A Soriano Trifecta

Last night in Atlanta Alfonso Soriano homered in each of his first three plate appearances before being intentionally walked, grounding out, and then singling in the ninth inning. His four for five day led the Cubs to a 9-1 victory over the Braves. After a fairly slow start (.270/.392/.308 in April) he hit .302/.500/.362 in May and is 19 for 37 in June a double, 2 triples, and 6 homeruns giving him 10 homeruns for the season. This season he's as aggressive as ever seeing 3.69 pitches per plate appearance and in the metrics on plate discipline I discussed the other day he swings at the highest percentage of pitches (54.8%) that are out of the strike zone of anyone not named Ivan Rodriguez. However, he's just about average in terms of the percentage of strikes he lets by (23%). In any case everything is working for him at the moment and on Friday night he jumped on the first pitch three times. Here are his five plate appearances from Friday night along with a chart of all the pitches he saw.

The number in the upper right hand corner indicate the pitch number along with the starting velocity, ending velocity, and break of the pitch. The pitches are color-coded and their shapes follow this key (you can click on any of the images to make them bigger):

Quantifying Plate Discipline

In my column this morning on Baseball Prospectus (subscription required but well worth it) among other things I take another crack at the PITCHf/x Gameday data. In part, inspired by this fascinating article, I created a couple of metrics to quantify plate discipline. They are:

Swing (S) defined as the percentage of pitches the batter swung at and also available in many other places. Obviously high values here are indicative of aggressive hitters or hitters who see a greater percentage of pitches out of the strike zone.

Fish (F) defined as the percentage of pitches out of the strike zone that the hitter swung at. A higher percentage here indicates that the hitter may have trouble recognizing pitches since he is offering at pitches that would likely otherwise be called balls. It should be noted that the strike zone as defined for this analysis is 17 inches wide (the standard) and uses the actual height customized for the player. No buffer room was added as was done in the previous articles since here we're not concerned with giving the umpire the benefit of the doubt.

Bad Ball (BB) defined as the percentage of pitches out of the strike zone that were swung at and made contact with (including foul balls although there is an argument to be made that a foul ball is not the intended outcome and so should be discounted in some way). A higher value in this category indicates that the hitter, when swinging at bad pitches, is at least able to get the bat on the ball.

Eye (E) defined as the percentage of pitches in the strike zone on non-three and zero counts that were taken for strikes. A smaller value in this metric indicates a player who recognizes strikes and aggressively offers at them. Non three and zero counts were excluded since obviously a hitter is much more likely to let a strike go by in this situation and so we don’t want to penalize them for that behavior. Some readers will see, however, where this idea could be extended to each of the eight possible counts and a system devised where less penalty is credited to hitters who take at 3-1 than those that do so at 0-2.

Without further ado here's the list (with the big caveat that only 24% of all pitches in 2007 have been tracked in the system and there is a heavy bias to the AL West because of the parks that the system is installed in) of all players who have 200 or more pitches tracked this season sorted by "Fish".

