Note: This article was originally published at the Statistically Speaking blog at on January 14, 2008.  Since the site is defunct and its articles are no longer available on the web, I am re-publishing the article here.

Many of you are hopefully familiar with the PITCHf/x system and at least some of the data and analysis that have been produced on the subject over the past year, but it may be completely new to some of you. In either case, I thought it would be helpful to provide an introduction and tutorial on the information that is available. I’ll point toward some existing resources and try to fill in some of the gaps. I’ve divided this primer into sections so you can easily skip to the parts that interest you.

  1. What is PITCHf/x?
  2. How do I get and use the data?
  3. Where can I find resources?
  4. How do I identify pitch types?
  5. How do I interpret graphs?
  6. Is the data reliable?
  7. Where can I go for further discussion and study?

1. What is PITCHf/x?

PITCHf/x is a system developed by Sportvision and introduced in Major League Baseball during the 2006 playoffs. It uses two cameras to record the position of the pitched baseball during its flight from the pitcher’s hand to home plate, and various parameters are measured and calculated to describe the trajectory and speed of each pitch. It was instituted in most ballparks throughout MLB as the 2007 season progressed, such that we have PITCHf/x data for a little over a third of the games from 2007. MLBAM used the PITCHf/x data in their Enhanced Gameday application and also made the data freely available for downloading and research.

In some ways, PITCHf/x is a bridge between scouting and analysis, giving us an objective window into the batter-pitcher matchup at a level we’ve never seen before. In 2008, the system should be installed in every major-league ballpark, and we will hopefully have complete detail for every pitch, although MLB has not committed to whether all the data will continue to be freely available in the future.

2. How do I get and use the data?

If you want to look at the XML data from a single game, you can go to the MLB website and browse through the files. Data is organized by year, month, day, and game. Within each game directory are a number of subdirectories containing the data in XML format. If you want to see the detailed pitch information within the game context, I suggest looking at the files in the inning subdirectory. If you want to see all the pitch information for a particular pitcher, you can go the pbp/pitchers subdirectory, but you need to know Elias playerID for your pitcher of interest. If you want to know what the various XML pitch data fields mean, read my glossary.

If you want to manipulate and analyze a single game’s worth of data, you can download and import the XML files into a Microsoft Excel spreadsheet. Dr. Alan Nathan has laid out the steps for you at his Physics of Baseball site.

If you want to get a little more hardcore, you can download the XML data for every game in the 2007 season. Using Perl scripts adapted from Joseph Adler’s Baseball Hacks, I downloaded the data and parsed it into a MySQL database. I’ve outlined the steps needed for you to do this yourself and shared the Perl code to give you a head start. (I’m not aware of anyone who’s gotten the Perl-to-MySQL path working on a Mac, so if you have, please drop me a line.)

3. Where can I find resources?

Probably the most popular and valuable PITCHf/x resource on the web is Josh Kalk’s collection of player cards. Josh has classified every pitch as either a fastball, sinker, cutter, splitter, changeup, slider, curve, or knuckleball using a clustering algorithm and made graphs of pitch speed, movement, and release point for every pitcher with at least 100 pitches recorded by PITCHf/x. Strike zone charts are available for hitters. This is a great resource that reminds me in some ways of Wikipedia: the depth, breadth, and accuracy of the information is amazing, doubly so since it’s free, but the accuracy isn’t perfect, and it’s worth keeping that in mind. Stuff that looks quirky to you may in fact be quirky. (Felix Hernandez does not throw a 100-mph splitter.)

Josh Kalk has also developed a PITCHf/x tool that allows you to query his database for a specific subset of pitches and plot their strike zone location.

The Hardball Times published a pitch identification tutorial by John Walsh that is a good introduction to the general PITCHf/x topic as well as the specific topic of pitch identification.

Dr. Alan Nathan’s Physics of Baseball site has a lot of interesting resources, including some PITCHf/x-related material.

4. How do I identify pitch types?

Some people are good at identifying pitch types while at the ballpark or from the center field TV camera view. That was a splitter. That was a sinker. That was a slider. Etc. I am not one of those people. If you are not one of those people either, PITCHf/x was made for you. Even if you are one of those people, PITCHf/x can be a useful resource for learning about how different pitches move.

