read this post before using our umpiring data.

Being an umpire is hard. It might be one of the hardest skills in baseball, and it sure doesn’t pay $20 million a year.

Umpires are also damn good at what they do.

But at least in the public domain, there’s been little systematic survey of umpiring. There are several reasons. First, because of the way people would naturally use the data without proper instruction, it would create unnecessary controversy. No one wants to be at the center of a media scandal involving umpiring. And so, consider this your proper instruction: If you use these data to rip umpires, consider yourself an idiot.

It’s true, there are good umpires and there are better umpires, but we’re aiming to show you what umpiring really looks like, not what umpiring fails to do. We want to paint a picture of each umpire's strengths and weaknesses, of their proclivities for calling particular pitches in particular ways. This is not an argument for computerized or mechanized strike zones; nothing could be further from the truth. Use this data wisely.

Second, umpiring is a difficult skill set to properly describe. To really do it well, you’ve got to have access to a nice database, have things properly classified, and have the right mathematical models to present the data. Here, we would like to extend a warm thanks to Dave Allen, who some five years ago showed us how to apply heat maps to PITCHf/x data in a presentation at Sportvision’s Summit using LOESS (Locally Weighted Scatterplot Smoothing) Regression.

Third, defining the strike zone is notoriously difficult. The problem is that using some average strikezone will probably not be good, because batters vary in height. However, the “easy” solution, which is to use the sz_top and sz_bot parameters from the Gameday data isn’t really a solution at all, because those parameters (as Mike Fast has convincingly shown) vary too wildly between games to give a good estimate of batter height on a per-pitch basis. Here, we’ve chosen to use an equation that looks at the average sz_top scores and weights those by a player’s height.

The strike zone is also technically a three-dimensional volume, and we’ve chosen to define it as a two-dimensional plane at the front of home plate (as we have elsewhere on the site). We realize this introduces error, but the alternative is simply too difficult to represent graphically. So we hope you forgive us here, and understand that this may slightly bias results.

We’re choosing to first present the data in two ways. The first is in a tabular form that reports not only hits (a pitch in the strike zone called a strike), misses (a strike called a ball), correct rejections (CRs, a ball called a ball) and false alarms (FAs, a ball called a strike), but also some psychometric measures of detection: d’ and c. Here’s the example from Angel Hernandez’s card:

While these last two require some explanation, the easiest way to think about them is that d’ represents discriminability (how well an umpire performs; larger is better) and that c represents how biased the umpire was in favor of hitters or pitchers (c<0 = pitcher friendly, c>0 = hitter friendly) on any particular pitch. These measures have not yet been re-normalized, but they will be, so that you get an idea of how friendly a particular umpire was relative to other umpires.

The second is a LOESS Heat Map for each batter handedness split by pitch type. This will give you the ability to tab through and see the differences in the strike zones called by each umpire in a more graphical way. Here’s Angel Hernandez’s strike zone:

We hope you enjoy these statistics and use them responsibly. Be an educated, informed fan who recognizes that ripping an umpire because he missed a call is often a selfish act with little justification. Often, looking at the bigger pattern of data can give you more answers than a single pitch.

Please feel free to direct any feedback to Dan Brooks (@brooksbaseball) or Harry Pavlidis (@harrypav) on Twitter, or by commenting below.

Read more great baseball stuff at The Hardball Times.

Things are trending up

Dan Brooks — 2012-03-28T14:21:15+00:00

About a year ago, sometime during John Lackey’s precipitous decline into worthlessness, I started paying attention to an odd phenomenon in which he would lose velocity during games:

Here, we can see a clear slope in fastballs thrown over the course of the game. It’s as if there’s some direct, measurable relationship between how many pitches he’s thrown and what the speed of the next pitch will be. The same is probably true of the breaking pitch, but it’s more difficult to see because the pitch mix (and speeds) are more variable.

Of course, people who look at PITCHf/x data will say that this is nothing particularly new—lots of pitchers do this—and they’d be right. But I wasn’t aware of any study describing this phenomenon in any detail. So, what I did was write a quick script that went through my database and found pitchers who had thrown at least 30 fastballs (sinkers and cutters included) in a game and had thrown 10 or more starts in either 2010 and 2011. It then fit a simple line to fastball speed by number of fastballs thrown to output a “fastball slope.”

It turns out John Lackey isn’t even the worst offender. For example, here’s a “.10” game by Jonathan Sanchez (who is the weakest link):

The “.10” here means that for every 10 fastballs, he loses a mph on his fastball over the course of the game.

“.10” isn’t as bad as it gets, though, Jonathan Sanchez is just the worst on average. Here’s a staggering “-.19” game:

Tommy Hunter starts the day blowin’ em away at nearly 96... and then barely tops 90 by the end!

Of course, the article is titled “Things are Trending Up.” How about games in which the opposite is true? For that, we need to turn to a subset of pitchers who actually gain speed over the course of their outing. Like, Justin Verlander, who does this with some sort of regularity. Here’s a “.14” game from Verlander last year:

Sure he’s got one mighty fastball in the early innings, but for the most part, he really gets cooking quite a bit later in the game, topping 100 several times.

You might ask yourself if this is a trait or just an oddity of strange games. So, I took those scores, averaged them, and then split them by year. Here’s what I found:

Each dot on this graph is a different pitcher over these two years. There’s a clear relationship between 2010 and 2011, suggesting that how a pitcher changes over the course of a game is a stable trait, likely a result of mechanics or physical attributes intrinsic to each athlete.

