May 23, 2013
Now Available for 2012 THT Essentials: Now available
THT's latest book Most Recent Comments
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) 
Rich Barbieri
John Barten Kyle Boddy Brian Borawski James Gentile Matt Hunter Frank Jackson Chris Jaffe Brad Johnson Jason Linden Dan Lependorf Bruce Markusen Jeff Moore Greg Simons Scott Spratt Dave Studeman Shane Tourtellotte Steve Treder And here's the full roster.
Or you can search by:
 All content on this site (including text, graphs, and any other original works), unless otherwise noted, is licensed under a Creative Commons License. |
Wednesday, February 29, 2012How will closing impact Brett Myers’ fastball?How Brett Myers will do as a closer and how valuable he is to the Astros in that role is not the question for this space. Just the fastballs. Let's take a look at some quick-and-dirty numbers (four- and two-seam fastballs all together; age determined by season year minus birth year—neither precise nor seasonal). If you go by the fastballs Myers threw as a closer in 2007, it seems starting impacted Myers more than expected, and now he's lost a wee bit more fastball than you'd expect, mostly with a big dip last year. There are no park or weather adjustments, so this is back-of-the-envelope quality. Let's take a look at some quick-and-dirty numbers (four- and two-seam fastballs all together; age determined by season year minus birth year—neither precise nor seasonal). If you go by the fastballs Myers threw as a closer in 2007, it seems starting impacted Myers more than expected, and now he's lost a wee bit more fastball than you'd expect, mostly with a big dip last year. There are no park or weather adjustments, so this is back-of-the-envelope quality.  This chart shows a gap that's around the typical 1.5 mph gap between starter and reliver velocity that Mike Fast documented here at The Hardball Times in 2010. The age effect from the back of the envelope is a little light, considering Josh Kalk found pitchers lose one or two mph a year once they are in their early 30s. Josh's study was published here in 2008, so the data he had available was limited. The smaller drops in speed we're looking at here are most likely due to selection bias (pitchers who lose velocity don't come back next year, etc.). In both cases, we're in the ballpark with the back-of-the-envelope approach. Put it all together, use your gut, and see if you agree —Myers will probably be around 92 and touching 94. Reasonable guess? This chart shows a gap that's around the typical 1.5 mph gap between starter and reliver velocity that Mike Fast documented here at The Hardball Times in 2010. The age effect from the back of the envelope is a little light, considering Josh Kalk found pitchers lose one or two mph a year once they are in their early 30s. Josh's study was published here in 2008, so the data he had available was limited. The smaller drops in speed we're looking at here are most likely due to selection bias (pitchers who lose velocity don't come back next year, etc.). In both cases, we're in the ballpark with the back-of-the-envelope approach. Put it all together, use your gut, and see if you agree —Myers will probably be around 92 and touching 94. Reasonable guess? Monday, February 27, 2012McCarthy declassifiedPitch classification is not an exact science. It requires a lot of review and rechecks over time to ensure no funny IDs have snuck in and that a pitcher's arsenel is accurately reflected. That last point can be the most difficult, especially if a pitcher changes things over time. And then there are changes in arm angles. So, what's an PITCHf/x junkie to do? Well, the Brooks Baseball player cards are a start, as they've given anyone with the urge to review data the chance to do so, even if that's not what they're setting out to do. Take a recent effort by Adam Foster of Project Prospect, mix in a little Twitter and, voila, you can polish up some pitch classifications with relative ease. Oh, that little Twitter put in the blender? It is especially helpful if the pitcher in question is active in the micro-blogging space. And an ESPN The Magazine feature rounds it all out. Brandon McCarthy's arm slot changes were reviewed last April by Kyle Boddy right here at The Hardball Times. The feature on both Mr. and Mrs. McCarthy ("Chicks dig the ground ball" is declared on the cover) talks in detail about his injuries and overall progression. McCarthy has gone from an overhand fastball/curveball specialist to a three-quarter cutter/sinker guy. He's in the process of dropping his four-seam fastball altogether and may not throw it at all in 2011, according to his remarks on Twitter. He also confirmed he experimented with a slider as a Ranger (a very inconsistent pitch, so I wasn't sure if it was a slurve or what, but a tweet or two cleared that up). One of McCarthy's latest pitch-related tweets referred to his desire to settle on a change-up. He's experimented with a few over the years, and that can be seen if you break down his PITCHf/x data game by game. He also threw two-seam fastballs before really developing the true sinker he throws now. They were tailers and came and went over the years. This may not be everyone's favorite picture from the article, but it's mine. It shows his two-seam grip used for his sinker. I'd like to see another one of the two-seam cutter he throws. But I guess more pictures of Amanda McCarthy were preferred by the ESPN editors.  