A pitcher’s fastest pitch is usually a four-seam fastball. A typical major-league fastball is around 90 mph, many a little faster, some a little slower. The fastball from a right-handed pitcher breaks in toward a right-handed hitter. Pitches from a lefty move the opposite way; a fastball from a lefty breaks away from a right-handed hitter. I’ll describe the movement for pitches from a righty and you can flip the orientation if you want to know how a similar pitch from a lefty would behave.

Pitchers throw variations of the fastball by changing the grip on the baseball or parts of their motion and delivery. The most popular variation is a two-seam fastball, which often thrown a couple mph slower and breaks in more and drops more to a right-handed hitter from a right-handed pitcher than the four-seamer. The cut fastball is also thrown a few mph slower than the four-seamer and breaks away a little from a right-handed hitter, if it breaks at all.

The most popular off-speed pitch is the changeup, which is typically thrown 7-10 mph slower than a pitcher’s fastball. It usually has a similar break to the fastball, in toward a right-handed hitter. Some pitchers employ a grip on their changeup to impart additional movement, usually causing the pitch to break in more and drop more to a right-handed hitter. The split-finger fastball acts much like a changeup except that its velocity and movement are usually somewhere between the fastball and changeup.

Breaking balls include the slider and curveball. The slider is usually thrown at the same speed as the changeup or sometimes a few mph faster. The movement on the slider can vary quite a bit from one pitcher to another. Some sliders move like a cutter, with hardly any left-right break. Other sliders move more like a curveball, which breaks away from a right-handed hitter and down. The curveball is the slowest pitch, thrown in the 65-80 mph range in major league baseball.

The knuckleball is a special case in major league baseball these days. As far as I know, there were only two regular practitioners of the pitch in the majors last year: Tim Wakefield and Charlie Haeger. The pitch is thrown with very little spin such that the airstream interaction with the seam orientation causes the baseball to move unpredictably. Wakefield and Haeger throw the knuckleball about 65-70 mph.

Of course, there are a number of variations and combinations of the above pitches and specialty pitches like the screwball and gyroball and even the 50-mph Orlando Hernandez eephus pitch.

Here is a plot showing the typical vertical and horizontal spin deflection (a.k.a.”break”) of typical pitches from a right-handed pitcher, as viewed from the catcher’s point of view. A mirror image would give you the plot for left-handed pitcher. You can use this as a key for interpreting some of the graphs on Josh Kalk’s player cards or for understanding the spin-induced movement on various types of pitches.

5. How do I interpret graphs?

PITCHf/x analysis and research is a promising field with wide application and broad interest, and there are a number of people who have made important contributions in the first year of analysis. As a result, there are many different formats for presenting the results. I’ll summarize and explain a few of them here and give a more detailed explanation of some of the graphs that I use most frequently.

The most common plots presented by other PITCHf/x researchers include information about the speed and spin-induced deflection of pitches. To the best of my knowledge, Joe Sheehan was the first to produce these plots, showing speed on the vertical axis and the two components of spin deflection as two sets of points on the horizontal axis. Joe hasn’t done much pitch classification work recently, but he deserves a nod as the groundbreaker in that field.

Something you’re more likely to encounter these days is a plot from John Walsh, such as those contained in his pitch identification tutorial. He plots vertical “movement” versus horizontal “movement”, where movement refers to the spin-induced deflection, and indicates speed by color-coding the points on the graph.

Most common of all are the plots from Josh Kalk’s pitcher cards, particularly the plots of vertical “break” versus horizontal “break”. These are similar to John Walsh’s plots except that instead of color-coding for speed, the points on the graph are color-coded by pitch type. Josh has separate graphs that plot speed versus horizontal break and speed versus vertical break, reminiscent of the original Sheehan plots. Josh’s player cards also contain information on release point, which is the height and left-right position of the pitch measured 50 feet from home plate, which is soon after the actual release by the pitcher.

In the past I have presented graphs similar to those of Sheehan and Kalk, but more recently I’ve adopted a graph from Alan Nathan as my mainstay. It is a polar plot, with the speed of the pitch on the radial axis. The faster the pitch, the farther from the center. The slower the pitch, the closer to the center. The angle is the angle of the Magnus force, which is the force that cause the ball to break. Curveballs break down, so they’ll be in the bottom part of the graph. Sliders break away from a right-handed hitter, so they’ll be on the left side of the graph. The Magnus force of a fastball pushes the ball up, causing it to drop less than it normally would due to gravity alone, so the fastballs will be on the top part of the graph.

