May 24, 2013
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A splitter from Buchholz (1)
A short story about two sinkers (4)
Umpire statistics (1)
Similarity Scores: a very beta feature (12)
Things are trending up (3)
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Hello. Ball-tracking technology—PITCHf/x and its offspring—has changed the way we look at the game of baseball. This is a place for our writers to share pitcher profiles and thumbnails, topical information about games, trades and anything else we can think of that ball-tracking technology helps us understand or enjoy.
Tuesday, June 26, 2012
In case you don't follow every last thing I put out on my twitter feed you may have missed these strange factoids.
Anthony Rizzo was traded from San Diego to Chicago for Andrew Cashner.
Rizzo's last Major League game was against the Cubs (0-3 with a walk) at Petco Park.
In his final at bat, Rizzo struck out swinging on a 1-2 slider from....Cashner.
Thursday, June 07, 2012
Against the division rival Rays, Ivan Nova spun an excellent game, going eight-plus innings, allowing just four hits (including none from the second batter of the game until the eighth inning) and one run while walking one, hitting one, and striking out five. He also got 12 balls on the ground out of 22 hit into play, a rate that tied his previous season high.
Something interesting about Nova is that, despite throwing his fastball with a cross-seam grip, he has, at least throughout his major league career, generated the results more typical of a sinking two-seam fastball. Josh Weinstock looked at this just about a year ago, and I’d like to come back to it today.
Nova’s fastball, which he throws from a ¾ arm slot, sinks a few more inches on its way to the plate than a typical right-handed four-seamer, but not as much as a two-seamer. As Nova said after a start against the Reds last month, keeping the ball low is the key for him, and the data certainly seem to back that up.
For this post, I’ve split up the vertical strike zone* into three equal regions, plus the areas below and above the zone. The MLB average data goes back to 2007, and only regular-season games were selected.
Groundballs/Ball In Play Low Out-Z Low In-Z Middle High In-Z High Out-Z Nova FA 65% 66% 55% 40% 26% MLB FA 58% 44% 35% 28% 25% MLB SI 70% 59% 50% 41% 35%
*The strike zone used is fixed (i.e., not adjusted for batter height) and runs from 1.75 feet off the ground to 3.4 feet off the ground, based on Mike Fast’s research here.
When Nova keeps his fastball low, it acts like a sinker, and a good one at that. But once you start getting into the upper third of the zone, the pitch (unsurprisingly) starts to get hit in the air more often.
Now, fastballs don’t always have to stay low to be effective. Back in 2009, Dave Allen simply and effectively showed us the relationship between whiffs and grounders and how they relate to pitch height. If Nova could consistently get swings and misses up in the zone, he would at least have that going for him. Unfortunately for Nova, batters seem to have little trouble making contact with his fastball wherever it is placed.
Swinging Strikes/Swings Low Out-Z Low In-Z Middle High In-Z High Out-Z Nova FA 24% 6% 4% 6% 26% MLB FA 17% 10% 12% 18% 33% MLB SI 23% 10% 8% 12% 25%
Sometimes on a two-strike pitch, you will see Russell Martin call and set up for a high fastball. That seems like a very dangerous proposition based on Nova’s inability to get the ball past hitters high in the zone. You’d need to get it way up out of the zone, and then it’s unlikely batters will chase it at all.
So, basically, Nova is left with a one-dimensional four-seam fastball that acts like a two-seamer. Since he’s very comfortable using his slider and power curve in the dirt to pick up strikeouts, it is important for him to keep his fastball low and avoid loud contact.
Monday, May 28, 2012
Buster Olney had this to say about Roy Halladay's pitch mix:
I have no idea what he's talking about. First and foremost, Doc doesn't throw four-seam fastballs. He cuts and sinks the ball, so you can maybe say he throws two different fastballs.
But anyway, I can't find anything in Doc's numbers to match up with Buster's tweet. These are my pitch IDs, and you can see more about Doc based on my tags on his player card.
Friday, May 18, 2012
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.
Thursday, May 17, 2012
The recent injuries to Mariano Rivera and David Robertson have caused something of a stir at the back end of the Yankees bullpen. Rafael Soriano, once the nominal “seventh-inning guy,” has taken hold of the closer’s role, meaning that the Yankees have needed to piece together their setup duties.
Fortunately for New York, they have gotten big contributions from relievers Cory Wade and Boone Logan, who have combined to allow eight runs and strike out 44 hitters over their 32.2 innings this year and are "climbing the ladder," so to speak. Wade is interesting in his own right, but let’s focus on Logan for right now.
So far this season, Logan is striking out batters in 35 percent of their plate appearances against him, placing him ninth among big league relievers and well above his career norms in that category. The reasonable next question is: what’s up? How has Boone Logan become so good? The answer, it appears, has mostly to do with his low-80s slider, his breaking pitch of choice. First, observe his pitch frequencies since he came to the Yankees in 2010:
FA SI SL CH 2010 45% 22% 26% 6% 2011 47% 15% 36% 2% 2012 38% 10% 48% 4%
Sliders have been on the increase for Logan, and this year, he’s actually thrown more of them than four and two-seam fastballs combined. And that looks like it’s a good thing: Logan’s slider has been his best pitch, by far.
Whiff% Called% Ball% 2010 49% 11% 36% 2011 48% 19% 32% 2012 59% 18% 35%
whiff% is whiffs per swing; called% is called strikes per pitch; ball% is balls per pitch
Anything over ~40 percent whiffs per swing is a good strikeout pitch, and Logan has taken it to the next level this season. His slider is also thrown for a ball less than the average slider is, and he can drop it in for a called strike early in the count if he wants to. The combination of throwing more sliders and getting more whiffs when he throws them is the driving force behind Logan’s success this year.
Wednesday, May 16, 2012
Derek Lowe tossed a shutout on May 15, 2012. Mark it down.
Three points of significance:
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Tuesday, May 01, 2012
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.
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Monday, April 09, 2012
Jeffrey Alan Samardzija
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.
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Tuesday, April 03, 2012
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.
Wednesday, March 28, 2012
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.