ESPN photo of McCarthy's two-seamer That's hot. You can find a bunch of charts on McCarthy on our Brooksbaseball.net player card. It will be updated as the 2012 season progresses, and we'll all be watching Twitter for news on his latest change-up. Tuesday, February 21, 2012A.J.‘s curve: missing bats, missing the zoneAfter much anticipation, the trade that sent A.J. Burnett from the Yankees to the Pirates was finally completed on Sunday. Burnett had a tumultuous three seasons in the Bronx, with a 4.79 ERA and 4.5 walks/hit batters per nine over 584 regular-season innings. His signature pitch is an 83 mph spike-curveball with exceptional movement. (Harry Pavlidis mentioned it here a few weeks ago—it’s not quite as nasty as Craig Kimbrel’s). Because he does a consistent job of keeping it below the strike zone, hitters don’t make much contact with the pitch when they swing at it, but it’s taken for a ball if when they don’t. The two tables below are for whiffs per swing and balls per pitch, minimum 2,500 pitches thrown dating back to 2007. Rank Pitcher Pitch # Whiff% 1 A.J. Burnett Curveball 4575 43.9% 2 Cole Hamels Changeup 4158 43.8% 3 Rich Harden Changeup 3053 43.4% 4 Francisco Liriano Slider 2632 43.4% 5 Tim Lincecum Changeup 2946 42.9% Rank Pitcher Pitch # Ball% 1 Randy Wolf Curveball 2562 44.2% 2 Gio Gonazlez Curveball 2663 43.2% 3 A.J. Burnett Curveball 4575 43.1% 4 Justin Verlander Curveball 3244 42.3% 5 Kevin Millwood Fastball 2718 41.2% Pitch labels are THT's. Friday, February 10, 2012None nastier than KimbrelCraig Kimbrel throws a nasty breaking ball. He grips it like a spike slider (maybe) and the ball moves like a curveball (sort of). It's a relatively short curveball, but one moving upwards of 87 mph when it leaves Kimbrel's hand. The combination of speed and drop are unparalleled in the major leagues today.  Checkout the discussion and a picture of the grip in the forum at Brooks Baseball. The label is not as important as the key characteristics—speed and drop—when looking for comparisons to Kimbrel's offering. The first comparison is flame-thrower Henry Rodriguez. Rodriguez threw a very similar breaking ball to Kimbrel's in 2011, averaging roughly the same drop (40 inches) but not as fast (84 mph).  Close, but no cigar. As a matter of fact, if you look for any pitch that averages more than 82 mph and dropped more than 38 inches on the way to home plate, you'd fine a decent list but no one in Kimbrel's exact neighborhood. If you go back past 2011, some sliders show-up (Brett Anderson, Billy Wagner), but otherwise this is a list of curveballs and their throwers. And, as it turns out, Mitch Talbot appears to be our co-runner up, if you sort by speed. Fast (82+ mph) and dropping (38+ in.), average values 2011
Note: all pitch classifications are our own and do not come from Gameday or BIS feeds Friday, February 03, 2012Yes, we actually classified every pitchLong term PITCHf/x data has always been difficult to find online. There are several existing sources available: Fangraphs has some of it, but not everything you might want (I’m sure they will tomorrow, just for that). Texas Leaguer has had a fantastic tool up for quite some time now, but still, there are places it lacks functionality. Josh Kalk used to have a wonderful website, but he moved on to the Rays. BrooksBaseball has never really had seasonal data per se, despite having data that spans each season. We think that, generally, there are several reasons the data has been difficult to find in a long-term format. First, there is the technical limitation: The PITCHf/x dataset is large—millions of pitches. This means that dynamic solutions—most PitchFX systems are dynamic—must have very good caching systems, well written databases, and powerful hosting solutions. But beyond raw computing, which we can solve using some combination of duct tape and Moore’s law, there are really two critical issues that are unfortunately intertwined. The first of these issues is a data quality limitation. The PITCHf/x data is beautiful and we have nothing but exemplary things to say about the people who both collect it and make it free to access. I hope I do not overstep my bounds when I say that Cory and his team at MLBAM and Greg and his team at Sportvision have probably contributed more to baseball research simply through the availability of this data than all but the most accomplished Sabermetricians. That said, there are simply issues with the data, most of which are due to park-specific camera quirks that make individual games more grokkable than complete months or seasons (see Chris Carpenter’s data in the World Series, both in Texas and in St. Louis, for an example). The second of these issues relates to pitch classification. This has become progressively less and less of an issue as the brilliant minds at BAM have worked to improve their classification algorithms, which have gone from mediocre to damn good in a very short amount of time. Yet still, if you’re going to average across a set of data to say something about a set of labels, the quality of what you report depends heavily on your data labels. These last two issues are related in the following way: Chris Carpenter’s data includes park specific errors in both