I’ve also started showing a graph of what I call “late break”, which is a combination of the effects of spin deflection and gravity as well as the speed of the pitch. The goal is to show something close to what the hitter perceives as the break or movement of the pitch. I calculate the deflection of the pitch due to two forces, spin and gravity, in the last 0.25 seconds of its trajectory before it crosses the plate, an idea I got from Tom Tango. I chose a quarter second because that’s roughly the reaction time of a batter executing a swing. I chose to include the effect of gravity because I believe that more accurately reflects what hitters see. Hitters don’t attempt to hit a gravity-less pitch; they attempt to hit a pitch that’s being affected by gravity and being deflected by spin.

6. Is the data reliable?

Whenever you are viewing or analyzing PITCHf/x data, it’s worth keeping in my mind that 2007 was a work in progress for Sportvision and MLBAM. They instituted the system in only a handful of stadiums to begin the year and added more systems in other stadiums, particularly in the second half of the year, as they gained confidence in the performance and accuracy of PITCHf/x. They experimented with measuring the initial point of the pitch trajectory at various distances from home plate, finally settling on 50 feet. They worked to identify and remove spurious data that was collected by the system. They trained operators who did such things as identifying the beginning of play in each half inning and setting the top and bottom of each batter’s strike zone in the system. In addition, the camera systems were sometimes recalibrated, possibly at the beginning of each home stand.

So it’s a bit naive to assume the data we have is a perfectly objective, accurate, and precise measure of each pitch. In most cases, it’s pretty close (within an inch or two) and good enough–much better than anything we’ve ever had before! But what are some of the sources of error to watch out for?

The data for some pitches is missing. In some cases this is obvious, when a stadium doesn’t have a system for part of the year, for example. Other times, portions of games will be missing, or even just individual pitches. Perhaps the operator may not have turned the system on for the first pitch of the inning, or MLB/Sportvision retroactively discovered an error in their data and removed it. We are also missing PITCHf/x data for all hit batsmen during the regular season.

There is erroneous data–spurious or mis-measured pitches. For example, the data may say that a pitch was released from ten feet off the ground, and unless Gumby has caught on with a major league team, I doubt any pitcher can reach that high. There are a number of 30-40 mph pitches that are recorded in the data that do not appear to be realistic. It’s been suggested that some of these may have been the system inadvertently recording other non-pitch throws of the baseball between the mound and the plate as a pitch.

There are indications of park and/or camera system bias. Data from Seattle and Toronto indicate pitch speeds that seem a few mph higher than they should be. Look how hard Dustin McGowan and Felix Hernandez are shown to have thrown on average. These guys are hard throwers, but not that hard. Similarly, the system at Fenway Park seems to have underestimated pitch speeds and otherwise collected strange data.

There are also altitude and temperature effects. In this case, the data collected by PITCHf/x may be completely correct, but our interpretation of the data has to take into account that air density affects how a pitched baseball moves. A curveball thrown in the thin air of Denver, Colorado won’t break as much as the same curveball thrown in the pea soup at sea level.

7. Where can I go for further discussion and study?

If you want to learn more about the details of Sportvision’s PITCHf/x system and MLB’s implementation, read this article by Mark Newman of

If you want to learn more about the physics of pitched baseballs, Alan Nathan is your man, and his freshman physics lectures on the Physics of Baseball at the University of Illinois are an excellent place to begin. You might also find these articles by Dave Baldwin and Terry Bahill helpful.

If you want to learn more about pitch classification methods, as I mentioned earlier, John Walsh’s pitch identification tutorial is a good place to start. You may also want to consult my survey of the topic, which contains a particular in-depth emphasis on my own work on the subject.

If you want to discuss PITCHf/x with other sabermetricians, I recommend The BOOK Blog run by Tom Tango.

If you want to learn about systematic error correction for the PITCHf/x data set, read Josh Kalk’s posts at his blog, and this post by Ike Hall, including comments by Alan Nathan.

If you want to learn about pitch sequencing analysis, Joe P. Sheehan’s Command Post at Baseball Analysts is a good resource, including these posts on the topic. Joe Sheehan’s writing is an excellent resource on a number of diverse PITCHf/x topics. Although I only listed him here under pitch sequencing, it’s well worth going through his archives on many other topics if you are interested in learning about PITCHf/x.

Dan Fox’s work is another great PITCHf/x resource, although, like Joe, I couldn’t find a neat category to file him under. He’s covered everything from pitch classification to measures of strike zone judgment.