Name              Stand    Pitches   Swing    Fish BadBall     Eye
A.J. Pierzynski   L            357   0.602   0.469   0.813   0.117
Garret Anderson   L            260   0.515   0.467   0.750   0.350
Delmon Young      R            200   0.550   0.454   0.630   0.228
Rob Mackowiak     L            297   0.525   0.438   0.738   0.240
Hank Blalock      L            332   0.521   0.430   0.632   0.183
Erick Aybar       L            235   0.532   0.430   0.869   0.217
Carl Crawford     L            216   0.519   0.427   0.738   0.171
Jeffrey Francoeur R            337   0.546   0.425   0.775   0.094
Vladimir Guerrero R            403   0.524   0.421   0.771   0.142
Adrian Beltre     R            430   0.512   0.403   0.694   0.234
Michael Young     R            585   0.523   0.403   0.766   0.284
Eric Chavez       L            519   0.503   0.394   0.736   0.225
Kenji Jojima      R            270   0.522   0.389   0.878   0.217
Nomar Garciaparra R            436   0.557   0.388   0.800   0.132
Juan Pierre       L            431   0.480   0.385   0.916   0.302
Yuniesky BetancourR            345   0.496   0.383   0.806   0.279
Joe Crede         R            376   0.503   0.381   0.729   0.238
Jason Phillips    R            203   0.493   0.381   0.721   0.303
Vernon Wells      R            417   0.501   0.380   0.713   0.216
Jose Lopez        R            347   0.478   0.375   0.853   0.288
Ichiro Suzuki     L            515   0.435   0.369   0.862   0.326
Khalil Greene     R            407   0.514   0.367   0.595   0.247
Orlando Cabrera   R            517   0.468   0.362   0.838   0.279
Brian Mccann      L            267   0.434   0.358   0.783   0.282
Kevin Kouzmanoff  R            363   0.499   0.350   0.724   0.167
Lyle Overbay      L            381   0.465   0.349   0.704   0.230
Sammy Sosa        R            532   0.492   0.349   0.625   0.205
Royce Clayton     R            227   0.515   0.348   0.457   0.194
Adam Lind         L            336   0.482   0.348   0.719   0.253
Andruw Jones      R            389   0.478   0.344   0.686   0.232
Jose Guillen      R            394   0.475   0.344   0.636   0.232
Alexis Rios       R            413   0.426   0.343   0.735   0.351
Marcus Giles      R            452   0.511   0.341   0.674   0.211
Bobby Crosby      R            474   0.451   0.338   0.652   0.327
Casey Kotchman    L            463   0.434   0.338   0.814   0.285
Gary Matthews Jr. L            449   0.441   0.334   0.781   0.223
David Dejesus     L            209   0.445   0.333   0.816   0.340
Nelson Cruz       R            374   0.457   0.330   0.690   0.268
Andre Ethier      L            376   0.500   0.329   0.803   0.200
Jose Vidro        L            336   0.461   0.329   0.870   0.200
Juan Uribe        R            334   0.476   0.328   0.651   0.232
Raul Ibanez       L            474   0.456   0.328   0.760   0.229
Gerald Laird      R            484   0.459   0.326   0.698   0.290
Jason Kendall     R            405   0.432   0.325   0.870   0.373
Jermaine Dye      R            452   0.442   0.324   0.753   0.267
Mark Teahen       L            213   0.437   0.324   0.636   0.307
Adrian Gonzalez   L            590   0.453   0.322   0.767   0.225
Darin Erstad      L            372   0.398   0.318   0.797   0.413
Shea Hillenbrand  R            330   0.445   0.308   0.821   0.297
Brad Wilkerson    L            263   0.414   0.306   0.755   0.303
Paul Konerko      R            457   0.420   0.303   0.765   0.290
Richie Sexson     R            448   0.446   0.303   0.732   0.211
Mark Ellis        R            440   0.409   0.302   0.851   0.380
Mark Teixeira     L            501   0.399   0.302   0.657   0.276
Kenny Lofton      L            514   0.434   0.300   0.818   0.239
Travis Buck       L            377   0.416   0.300   0.662   0.255
Michael Napoli    R            346   0.419   0.299   0.683   0.322
Kelly Johnson     L            384   0.393   0.292   0.787   0.315
Josh Bard         L            283   0.470   0.292   0.878   0.177
Edgar Renteria    R            346   0.405   0.290   0.688   0.275
Jeff Kent         R            420   0.476   0.285   0.611   0.176
Ian Kinsler       R            591   0.423   0.280   0.784   0.268
Terrmel Sledge    L            285   0.446   0.272   0.609   0.217
Rafael Furcal     L            371   0.391   0.270   0.857   0.353
Shannon Stewart   R            437   0.410   0.269   0.810   0.327
B.J. Upton        R            225   0.431   0.269   0.528   0.227
Frank Thomas      R            478   0.377   0.267   0.838   0.335
Troy Glaus        R            306   0.376   0.264   0.706   0.330
Jose Cruz Jr.     L            285   0.428   0.262   0.591   0.216
Aaron Hill        R            417   0.415   0.253   0.729   0.302
Willie Harris     L            205   0.400   0.248   0.765   0.239
Russell Martin    R            506   0.401   0.241   0.757   0.290
Luis Gonzalez     L            428   0.395   0.236   0.889   0.256
Nick Swisher      L            384   0.365   0.232   0.559   0.307
Tadahito Iguchi   R            462   0.422   0.230   0.678   0.243
Frank Catalanotto L            309   0.372   0.228   0.821   0.319
Mike Cameron      R            539   0.399   0.224   0.569   0.237
Reggie Willits    L            274   0.328   0.219   0.818   0.455
Brian Giles       L            381   0.396   0.218   0.804   0.269
Jim Thome         L            330   0.358   0.203   0.545   0.239
Bobby Abreu       L            203   0.374   0.185   0.545   0.274
Jack Cust         L            294   0.310   0.175   0.563   0.342
Wilson Betemit    L            208   0.327   0.155   0.700   0.282
Dan Johnson       L            387   0.282   0.148   0.737   0.346

Of course the interesting thing is that you can then plot the Fish and Eye metrics on a graph and then split the graph into four quadrants. Each quadrant creates a little profile that can be used to characterize a hitter's plate discipline. Note that the bottom left is the sweet spot.

Fun at the Old Ballpark

Last Sunday afternoon my daughter and I headed to Coors Field to see the Rockies take on the Reds in the rubber game of their three-game set. We arrived plenty early and had a good time watching batting practice and chatting about the routine the players perform before the game and how the two teams were doing this season. I was also able to throw in some physics in as we discussed the topic of my recent columns on Baseball Prospectus. She still had three more days of school to go at the time and so the "lecture" was received amiably but not deeply.


My daughter isn't really a very big fan but does enjoy the experience and so I wanted her to be able to see Ken Griffey Jr. play.

The conversation of course turned to his career and accomplishments and she was wowed when she learned he was eighth on the all-time list with 575. So she had to snap a few pictures and caught him coming in after BP and joking around with some folks on the sidelines.








After a colossal lunch that included garlic fries and all manner of fried food we settled down for the game and were rewarded with a 10-9 Rockies victory in 10-innings complete with a six-run comeback by the Rox capped by a three-run triple by Kaz Matsui in the bottom of the eighth inning. Garrett Atkins drove home the winning run in the bottom of the 10th on a line-drive single to left. There were also several interesting baserunning plays in the game, interspersed with dippin' dots of course, that were fodder for discussion. As countless fathers can no doubt attest, most of all it was just a great time spent with my daughter.

The Nats and VORP

I was tipped to this article by Will Carroll. The fascinating paragraph follows:

The Nationals, too, are deploying more tools and ideas. Over the weekend, a group of the club's statistical analysts -- "I'll call them our 'Mod Squad,' " Kasten said -- made a presentation to scouts detailing trends in the draft, using such modern stats as "VORP" (Value Over Replacement Player), the kind of analysis that can make old-school scouts shudder.

It would be interesting to see just how they're applying VORP to amateur players... In any case this is just the kind of thing I was thinking about when I wrote the following in an interview with Baseball Digest Daily last year.