Read more great baseball stuff at The Hardball Times.

Similarity Scores: a very beta feature

Dan Brooks — 2012-03-23T15:16:15+00:00

Last night during the heart-stopping Syracuse-Wisconsin game, Harry and I were talking (okay, Harry was mostly watching his team win by the skin of its teeth) about ways to improve the Brooks Baseball player card system. We exchanged some data and are presenting the first of our "data-driven" search tools—pitcher similarity.

This feature is incredibly beta and likely to change over the next few weeks, but right now when you search for a player (let’s pick Josh Beckett), you will get a table listing other players in the "Josh Beckett Family," along with the "distance" to each player.

The scores are generated by comparing a vector of pitch speed, frequency, release, spin angle, and spin rate using MATLAB’s knnsearch algorithm to identify neighbors. Currently, we’re presenting the top five neighbors for each pitcher.

These are not perfect right now. We haven’t weighted the scores yet (that’s another conversation over basketball), so while we do a good job representing pitch mix and style, we’re not doing a very good capturing pitch speed yet.

There are also a few pitchers with hardly any comparables. Matt (@HouseOfTheBB) noted that neither Roy Halladay nor Mariano Rivera have a comparable pitcher at all!

Punch in a few pitchers, and let us know how our system is doing. Let us know over Twitter if we’ve really missed on someone. I'm @brooksbaseball, and Harry is @harrypav.

Read more great baseball stuff at The Hardball Times.

Danny Duffy PITCHf/x

Harry Pavlidis — 2012-03-16T19:52:15+00:00

Here's something new. Jeffrey Gross from THT Fantasy and I are exchanging "watch this guy" ideas—fantasy picks from one side and PITCHf/x based picks on the other. First up is Jeff's first breakout candidate for a cheap but valuable pitcher—Kansas City's Danny Duffy.

The PITCHf/x data we're discussing can be seen on Duffy's player card at Brooks Baseball. The data is from MLB Advanced Media ('BAM) but the pitch classifications are our own. Duffy pitched in the majors from May to September in 2011, ending his season after one start in that final month. It was his first year in the show, although there are some PITCHf/x data points from the 2009 Futures Games, the Arizona Fall League in 2010 and some Cactus League action from 2011.

Duffy's primary pitch is his four-seam fastball, followed by a curveball, change-up and the occasional two-seam fastball and slider.

Horizontal spin movement and vertical spin movement+gravity

vs LHH

vs RHH

shown from catcher's perspective

The black blobs at the top are four-seam fastballs, straightest flight and the backspin keeps gravity at bay fairly well. He's got a good hopping fastball, generates pop-ups and fly balls. To the right of the fastballs, running down and away from right-handed batters and rarely thrown to left-handed batters, are his two-seam fastballs or sinkers if you prefer. The blue dot change-ups have a similar tail to the sinkers but their reduced speed gives them separation vertically. The yellow curveballs are a group of over-the-toppers with a red cherry of the occasional slider on top.

Fastball (FA) 94 mph, 62% usage
Curveball (CU) 76, 19%
Change-up (CH) 84, 14%
Sinker (SI) 93, 3%
Slider (SL) 81, 2%

Duffy's fastball is above average in whiff rate, pop-up rate and flyball rate compared to other fastballs. His change-up is mostly average but his curveball doesn't miss too many bats but yields plenty of ground balls. At least in 2011, that is. He's yet to develop a swing-and-miss secondary pitch, and that's going to hold him back. You can't pitch on fastballs alone in a big league rotation. Or not for long.

If he's going to rely on his impressive power, he would benefit in the long run by further develop his two-seamer, which was ineffective in yielding worm killers in its limited use. With his high arm slot, a true sinker isn't likely to emerge. He may get more ground balls out of a cut fastball if he were to develop one.

Some trends to note before we hand the baton to Jeff for his Fantasy perspectives ... caveats are the small sample size and the PITCHf/x data from Kansas City tending to yield higher pitch speeds than most other parks.

Duffy's whiff rate trended downward as the season progressed (May: .25, June & July: .22, August: .15, September: .17)

Duffy's fastball speed declined in late July, recovering briefly before declining steadily through his last start. His final five starts: 94.4 mph, 94.1, 93.7, 93.4 and 92.7

Despite showing some signs of fatigue (speed and whiffs dropping), Duffy's highest pitch count came in that final start on Sept. 6, throwing 119 after not crossing 105 in his other starts

Jeff's thought was 2012 could be a breakout year for Duffy. I'd watch him with an eye on 2013.

Read more great baseball stuff at The Hardball Times.

Darvish will make pitch classification fun

Harry Pavlidis — 2012-03-08T19:30:15+00:00

Your definition of fun may vary. But Yu Darvish and his eight-pitch mix are going to make life interesting for catchers, hitters and even PITCHf/x analysts. Here's a picture from his Cactus League debut. The pitch in red was a strike three splitter to end an inning. The axes show movement during the flight to home plate from the catcher's perspective.

There's an 86 mph cutter that's got less drop and more hook than the other, faster, cutters. What's up with that? Looks like two change-ups on the left side of the change-up and splitter group. And you can clearly see the slow curveball.

This was a situation where game video and post-game interviews helped out. Dan Brooks looked at my original rough classifications and suggested some improvements. You can see those on Darvish's player card. The charts include his World Baseball Classic appearances, but you can filter the tables by year. We plan on providing yearly movement graphs in our next update to the site.

Read more great baseball stuff at The Hardball Times.