If you want to learn about pitching styles, strategies, and repertoires throughout baseball history, I highly recommend reading the Neyer/James Guide to Pitchers, published in 2003. Rob Neyer has updates to the book at his blog.


As he does every year, Tom Tango is compiling the Fans’ Scouting Report. He is seeking help from baseball fans to rate the defensive abilities of the players they have watched this season.

Baseball’s fans are very perceptive. Take a large group of them, and they can pick out the final standings with the best of them. They can forecast the performance of players as well as those guys with rather sophisticated forecasting engines. Bill James, in one of his later Abstracts, had the fans vote in for the ranking of the best to worst players by position. And they did a darn good job.

There is an enormous amount of untapped knowledge here. There are 70 million fans at MLB parks every year, and a whole lot more watching the games on television. When I was a teenager, I had no problem picking out Tim Wallach as a great fielding 3B, a few years before MLB coaches did so. And, judging by the quantity of non-stop standing ovations Wallach received, I wasn’t the only one in Montreal whose eyes did not deceive him. Rondel White, Marquis Grissom, Larry Walker, Andre Dawson, Hubie Brooks, Ellis Valentine. We don’t need stats to tell us which of these does not belong.

What I would like to do now is tap that pool of talent. I want you to tell me what your eyes see. I want you to tell me how good or bad a fielder is. Go down, and start selecting the team(s) that you watch all the time. For any player that you’ve seen play in at least 10 games in 2009, I want you to judge his performance in 7 specific fielding categories.

If you’ve watched a lot of baseball in 2009, or at least enough to meet the guidelines, please participate in compiling this valuable resource.

I have a couple scouting reports up at the Hardball Times based on data from PITCHf/x, one on Scott Kazmir and the other on Cole Hamels.

I also highly recommend Matt Lentzner’s article at THT on his theory of pitching mechanics.

I’ve been doing a few other things behind the scenes that haven’t seen publication here or at THT, but I’m still involved in baseball analysis and writing, in case you were wondering.  You can look for my article on Cliff Lee in the upcoming Hardball Times Annual 2009, which will be available November 30.

As I write this, the All-Star game goes to the 15th inning, and I go to bed. I’ll check the box score in the morning.

Over at the Hardball Times, I take a look at the bases loaded, two out, bottom of the ninth, one run lead situation going back to 1956.

It’s mostly just a fun historical research article, with some numbers gathered from Retrosheet and the Baseball-Reference Play Index and a dash of PITCHf/x at the end for flavor.

Finally! The new PITCHf/x article archive, now powered by a back-end database thanks to Bryan Donovan, is available at the Hardball Times.

You can read all about it here.

EDIT: I am in the process of working out some data corrections to the PITCHf/x data, and I have updated this post with corrected pitch speed data.

Dave Cameron wrote a piece yesterday at Fangraphs about Justin Verlander’s fastball speed (hat tip to Tango). I love both Dave Cameron’s work and Fangraphs. Fangraphs is quickly becoming one of my very favorite sites on the InterWebs. However, something about Dave’s post today struck me a little funny, and I decided to investigate further.

Here are a couple excerpts from what Dave said about Verlander’s fastball:

One of the first things we noticed using that data this season was that Justin Verlander’s fastball disappeared in April. He was throwing 91-92 instead of his usual 94-95, and his performance suffered as a result.

For all the talk of guys learning how to pitch without their best stuff, Justin Verlander is clearly a better pitcher when he’s throwing 95 instead of 92.

It bugged me because I wasn’t sure it was true, either that Verlander’s fastball speed was improving as Dave said it was, or that there was a correlation between his fastball speed and performance.

So I decided to dig into the PITCHf/x data for Verlander. Here’s what we see about his pitch speeds going back to the 2006 playoffs.

During the 2006 playoffs his average fastball speed was 94.7 mph. In 2007, PITCHf/x recorded his average fastball speed at 95.0, although the period from which we have most of our PITCHf/x data is after the All-Star break.

In 2008, his average fastball speed has been 94.1, and the trend matches fairly well with that which Dave describes seeing in the BIS data. However, I’m not sure I see as direct a correlation between fastball speed and performance for Verlander as Dave Cameron does.