It seems to me that one of the ways in which a team could gain an advantage is to more tightly marry their statistical analysis to their scouting departments in a kind of Hegelian Synthesis. It's just my sense of things (being an outsider after all), but I would guess that most scouts still have little understanding of some of the core sabermetric principles and conclusions. For example, in a roundtable discussion Alan Schwarz had with Eddie Bane (Angels) and Gary Hughes (Cubs) published in 2005, it was clear that Hughes had never even heard of Defense Independent Pitching Statistics (DIPS). While I know that everybody can't know everything, and I'm not throwing Hughes under the bus, I can't imagine that it wouldn't help any team to have their staffs educated on the big picture items like this.

Legos to the Rescue

A couple of interesting tidbits this morning.

A co-worker sent me this link from the local Colorado Springs paper. Teenager Cameron Kruse, who works as a Sky Sox ball boy, built a machine to rub down baseballs in a uniform way. There's also a nice video on YouTube and although it doesn't mention it in the article, he might have used Lego Mindstorms for the programming.

Clay Davenport looks a little deeper into the number of minor league innings rookie pitchers have logged over time. The analysis is still incomplete but is suggestive that perhaps the number of innings accumulated in the minor leagues hasn't declined as has recently been discussed.

Friday Night Links

Well, the Rockies lost for the third straight night, this time to the Reds at Coors Field falling 4-2. Ken Griffey Jr. hit the 575th homerun of his career and Alex Gonzalez hit a three-run homer down the left field line. Those were the two big blows that Taylor Buchholz gave up - otherwise he pitched a pretty good game and was very efficient with his pitches.

A couple of interesting links:

Bad Interview - An interview with Buzz Bissinger regarding an article that will come out this weekend in the New York Times magazine (update: the link is now active and the article is excellent with the caveat mentioned below) on pitching featuring Kerry Wood. In the interview Bissinger says that pitchers have been coming to the big leagues younger and younger. Turns out that's not true although in Bissinger's defense his underlying point was really more about the number of minor league innings today's pitchers throw which as far I know has not been studied. The questioning of the "younger and younger" statement then takes the interview on a bad turn (hat tip to Will Carroll). While I certainly agree with the interviewer's primary point that we needn't take someone's word for what is in the end an objective question for which there is data to definitively answer, it could have and should have been handled better. Bissinger seemed to lose control himself a little which hastened the badness.

Singular - A really well done article by Steve Treder on Steve Dalkowski on THT and one that befits the fact that "His performance profile is beyond question the most singular in the sport's long and meticulously detailed accounting."

The Physics of Drag

My column today on Baseball Prospectus delves once again into the PITCHf/x data tracked by the new Gameday application. This time I take a look at the drag on a pitched ball and square the data with the description of the model discussed by Robert Adair in The Physics of Baseball.

To answer the most frequently asked question thus far - no, I haven't looked at Tim Wakefield in any depth. I did see, however, that his average pitch (and I have 346 to look at) lost exactly 10% of its velocity. Overall though that percentage decrease is in line with the following chart (a version of this chart is also in the original article) since his average pFX (which is a measure of the break of the pitch) was 8.6 and his average start speed was 68.5 miles per hour as shown in the chart below.



However, that percentage does not seem to differ by the break length (a different measure of break introduced this year) nor the pFX value. It's also interesting to note that all but one pitch came out of his hand at less than 79 miles per hour. I think it's likely that the Magnus force placed on a knuckler as it moves in various directions tends to slow it down more than one would other think based on the slow speed and lack of spin.

The 100 RBI Men

A reader makes the following observation:

"Carlos Delgado is currently on pace to get 100 RBIs for the Mets this year, despite sporting an abysmal .234 BA, 306 OBA and 359 SL%. This helps to make the case that RBIs are as much a team stat as an individual one, as the fellas hitting in front of Delgado (Jose Reyes, Carlos Beltran, and David Wright) are pretty adept at getting on base, which allows Delgado to make outs 7 times out of ten and still accumulate a decent number of RBIs. Simply put, he is getting a boatload of opportunities. Delgado also is lucky enough to bat cleanup on a team that is doing very well in the standings, so the manager is unlikely to move him out of the cleanup slot anytime soon."

Well, assuming Delgago plays in 144 games this season as he did in both 2005 and 2006 he'll wind up with 91 RBI and won't break the 100 mark. And as of tonight Delgado's OPS is 665 and taking 2006 league norms for league OPS and the park effect of Shea Stadium that means that Delgado is on pace to have a league normalized and park adjusted OPS of 92. In any case, that doesn't directly bear on the question which follows...

"So, anyways, my question: Who are the worst hitters in MLB history to get 100 RBIs in a season in MLB? And what are their stories? Were these players in similar situations to Delgado, or do they have some other tale to tell?"

To answer the first part and take a crack at the second, here are the "top" 50 players with 100 or more RBI in a single season with the lowest normalized and park adjusted OPS. There are 1,543 players with 100 or more RBI in a single season since beginning in 1901.