St. Louis and Texas, so, it is classified by the automatic algorithm differently in St. Louis and Texas despite being internally consistent. Therefore, quality issues propagate through various parts of the system. So, you really want a very qualified human to do the tagging. But, good luck convincing THT's Harry Pavlidis or Lucas Apostoleris to tag three and a half million pitches, because that would be insane. What’s that you say? They’ve actually done that?! By that, I mean individually tagged every pitch. This isn’t a very efficient solution, but it escapes the problems above by putting a human hand on the classification problem. When the cameras capture internally consistent data with park specific quirks, Harry can find adjust for those quirks and tag the pitches correctly. The raw numbers in the data aren’t changed, but the labels are—solving at least part of the problem that exists in the dataset. It’s not a perfect solution, but it allows us to present you with an enormous database of properly tagged, seasonal PITCHf/x data. Basically, this project started in the following way. Harry asked me to help write an automatic algorithm for detecting clusters of bad classifications, because humans make different kinds of mistakes than computers. Whereas a computer might misclassify Chris Carpenter because of Park Specific Camera Quirks, a human might misclassify CC Sabathia’s rare curveball as a slider because he didn’t think they were two pitches when looking at individual game datasets, though they become apparent when all of the data is shown at once. I told him not to worry about writing an algorithm to find these problems, because the internet is beautiful. He asked if I was drunk. I said no. And then, we arrived at the solution: put it online. Because no algorithm was going to be as good as human eyes, and people were going to want to see this. This is a little script that displays seasonal PITCHf/x data using labels tagged by the THT writers. It includes over a dozen different ways to cut and slice the data, with some of the most common PITCHf/x plots (e.g., Horizontal Movement x Speed) and some uncommon ones (e.g., fancy trajectory plots with nice art). For example, let’s take a pitcher: Clay Buchholz. Click on the name, or you can find him by navigating to BrooksBaseball.net and typing “Buchholz” in the search bar. Let’s look a little at his changeup, which has always been a stellar pitch.  On the above chart that shows pitches separated by Horizontal Movement and Speed, the changeup (CH) is highlighted here in blue—about 80mph with slight horizontal fade. A Trajectory and Movement table at the top of the page tells us 80.99 miles per hour on average, to be exact.  You might be interested to know how much it drops on its way to the plate. This graph shows that—with gravity included— the Buchholz changeup drops off the table over 2 feet—the Trajectory and Movement table, again, tells us 26.01 inches! One thing that’s incredible about the Buchholz's changeup is how often it generates whiffs. The Pitch Outcomes table tells us that batters whiff 22.9 percent of the time when he throws it, a very high number. And the Sabermetric Outcomes table tells us that the Whiff/Swing on the changeup is 45.9 percent! That number is even higher for right-handed hitters (52.25 percent!) and interestingly higher still on 0-strike counts. When batters are expecting fastball and Clay throws the Change, there have been 91 recorded swings, and 59 of those—64.84 percent!!—have been whiffs. What’s part of what makes it so good? The trajectory of the pitches nearly overlap until the very last part of the pitch trajectory, making it almost impossible to distinguish a change from a fastball.  And even when batters do put it in play, the pitch generates ground balls at a 44.7 percent clip. Despite this dominance, it looks like he’s throwing fewer changeups as he moves to throwing more and more of his newfound cutter.  OK, so that wasn’t a very useful discussion—you all know Clay Buchholz has a great changeup. It was just meant to demonstrate some of the power of this little app. Now that you’ve seen it in action, your mission, if you choose to accept us: Help us validate. How: 1) Run around like children in Wonka’s chocolate factory and consume as much data as possible (do not drink directly from the waterfall). 2) Using your knowledge of pitchers that you watch every day, let us know what looks wrong by starting threads on the BrooksBaseball.net Forums (we’ll have a thread for each pitcher, there are examples already there). 3) Use these graphs and tables however you want in any of your favorite blogs. Consult legal counsel first, and sign this waiver releasing us from liability. 4) Help us by sponsoring your favorite pitcher or two if you think what we’ve done is cool. This is an important step. Just like on Baseball Reference, you can add your own witty message to appear every time someone pulls up a card. We really, really hope you enjoy this—we think it will be a great addition to the baseball resources on the Internet. Please don’t hesitate to contact us by leaving comments on this article, by leaving messages on the forums, or dropping us an email. In addition to fantastic work by Harry and Lucas, a tip of the hat goes to Dustin Kikuchi. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||