To look a little deeper, I calculated Verlander’s average fastball speed for each of his starts for which we have PITCHf/x data. I decided to use the Bill James pitching game score as the measure of performance, and I grabbed that data from Baseball Reference. (Fangraphs! Baseball-Reference! Is there any better time in history to be a baseball fan?) Comparing the game score for each of Verlander’s starts to his average fastball speed, there appears to be a correlation, but a fairly weak one. (The R squared is 0.09.)

I guess you could say he hasn’t pitched any great games with a fastball in the 92-93 mph range, although having a faster fastball does not appear to be a firm guarantee of success. Mostly at this point, I am skeptical of our ability to ferret strong conclusions out of a data set where the sources of error are on the same magnitude as the effects we are trying to measure. My skepticism applies healthily to the BIS data as well as the PITCHf/x data.

My article at Hardball Times on Danny Herrera’s screwball includes views of his pitch trajectories as seen from the right-handed and left-handed batter’s boxes.

I mentioned in the References section that I did some trigonometry to transform the coordinate system from plate view to batter’s box view.

Here is what I did.

The pitch trajectory is shown as the dotted black line. Any point on the trajectory can be calculated using the initial position, velocity, and acceleration provided in the PITCHf/x data, along with the equations of motion. Only the x-y plane is shown above since no transformation was done to the z axis. The coordinates in the PITCHf/x coordinate space are x and y, shown in black.

The coordinates in the batter’s box view are x’ and y’, shown in red. The y-axis in the batter’s box view runs along a line from the batter’s head to the pitcher’s approximate release point (the average x value of his pitches at y = 55 feet). The x-axis in the batter’s box view is set perpendicular to this new y-axis.

The origin of the batter’s box view is offset 2.8 feet in the x direction from the origin in PITCHf/x coordinate space. I calculated 2.8 feet from the center of the plate as the approximate location of the batter’s head, based on a video frame capture in Marv White’s presentation at the PITCHf/x Summit. I chose not to offset the origin in the y direction for simplicity, although I also believe this does not introduce any significant inaccuracy. The batter’s head is typically within a foot or so of y=0.

First, I calculated the quantity m, the distance to the baseball, shown by the blue line. This distance m = sqrt ( y^2 + ( x + 2.8 ft)^2 ).

Next, I found the value of the angle alpha. The angle alpha = arctan ( 55 ft / ( x0 + 2.8 ft) ).

The angle (alpha – theta) = arctan ( y / ( x + 2.8 ft) ), which allows us to calculate the angle theta.

The angle theta = arctan ( 55 ft / ( x0 + 2.8 ft) ) – arctan ( y / ( x + 2.8 ft) ).

The batter’s box coordinates x’ and y’ can be found from the angle theta and the distance m. The new y’ = m * cos (theta), and the new x’ = m * sin (theta).

I am happy for you to use my method for batter’s view transformation if you provide attribution in the form of my name and/or a link to this website.

I have finally gotten around to publishing the 2008 updates to my pitch database parsing scripts.

There are new fields available in the 2008 data. The sv_id field is a date-time stamp of when the pitch was thrown, the pitch_type is the MLBAM algorithm’s best guess at the pitch type, and type_confidence is the confidence value associated with that guess. Starting in mid-May, there are also b_height and p_throws fields in the pitch element. I don’t currently use those fields. I get the pitcher throwing hand from the players’ information, and I don’t record the batter height at this time.

Here is my new database structure for 2008 with these fields added to the pitch table. You can download the new database parser script to use these fields. I have an additional script to update the pitches table with the ball-strike count at each pitch.

I used the time stamp data to look at how quickly pitchers work, and I wrote an article on this topic at The Hardball Times. Several people have asked or been curious about the pitch time data for all the pitchers on their time. Here are the data that I compiled as of June 5.

20.0 Ervin Santana
20.3 Joe Saunders
21.0 John Lackey
22.2 Jered Weaver
22.7 Dustin Moseley
23.4 Jon Garland
20.5 Chris Bootcheck

20.7 Darren O’Day
20.7 Scot Shields
20.8 Justin Speier
21.8 Jose Arredondo
23.2 Darren Oliver
24.1 Francisco Rodriguez

19.3 Roy Oswalt
19.6 Wandy Rodriguez
21.0 Jack Cassel
21.3 Shawn Chacon
22.2 Chris Sampson
24.4 Brian Moehler
24.7 Brandon Backe