Name                      Year      PA       G     RBI     OPS  NOPS/PF
Joe Carter                1997     668     157     102     683       89
Vinny Castilla            1999     674     158     102     809       92
Ruben Sierra              1993     692     158     101     678       93
Tony Armas                1983     613     145     107     707       94
Paul O'Neill              2000     628     142     100     760       94
Ray Pepper                1934     598     148     101     732       94
Marv Owen                 1936     655     154     105     750       96
Joe Carter                1996     682     157     107     782       96
Glenn Wright              1927     626     143     105     716       96
Joe Carter                1990     697     162     115     681       97
Travis Fryman             1996     688     157     100     766       97
Joe Randa                 2000     665     158     106     781       97
Jeff Francoeur            2006     686     162     103     742       98
Jeff Cirillo              2000     684     157     115     869       98
Tony Batista              2004     650     157     110     728       98
Torii Hunter              2003     642     154     102     762       99
Ray Jablonski             1953     640     157     112     735       99        
Joe Pepitone              1964     647     160     100     698      100
Ernie Banks               1969     629     155     106     725      100
Carlos Beltran            1999     723     156     108     791      100
Bill Buckner              1986     681     153     102     733      100
Bill Brubaker             1936     620     145     102     736      101
George Bell               1992     670     155     112     712      101
Garret Anderson           2001     704     161     123     792      101
Travis Fryman             1997     657     154     102     766      101
Andres Galarraga          1995     604     143     106     842      101
Chili Davis               1993     645     153     112     767      101
Eddie Robinson            1953     685     156     102     735      101
Wally Pipp                1923     634     144     108     749      101
Willie McGee              1987     652     153     105     746      101
Bing Miller               1930     654     154     100     795      101
Pinky Higgins             1938     603     139     106     794      101
Butch Hobson              1977     637     159     112     789      101
Andruw Jones              2001     693     161     104     772      101
George Kelly              1929     632     147     103     760      101
Gee Walker                1939     645     149     111     773      101
Moose Solters             1936     676     152     134     802      101
Ed Sprague                1996     670     159     101     821      101
Al Simmons                1924     644     152     102     774      102
Ruben Sierra              1987     696     158     109     771      102
Billy Rogell              1934     679     154     100     766      102
Richie Sexson             1999     525     134     116     818      102
Vernon Wells              2002     648     159     100     762      102
Pinky Whitney             1930     662     149     117     849      102
Pinky Whitney             1928     636     151     103     768      102
Matt Williams             1997     636     151     105     795      102
Glenn Wright              1924     662     153     111     744      102

I know many of you had an inkling that Joe Carter would take the top spot. He also appears at numbers 8 and 10 (and number 53 for his 1987, 104 for his 1989 season, 110 for his 1993 season, and number 146 for his 1994 season...you get the idea). But given the poor light in which RBI have been cast in recent years, perhaps surprisingly only 17 times (a little over 1%) has a player ever driven in 100 runs while not being at least league average. So while getting to 100 RBI doesn't ensure that the hitter is an elite offensive performer, it is a pretty good proxy. Put in another way, one needn't be a great hitter to accrue 100 RBI but great hitters often get to 100 RBI. And so in the absence of better metrics it's not surprising that 100 RBI became shorthand for a great offensive performance. This is illustrated by the fact that the "average" 100 RBI man had NOPS/PF of 125 and the histogram belows which shows their distribution:



Contributing to the idea that RBI equals greatness is the ongoing debate over the significance and prevalence of clutch hitting. A player with alot of RBI is often automatically assumed to be a clutch performer as Joe Carter was.

That said, given that we now have much more granular means (with OPS actually being on the lower end) of estimating the run contribution of individual hitters, that usage should wane some although it may take generational turnover to bring about its demise. For a little deeper perspective on traditional and more modern methods of gauging a player's contribution see chapter 1 of Baseball Between the Numbers.

But in getting back to the question at hand, perusing the list you see a few factors that certainly play into reaching the century mark:

Performance - As mentioned above there is no doubt that in large part getting to 100 RBI requires a strong performance. From the graph above (the pink cumulative line that uses the y-axis on the right) you can see that fully 75% of those who have driven in 100 runs were 15% or more above league average and 60% were 25% or more above average.

Park - Vinny Castilla and Jeff Cirillo in the top 20 show that playing in a park where lots of runs are scored certainly helps, and of course by adjusting for park we don't give them any benefit

Era - Twelve of the top 20 players either played in the 1930s or since 1993 which were the two highest scoring eras in modern baseball history. Just like playing in a park where runs are more plentiful allows lesser hitters to drive in more runs, playing in an expanding offensive environment devalues the 100-RBI mark.

Teammates - Certainly the reader makes a good point about teammates having to be on the bases. You could probably make a case that Paul O'Neil in 2000 with the Yankees, Marv Owen for the Tigers in 1936, Glenn Wright with the Pirates in 1927, and Bill Buckner with the 1986 Red Sox all fall into this category where the individual was part of a strong offensive team from top to bottom.

Lineup Position - It probably comes as no surprise that many of the players on this list and that accrue 100 RBI generally are middle-of-the-order hitters. It probably comes as a bit more of surprise that, as shown in the graph below, the number three position in order actually hits with relatively fewer runners on base than does any other lineups positions save the leadoff and second spots in the order.

Plate Appearances - More generally the latter two contributing factors as well as this one fall into the category of opportunity. A player has to come to the plate often enough to reach the 100 RBI mark. Probably no one in this list better exemplifies these is Ruben Sierra's 1993 performance with the Oakland A's. In that season Rickey Henderson hit leadoff for half the year and he hit third in an AL lineup which increases the opportunities for the third hitter and racked up almost 700 plate appearances. On the average, the 100 RBI men had 651 plate appearances. Yes Rudy York did drive in 103 runs in just 417 plate appearances for the 1937 Tigers but all told just 213 players (14%) have ever driven in 100 runs while not coming to the plate at least 600 times.

So will Carlos Delgado get to 100 RBI and if so what does it mean? Speaking only in generalties and knowing only that about this performance, we'd have to guess that he was a pretty good hitter. However, that doesn't completely rule out the possibility that his park, era, teammates, lineup position, and playing time all conspired to his breaking the 100 RBI barrier.

Deep Data Dive

As promised yesterday my column on Baseball Prospectus this morning dives deeper into the PITCHf/x data tracked by the 2007 version of Gameday (a new update was released on May 10th and is much more performant).

In this article I take a look at the velocity and location data that includes over 40,000 pitches and discover that given a one-inch margin of error the system agrees with umpires to the tune of 90%. Not bad and very similar to the QuesTec results published by Robert Adair in an article titled "Cameras and Computers, or Umpires?" that was published in Volume 32 of SABR's The Baseball Research Journal.

A Little Light Reading

Just a few things to chew on...

Interesting article by Paul Swydan on the Rockies use of information technology. There is more in the Rockies magazine that they sell at the ballpark with some excellent photos of the custom software they use.

Interesting quotes from Tony LaRussa on scoring in multiple innings increasing the probability of winning. As Phil points out, however, simply scoring more runs also increases the probability of winning dramatically and so the quesiton is really which is the more fundamental thing to strive for - scoring in multiple innings or putting together an offense that can score more runs in the aggregate. It seems to me that the former is just an effect or outgrowth of the latter.

Here are seven reasons why sabermetrics helps build a better ballgame by Nate Silver.

An excellent and succint analysis of this April's weather and it's impact on run scoring. It's all about the temperature. I'll have more to say about this in my Baseball Prospectus column tomorrow with regards to pitch velocity.

In my last chat I said:

A couple weeks ago I overheard one beat writer mention repeatedly that so-and-so was a "four-A" player indicating that regardless of what he's doing in AAA (this guy has an OPS of 1.25 thus far and a pretty good track record) he'll never make it in the majors. One of the things that performance analysis has revealed is that for the most part there is no magical line between the minors and majors as evidenced by a certain measure of predictability as players cross that threshold. To me, the way we as fans and the industry itself views the minors versus the majors in terms of attention, compensation, media exposure etc. is responsible for that view.

And the so-and-so was...this guy.

I had never seen this before but the British Library has a wonderful way of viewing some of their material. They also have a new beta that uses Microsoft Silverlight.

The Dismal Science?

My column this morning on Baseball Prospectus is actually a review of the book The Baseball Economist: The Real Game Exposed by J.C. Bradbury. Like many others I've enjoyed Bradbury's commentary on his site Sabernomics.

In short, I recommend the book and really appreciated both the wide range of sabermetric studies but more so how they're couched in economic concepts. That chapters on performance enhancing substances and baseball's monopoly power are especially strong. I did have a few quibbles with some of the studies but you can read more about that in the column...

Mad Dog

Greg Maddux is off to a nice start in 2007 with a 3-2 record and 3.20 ERA in 50 2/3 innings having given up only 45 hits and 7 walks. It turns out that of his eight starts this season five have been recorded more or less completely by the Enhanced GameDay system. In all, that's 358 pitches, 164 to right-handers and 194 to left-handers. Just fiddling around with some of the data today here are some random observations.

The fastest pitch leaving his hand was 94 mph. He threw the pitch to Garrett Atkins on April 6th in the top of the third inning. The pitch was called a ball. That pitch was also the fastest when it reached the plate at 83 mph.

The slowest pitch he threw was 71.5 mph to Kazuo Matsui in the 6th inning of that April 6th start. That pitch crossed the plate at 62.6 mph before Matsui hit a ground ball to third for an infield single.

His average velocity out of the hand was 85 mph and crossing the plate was 75.2 mph. But what's most amazing is that the standard deviation of his muzzle velocity was just 2.86 mph. By comparison, of pitchers who have thrown 100 or more pitches in 2007 with GameDay watching his pitches have varied the least. Jason Schmidt was close at 2.95 mph and on the other end Randy Wolf was at 8.85 mph.

The breakdown of the outcomes of his pitches were:

Ball                         102   28.5%
Called Strike                 79   22.1%
In play, out(s)               61   17.0%
Foul                          50   14.0%
Swinging Strike               24    6.7%
In play, no out               19    5.3%
In play, run(s)                6    1.7%
Foul Bunt                      5    1.4%
Pitchout                       4    1.1%
Foul (Runner Going)            3    0.8%
Ball In Dirt                   2    0.6%
Swinging Strike (Blocked       2    0.6%
Missed Bunt                    1    0.3%
                             358

Not Surprisingly he doesn't get many swinging strikes.

The break down of his pitches against lefties and righties is shown in the two graphs below (pictured from the perspective of the pitcher).


Against lefties he clearly stays on the outer half and besides how few pitches he leaves in the middle of the plate, it would appear he gets a pretty good number of calls on balls that are actually outside the strike zone. Cory Schwartz was kind enough to answer a few questions on the system over at The Book blog recently and said that through testing they're confident that the tracking is within 2 inches with regards to a pitcher's release point and within 1" as the ball crosses the plate. Even with a 1" margin of error and remembering that the data points I'm using are much smaller than an actual baseball, that's still a fair number of pitches that Maddux seems to get the benefit of the doubt on.



Against righthanders he seems to catch more of the plate and interestingly doesn't seem to pitch as much down in the zone.

The Gospel of Pronation

At last year's SABR convention I had the opportunity to listen to Mike Marshall, former Cy Young winner and PhD, preach the gospel of injury avoidance through his rather unique pitching mechanics and instruction. At the time I noted how he's not taken seriously by the industry and now Jeff Passan over at Yahoo Sports has written an excellent article on Marshall (or "Doc" as his disciples call him) and his Pitching Research and Training Center in Florida north of Tampa. What's particularly interesting is the accompanying video of Marshall protege Jeff Sparks who once pitched for Tampa Bay and who still travels to Marshall's complex in hopes of catching on somewhere.