20.9 Oscar Villarreal
21.1 Dave Borkowski
22.0 Doug Brocail
22.7 Tim Byrdak
23.2 Wesley Wright
24.4 Geoff Geary
28.0 Jose Valverde

17.6 Joe Blanton
18.9 Rich Harden
20.0 Justin Duchscherer
20.6 Chad Gaudin
20.9 Gregory Smith
21.4 Dana Eveland

18.7 Dallas Braden
21.4 Keith Foulke
22.4 Joey Devine
22.5 Santiago Casilla
22.8 Huston Street
23.4 Andrew Brown
25.3 Alan Embree

Blue Jays
19.9 Jesse Litsch
22.1 Roy Halladay
22.5 Shaun Marcum
24.6 Dustin McGowan
24.7 A.J. Burnett

19.5 Jesse Carlson
21.3 B.J. Ryan
22.8 Shawn Camp
24.0 Brian Tallet
24.4 Jeremy Accardo
26.0 Scott Downs
26.6 Jason Frasor

19.3 Chuck James
19.5 Jo-Jo Reyes
20.1 Tom Glavine
22.1 John Smoltz
22.1 Jair Jurrjens
22.5 Tim Hudson

21.1 Jorge Campillo
21.4 Jeff Bennett
22.1 Manny Acosta
22.5 Blaine Boyer
22.8 Will Ohman
23.0 Royce Ring
25.7 Chris Resop

18.4 Ben Sheets
20.9 David Bush
22.0 Yovani Gallardo
22.2 Manny Parra
22.4 Carlos Villanueva
23.3 Jeff Suppan

21.0 Mitch Stetter
21.3 Brian Shouse
22.9 David Riske
23.4 Seth McClung
23.6 Salomon Torres
25.9 Eric Gagne
26.4 Guillermo Mota

19.9 Kyle Lohse
20.2 Braden Looper
20.7 Todd Wellemeyer
21.2 Brad Thompson
21.5 Joel Pineiro
22.0 Adam Wainwright

20.0 Kyle McClellan
20.7 Anthony Reyes
21.1 Michael Parisi
22.7 Randy Flores
23.0 Ryan Franklin
25.0 Ron Villone
25.4 Russ Springer
27.4 Jason Isringhausen

18.8 Rich Hill
18.9 Sean Gallagher
19.0 Carlos Zambrano
20.1 Ryan Dempster
21.3 Jason Marquis
22.1 Ted Lilly

17.8 Jon Lieber
20.1 Carlos Marmol
20.7 Kerry Wood
21.5 Mike Wuertz
24.9 Kevin Hart
26.5 Bob Howry

20.9 Randy Johnson
21.5 Brandon Webb
21.7 Dan Haren
22.2 Micah Owings
22.6 Max Scherzer
23.2 Doug Davis
24.5 Edgar Gonzalez

20.5 Doug Slaten
24.1 Brandon Lyon
24.1 Brandon Medders
25.2 Tony Pena
25.3 Chad Qualls
25.6 Juan Cruz

18.4 Esteban Loaiza
18.6 Derek Lowe
20.0 Clayton Kershaw
21.9 Brad Penny
22.6 Chad Billingsley
23.4 Hiroki Kuroda

19.9 Cory Wade
22.5 Scott Proctor
22.8 Chan Ho Park
24.2 Takashi Saito
25.5 Hong-Chih Kuo
26.0 Jonathan Broxton
26.6 Joe Beimel

19.7 Matt Cain
20.4 Tim Lincecum
21.4 Barry Zito
21.7 Pat Misch
22.0 Jonathan Sanchez
22.4 Kevin Correia

21.9 Billy Sadler
22.6 Merkin Valdez
23.6 Brian Wilson
23.7 Keiichi Yabu
24.1 Brad Hennessey
25.5 Vinnie Chulk
26.2 Jack Taschner
27.0 Tyler Walker

20.2 Aaron Laffey
21.4 Jake Westbrook
21.5 Jeremy Sowers
21.7 Paul Byrd
21.8 Cliff Lee
23.3 Fausto Carmona
23.7 C.C. Sabathia

20.7 Jorge Julio
21.6 Jensen Lewis
24.2 Craig Breslow
26.9 Masa Kobayashi
29.1 Rafael Perez
32.0 Rafael Betancourt

19.4 Carlos Silva
19.9 Jarrod Washburn
21.3 Felix Hernandez
23.2 Miguel Batista
24.5 Erik Bedard

17.9 R.A. Dickey
20.0 Ryan Rowland-Smith
21.9 Sean Green
22.2 Cha Seung Baek
22.4 Mark Lowe
22.7 Roy Corcoran
24.2 Brandon Morrow
27.9 J.J. Putz