In the video you'll see the extreme pronation of the wrist in both the fastball and curveball motions as well the screwball which Marshall preaches will effectively eliminate elbow injuries requiring Tommy John surgery. The video is high speed and so shows the motion pretty clearly. You'll also want to check out Passan being interviewed by Will Carroll on BP Radio from last weekend.

In the end it would be interesting if a team would send a few borderline pitchers Marshall's way in order to see if there's anything to his claims about injury avoidance and additional velocity. Those who attend his center are certainly true believers but the rest of us won't be until there are some documented successes.

Charting Sammy

Along with the Mother's Day festivities I had a fun weekend collecting and analyzing Enhanced GameDay data. What that allows is for analysis of individual pitchers and hitters. For example, below is a graphic (click to make larger) of all 340 pitches that have been tracked for Sammy Sosa this season. Since Texas is one of the parks in which the system is running and since others where it has been running the longest include Chicago (AL), Oakland, Seattle, and Anaheim, the Rangers tend to have more of their games being tracked. New parks will be added as the season goes along and all told there are now 13 parks in which the system has collected some data with Detroit, St. Louis, Washington, and Colorado (yeah!) being add in the last few days.

The strike zone that is drawn is an average of the strike zone for each plate appearance. Obviously these graphs can be broken down further by count, pitch type, and especially handedness of the pitcher but even so here you can see that when Sammy chases pitches they are often down and away and fouls off a fair number of pitches that inside.

In any case, look for more of this in the future here and on Baseball Prospectus.

The Proper Goal

A well-reasoned article by George Will on Barry Bonds and his upcoming "achievement". He covers all the relevant bases in discussing why it is that baseball seems to be held to a higher standard than other sports, a little of their history in sports other than baseball, the difficulty in drawing the line, and the confluence of other factors in baseball that makes it difficult to detect performance enhancers, and of course the kind of circumstantial evidence that I discussed in my 2005 article on the heels of Bonds 704th homerun.

But my favorite aspect I think is the reminder that Will provides as to just why it is that performance enhancing substances are bad for the game - actually any game. I've often heard talk show hosts and read columnists that argue that players have a right to do whatever it takes to feed their family etc. etc. and who are we to judge and why should we or they care? As an antidote to that kind of thinking Will offers the following:

Athletes who are chemically propelled to victory do not merely overvalue winning, they misunderstand why winning is properly valued. Professional athletes stand at an apex of achievement, but their achievements are admirable primarily because they are the products of a lonely submission to a sustained discipline of exertion. Such submission is a manifestation of good character. The athlete's proper goal is to perform unusually well, not unnaturally well. Drugs that make sport exotic, by radical intrusions into the body, drain sport of its exemplary power by making it a display of chemistry rather than character. In fact, it becomes a display of some chemists' virtuosity and some athletes' bad character.

Pitch by Pitch

In my column today on Baseball Prospectus I take a pitch by pitch look at the almost no-hitter of Yankees rookie Phillip Hughes on May 1st using the enhanced GameDay data provided by MLBAM. Joe P. Sheehan over at Baseball Analysts has pioneered alot of the analysis and so you'll want to check out his work as well.

To me, the interesting aspect of this is in how it will certainly be combined with bio mechanical analysis in order to provide quantitative support for what is today primarily observational. A synthesis of the two worlds will offer insights that neither could provide independently and that's exciting stuff.

Update: My article actually was published after this one by John Beamer at THT where he looks at the consistency of the data between parks. Obviously, if I had seen this I would have mentioned it but interestingly, his research on Kevin Millwood, who has pitched at five of the eight parks in which the Enhanced GameDay system is installed, suggests that there may be some park bias in play. Particularly he sees this in speed and location (for example location of release point). Both of course become much more useful when you can compare them from game to game. If indeed the system has built-in biases, either adjustments will have to be made internal to the system (if possible since a variable like mound height may be a contributing factor as may atmospheric conditions although Beamer finds that unlikely) or those using the data will have to make the adjustments themselves. The second option is certainly not appealing.

Lightning

In the spring of 2004 my wife and eldest daughter had the chance to travel to England so that I could attend a conference. Since this was our first visit we planned to pack in as much as we could during the trip and in addition to a quick tour of London that included the British Museum, Westminster Abbey, Trafalgar Square, we headed up to the Yorkshire Dales where the real James Herriott (Alf Wright) practiced. Before making our way to the conference south of London we swung over to Oxford in order to get a look at where C.S. Lewis lived and worked. On a rainy Saturday, with the help of a local man who knows the pastor, we were able to visit the Anglican church near Lewis' home where he worshipped for all of his adult life and where he was buried in the small church cemetery. Inside the church there are several remembrances of Lewis including a very nice window depicting a scene from Narnia and a marker on the pew where Lewis and his brother Warnie regularly sat. That's my daughter sitting in the seat that Lewis typically occupied.



All of this came to mind as I was reading Lewis' essay "On Church Music" published in Christian Reflections the other night. The essay attempts to navigate the controversy of "high" versus "low" church music with high meaning more serious music sung by a trained choir and low meaning hymns sung by the congregation. As Lewis often does he sees in both the opportunity for Christians to "humbly and charitably" sacrifice by either indulging the "lusty roar of the congregation" or remaining silent and respectful of that which one doesn't understand. In that way "Church Music will have been a means of grace; not the music they have liked but the music they have disliked." For his part Lewis was more skeptical that any music is very religiously relevant and even in this essay we find one of his famous quotes that "What I, like many other laymen, chiefly desire in church are fewer, better and shorter hymns; especially fewer."

Given the admonition that at the very least music is a chance at sacrifice and a means of giving grace, I'm somewhat hesitant to proceed. And yet I'll share what I found to be my own interesting reaction to one of the praise songs we sometimes sing in our church. The song is called "Indescribable" by Chris Tomlin, the first two verses and the chorus of which go like so;

From the highest of heights to the depths of the sea
Creation's revealing Your majesty
From the colors of fall to the fragrance of spring
Every creature unique in the song that it sings
All exclaiming

Indescribable, uncontainable,
You placed the stars in the sky and You know them by name.
You are amazing God
All powerful, untamable,
Awestruck we fall to our knees as we humbly proclaim
You are amazing God

Who has told every lightning bolt where it should go
Or seen heavenly storehouses laden with snow
Who imagined the sun and gives source to its light
Yet conceals it to bring us the coolness of night
None can fathom

Now, this song is clearly extolling the power and majesty of God and has a melody and cadence that heightens the emotions and from what I observe is clearly one of the favorites of the congregation. That said, each time the second verse beginning with "Who has told every lightning bolt where it should go..." begins I cringe just a little.

To me, these lyrics that God has an interest in directing individual lightening bolts harkens back to the early 18th century when lightning was viewed as a means of God's displeasure and/or the work of demons which, along with good spirits, were thought to have filled the air. In those days as a storm approached church bells would be rung in order to ward off the bolts as in the words of St. Thomas Aquinas "The tones of the consecrated metal repel the demon and avert storm and lightning". As you can imagine this wasn't an effective strategy and as Walter Isaacson related in Benjamin Franklin: An American Life, "during one thirty-five year period in Germany alone during the mid-1700s alone, 386 churches were struck and over a hundred bell ringers killed." Of course Franklin's invention of the lightning rod in 1752 began to change this way of thinking although some theologians resisted its use fearing that it would be impious to resist the hand and judgment of God. In one particularly tragic event over 3,000 people were killed in 1767, some fifteen years after Franklin's invention when the church of San Nazaro in Venice was struck igniting gunpowder being stored in the church.

What I find interesting in all of this is that in the praise song lightning is viewed as just another display of God's creativity and power along with the flowers and stars. And yet this is a power that has been tamed by the intervention of man and so in the song we can stand back and admire it without fear of consequences or judgement. While congregations 300 years ago may indeed have also looked at lightning as a display of God's power, they would additionally have looked at it as an instrument of God's judgement. The mention of lightning in a hymn would have conjured up far different notions to them than it does to us. One wonders whether including the other sentiments expressed in Tomlin's song would even have seemed appropriate. Beyond that it seems just silly to praise God in worship songs for directing lightning bolts when we do our darnedest to intercept and redirect them whenever possible. What if the word lightning in the song were replaced with "tornado"? Would we really sing "Who has told every tornado where it should go..."? I just don't think most modern Christians think God uses natural events to punish people and so I find it somewhat surprising that the concept is so blatant in a song that I've heard sung in more than one evangelical church in the last decade. Unless I'm wrong one would hope church leaders would do a better job of ensuring that what is sung and said in the service lines up with current Christian belief. To that end, I wonder what seekers attending services think when they see lyrics like this?

The point was also hit home a few weeks ago when we also had a guest worship leader who sang a song he had written that included the line "to the God of lightning." Before the song he relayed the context of its writing which included sitting with his eight-year old daughter on the back porch watching the thunderstorms roll over the eastern Colorado plains. His daughter was awed by the display and before heading to bed asked to linger and then prayed that God would send another blast of lightning and thunder. Again, 300 years ago that would have been unfathomable.

To me, remembering the terror and destruction that lightning has caused along with how the church viewed it historically, I find it anachronistic and intellectually vacuous to sing praise songs in which we in effect blame God for a natural phenomenon.

The Song of the Dodo

Two of the genres that are among my favorites are natural history and travelogues. So it's no surprise that I found The Song of the Dodo: Island Biogeography in an Age of Extinctions by David Quammen one of the best books I've read in a while. Originally published in 1996 it retains the status of a classic of sorts having won several literary awards and remains in print and on store shelves in a newer 2004 printing that I picked up a couple months ago.

Basically, the book traces the intellectual pedigree of the field of island biogeography starting with Alfred Russell Wallace, a first rate naturalist in his own right, and more famously the "co-discoverer" of evolution (although Quammen has a little axe to grind on that score) with Charles Darwin. From there Quammen traces the development of the field through the 20th century which really picks up momentum in the 1960s with the work of Robert MacArthur and Edward O. Wilson. He brings it up to the present day as the field morphs from being primarily descriptive (what organisms live on what islands) to a theoretical and quantitative one (why are those animals found in their distributions on those islands) to the field's application beyond islands to the problem of habitat fragmentation and its consequences for the future.

While that may sound a little dry and challenging to some readers, Quammen makes the intellectual journey an enjoyable one as he describes the theories, papers, and studies in the context of his visits to the famous and not so famous islands and locales of habitat fragmentation. From traipsing through Brazil to get a look at species of New World monkey (Brachyteles arachnoides) isolated in rainforest bordered on all sides by clear cut where he helps collect droppings, to the Galapagos, Mauritius (once home to the Dodo, Raphus cucullatus, whose song if it had one is now lost, hence the title of the book), Guam, Hawaii, Madagascar, Tasmania (and his search for the extinct? Tasmanian wolf (Thylacinus cynocephalus), Aru in the Malay archipelago to view the birds of paradise (Cicinnurus regius and Paradisaea apoda) which was Russell's last stop before heading home, to the islands Komodo where he relates the sometimes dangerous coexistence of people and dragons (Varanus komodoensis), Quammen makes each site come alive while illustrating some theme in the larger intellectual story he's telling. Although not the main story he's telling, I've always been fascinated by the stories of human "first contact" with island species and how that contact typically decimated the species through what Quammen calls their "ecological naivete" evidenced by their shortage of defensive adaptations that creatures on larger land masses must evolve in order to survive. In part I liked the book because he does a good job of documenting many of these cases and giving us a sense of what's been lost.