19.0 Scott Olsen
20.4 Andrew Miller
21.2 Burke Badenhop
22.4 Mark Hendrickson
23.7 Ricky Nolasco

20.4 Justin Miller
21.2 Doug Waechter
21.4 Kevin Gregg
22.8 Renyel Pinto
24.1 Logan Kensing
24.2 Matt Lindstrom
25.5 Taylor Tankersley

20.3 John Maine
20.8 Nelson Figueroa
22.4 Mike Pelfrey
22.7 Oliver Perez
22.7 Johan Santana
23.8 Claudio Vargas

21.3 Pedro Feliciano
21.7 Duaner Sanchez
22.8 Scott Schoeneweis
22.8 Joe Smith
22.9 Billy Wagner
23.5 Aaron Heilman
25.0 Jorge Sosa

17.7 Jason Bergmann
19.6 Shawn Hill
20.0 John Lannan
20.2 Tim Redding
20.5 Matt Chico
22.9 Odalis Perez

20.5 Joel Hanrahan
21.8 Saul Rivera
21.9 Jon Rauch
22.3 Luis Ayala
23.8 Jesus Colome

19.9 Adam Loewen
20.7 Jeremy Guthrie
20.9 Daniel Cabrera
22.0 Garrett Olson
22.4 Brian Burres
24.1 Steve Trachsel

21.3 Randor Bierd
22.0 Lance Cormier
22.1 Matt Albers
22.4 Jim Johnson
22.5 George Sherrill
23.7 Dennis Sarfate
24.0 Jamie Walker
24.6 Chad Bradford

19.5 Randy Wolf
19.6 Jake Peavy
20.2 Shawn Estes
20.7 Justin Germano
21.0 Greg Maddux
21.3 Chris Young
23.1 Wil Ledezma
28.1 Josh Banks

17.2 Glendon Rusch
19.6 Cla Meredith
20.4 Joe Thatcher
20.9 Mike Adams
23.1 Trevor Hoffman
24.0 Heath Bell
24.8 Bryan Corey

19.2 Cole Hamels
19.8 Brett Myers
20.2 Adam Eaton
20.3 Jamie Moyer
21.2 Kyle Kendrick

19.1 Clay Condrey
19.6 Chad Durbin
21.2 Brad Lidge
22.4 Ryan Madson
25.1 Rudy Seanez
25.2 J.C. Romero
25.4 Tom Gordon

20.0 Zach Duke
20.9 Phil Dumatrait
20.9 Matt Morris
21.0 Tom Gorzelanny
21.5 Paul Maholm
24.1 Ian Snell

20.2 John Grabow
21.5 Damaso Marte
21.5 Sean Burnett
22.5 Franquelis Osoria
22.8 Matt Capps
22.8 Evan Meek
26.0 Tyler Yates

19.5 Sidney Ponson
21.3 Scott Feldman
21.3 Jason Jennings
21.4 Kason Gabbard
23.0 Douglas Mathis
23.4 Kevin Millwood
24.2 Vicente Padilla

19.5 Eddie Guardado
22.8 C.J. Wilson
23.7 Josh Rupe
24.1 Jamey Wright
24.6 Franklyn German
24.9 Frank Francisco
26.2 Joaquin Benoit

19.7 Andy Sonnanstine
22.5 James Shields
22.5 Edwin Jackson
22.6 Jason Hammel
22.8 Scott Kazmir
24.2 Matt Garza

21.7 Trever Miller
23.4 J.P. Howell
23.6 Gary Glover
25.8 Al Reyes
26.6 Troy Percival
26.9 Scott Dohmann
27.3 Dan Wheeler

Red Sox
18.9 Justin Masterson
19.3 Tim Wakefield
23.2 Bartolo Colon
23.5 Jon Lester
24.3 Daisuke Matsuzaka
26.0 Josh Beckett
26.5 Clay Buchholz

23.7 Craig Hansen
23.8 David Aardsma
25.5 Julian Tavarez
26.3 Mike Timlin
26.3 Manny Delcarmen
26.5 Javier Lopez
27.5 Hideki Okajima
28.4 Jonathan Papelbon