But the main story line is also a fascinating one that at its core relates how the fundamental ideas of species distribution on islands were slowly formulated in the twentieth century. In particular Quammen points to the distillation of 100 years of observation in a 1967 book titled The Theory of Island Biogeography by MacArthur and Wilson that describes the area and distance effects. In epitome the area effect shows itself in that small islands tend to harbor fewer species than large islands with the underlying causes being fewer immigrations and more extinctions (due to what he refers to as stochastic factors or seemingly random occurrences that can decimate a smaller population such as drought, fire, or volcanic eruption) with the area of the island being determinative of the quantities. The distance effect shows itself in the fact that more remote islands are the home to fewer species than islands closer to the mainland since there are fewer immigrations. These ideas then lead to the so-called "species-area relationship" that attempts to quantify these effects through an equation (known as the species-area equation). Biogeography had matured into a full theoretical and quantitative science.

The really interesting part however, is when these ideas began to be applied not just to islands but other ecosystems as they were in a seminal paper by Jared Diamond in the mid 1970s. The implication of this application is simply that as habitats are fragmented they may end up supporting fewer species than larger contiguous blocks. In other words, although efforts to preserve species through the creation of parks and reserves is noble, their creation may end supporting fewer species than we think due to the inability of species to migrate and "extinction" of the local population due to these random events which probabalistically have more impact than they would if the populations were larger. As a hypothetical example, assume you were to survey your lawn and count the number of species of organisms that inhabit an average square yard. If you then cut out one of those squares and isolated it from the rest of yard, the idea is that if you surveyed it again later it would contain fewer species because of the area and distance effects. Quammen then details the raging scientific debate that ensued regarding not only if the application of biogeography to the design of preserves was appropriate but if so, just how large preserves should be to maintain what would become known as the "minimum viable population" or the number of animals required in order for a population to survive in the long-term, however that's defined.

Quammen interviews many of the participants, or should I say characters since several are quirky to say the least, in the debate on all sides but certainly seems to take the view that at least in the larger perspective, attempts to connect habitats and enlarge nature preserves are what is needed to avoid the problems inherent in fragmentation.

The ideas and the debate fascinating but Quammen also brings to them a life in a way that allows the reader to not only understand the concepts but think deeply about their implications. For my money you can't ask more of a book than that.

Keith Woolner Gets the Call

Congratulations are in order to Keith Woolner, a BP colleague and the inventor of VORP, who has taken the position of Manager of Baseball Research and Analysis with the Cleveland Indians. The quote by Indians GM Mark Shapiro that I used in my Hope and Faith piece on the Indians in March is clearly borne out by this move and I'm sure that Keith will be a key contributor to their future success. Congrats!

MLBAM and Silverlight

Maury Brown, our guru of all things related to the business of baseball at Baseball Prospectus, has many times documented the rise of Major League Baseball Advanced Media (MLBAM) and how it has not only made baseball available in a very rich way but also added to the revenue stream of the owners. From it's first attempts at streaming games in September of 2002, all the while aided by the relentless march of technology, MLB.com has grown to where it will host over three billion visitors and by the end of the year will crack the one million subscriber count for multimedia content.

I don't want to steal Maury's thunder but I had just had to pass along this demonstration of MLBAM's use of a new technology called Microsoft Silverlight. In a nutshell Silverlight is a cross-browser, cross-platform plug-in for delivering media experiences and interactive applications for the web. For software architects and developers heavily invested in the Microsoft platform (like myself) this is exciting since it allows development across platforms using the .NET Framework that we're also using to build our business applications.

In any case, MLBAM's President Bob Bowman and Justin Shaffer who is in charge of new media joined Microsoft at their MIX07 conference in Las Vegas earlier this week to demo how they'll be using Silverlight in the very near future. Essentially, Silverlight will be rolled into the MLB.TV applications later this summer and will enable a host of new features. Some of those demoed include:

More Bandwith: 1.5 Mbps streaming whereas today they stream at 400K and 700K

Composite Video: Overlays of the video stream that offer controls that allow you to manage the video or view enhanced information. For example, the data that you see today in Gameday will be made available via widgets that rest semi-transparently on top of the video in an unobtrusive form providing the wealth of additional information that MLBAM is collecting. For folks like me, this is a true value-add as it creates new ways of watching the game by integrating that information that we crave.

Chatting: In the same vein a chat widget will be available that allows for conversations with friends and fans of rival teams.

Player Tracker: The Player Tracker used in Mosaic will be available with widgets that overlay the video that provide real time updates on your players and can then make available highlights of your players.

Picture in Picture: They showed a nice feature where a "friend" can send a video clip which can then be viewed in "picture in a picture" mode while the main stream continues to play.

Resizing: One of the features that Silverlight enables is dynamic resizing to allow the user to check on other scores, stats, and content and resize the video window on the fly accordingly.

Mobility: And finally, they showed a forward looking demo that had Gameday running on a Windows mobile phone with the player tracker feature playing video clips.

As an MLB.TV subscriber this is some exciting stuff and for those who aren't subscribers it provides some additional impetus to get signed up.