20.0 Bronson Arroyo
20.0 Matt Belisle
20.7 Johnny Cueto
20.9 Josh Fogg
21.1 Aaron Harang
23.2 Edinson Volquez

19.6 Mike Lincoln
19.7 Kent Mercker
20.6 Francisco Cordero
21.3 Jeremy Affeldt
21.4 Todd Coffey
21.9 Bill Bray
22.2 David Weathers
22.8 Jared Burton

20.7 Franklin Morales
21.0 Mark Redman
21.1 Aaron Cook
21.6 Jeff Francis
21.9 Ubaldo Jimenez
23.3 Jorge De La Rosa
23.8 Gregory Reynolds

21.0 Alberto Arias
23.7 Taylor Buchholz
23.8 Brian Fuentes
24.9 Jason Grilli
25.3 Ryan Speier
25.7 Manny Corpas
25.7 Matt Herges
26.3 Kip Wells

20.0 Brian Bannister
20.3 John Bale
21.4 Zack Greinke
22.0 Brett Tomko
22.7 Gil Meche
23.0 Luke Hochevar
23.4 Kyle Davies

21.0 Joakim Soria
22.5 Ron Mahay
23.4 Yasuhiko Yabuta
24.6 Ramon Ramirez
26.0 Jimmy Gobble
27.1 Leo Nunez
29.0 Joel Peralta

20.4 Justin Verlander
20.9 Nate Robertson
21.1 Dontrelle Willis
23.2 Armando Galarraga
24.2 Kenny Rogers
24.7 Jeremy Bonderman

21.0 Todd Jones
22.1 Aquilino Lopez
22.7 Zach Miner
24.5 Freddy Dolsi
25.9 Francisco Cruceta
26.8 Bobby Seay
27.6 Denny Bautista

20.2 Glen Perkins
21.3 Kevin Slowey
21.4 Nick Blackburn
21.8 Francisco Liriano
22.1 Livan Hernandez
23.2 Boof Bonser
24.3 Scott Baker

22.1 Brian Bass
23.2 Matt Guerrier
23.4 Pat Neshek
24.4 Juan Rincon
24.9 Dennys Reyes
26.5 Jesse Crain
26.8 Joe Nathan

White Sox
17.2 Mark Buehrle
20.5 John Danks
21.6 Gavin Floyd
22.8 Jose Contreras
22.9 Javier Vazquez

20.3 Scott Linebrink
20.7 Matt Thornton
20.8 Nick Masset
21.4 Boone Logan
22.7 Octavio Dotel
23.9 Bobby Jenks

21.3 Darrell Rasner
22.1 Andy Pettitte
24.0 Ian Kennedy
25.1 Phil Hughes
25.7 Mike Mussina
26.6 Chien-Ming Wang

22.7 Mariano Rivera
22.8 Kyle Farnsworth
24.4 Jose Veras
25.0 Edwar Ramirez
25.1 Joba Chamberlain
25.5 Jonathan Albaladejo
25.8 Brian Bruney
26.3 Ross Ohlendorf
26.7 LaTroy Hawkins

This has nothing to do with anything except me reveling in the things you stumble upon in the PITCHf/x data set. I was looking at some Roy Oswalt data from last year. When I looked at his August 18 start, I noticed he had thrown his fastball at two distinctly different speeds.

Roy Oswalt pitch sequence August 18, 2007

When do you think Oswalt pulled his left oblique muscle?

You’re right. From the AP game recap:

Oswalt said he first felt something near his rib cage on his last pitch of the third inning, a curveball to Geoff Blum. Oswalt batted with two outs in the fourth and beat out an infield RBI single to give the Astros a 3-0 lead.

“I went through the fourth and told them I want to stay out there and see if I could get through two more innings,” Oswalt said. “Made it through the fourth and thought I could have made it through the fifth.”

No revolutionary analysis there, but I thought it was a fun tidbit.

Last year I diligently kept a catalog of articles written about topics related to PITCHf/x or using PITCHf/x data. Some of you have noticed that I have been negligent in updating that catalog this year. My last full update was January 15, and I did a partial update on March 1.

A new update is now in progress behind the scenes. Since the article list now exceeds six hundred articles, I’m working toward a database solution to better track them all. Hopefully, I’ll be able to unveil something within the next few weeks. In the mean time, Harry, if you would quit writing more than an article per day, that would help a lot. I should just rename my catalog the Cubs f/x Index. 😉