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Sunday, February 26, 2006

Books, Books, Books

Pitchers and catchers have reported and besides the baseball world being filled with stories of the delinquency of Manny Ramirez, the injuries of Mark Prior and Kerry Wood, the World Baseball Classic, and Pedro's toe, that means that it's time for baseball books.

Don't you love it when two of your interests collide?

Walk Like a Sabermetrician has a positive review of the THT Baseball Annual, which is nice to see. He also has a rather negative review of The Book on the Book by Bill Felber in which he makes the statement:

"If you are well-read in sabermetric theory, you will not learn anything from this book. If you are not, you still may not learn anything from this book. The studies Felber presents are wrought with questionable methodology and selective sampling problems."

I wanted to read this book but never got around to ordering it, and perhaps now I'll pass it by.

In other reading I just finished Winners by Dayn Perry and will have a review on THT shortly as well as Baseball's All-time Best Sluggers by Michael Schell. Yes I'm more than a bit tardy on that one but should have some comments posted here shortly.

In the mail I see Baseball Between the Numbers, which I'll also review on THT in the coming months and of course Baseball Prospectus which should be shipping soon. I pre-ordered John Dewan's The Fielding Bible but have yet to see it as well as The Book: Playing the Percentages in Baseball, which should be out soon.

Dave Studeman also had some comments on the new crop of baseball books over at THT.

Friday, February 24, 2006

Prior Aggression

Nice to see this story on MLB.com today regarding Mark Prior and his desire to be more aggressive in his pitch calling.

"I definitely think I need to have more of an aggressive, not attitude, but aggressive nature in my pitch calling and not wait for them to make a mistake but me kind of try to force the issue on their part more," he said. "Does that mean I throw more inside or more offspeed or whatever? That'll be determined by who you're facing and what type of year that person is having.

"I think once I get rolling, there will be game plans and strategy issues that I'll look at and try to do differently than I did in the last couple years."

I watched him pitch at Coors Field last August and noted at the time that he needed to quit nibbling at the corners and simply go after hitters. Last season he threw 16.96 pitches per inning which ranked him 370th in the majors. By comparison Greg Maddux threw 13.77 pitches per inning good for 13th in baseball and 5th among starters.

Thursday, February 23, 2006

Caught Looking versus Swinging

A commenter on my article on Baseball Analysts this morning asks this question:

"Somewhat related to your analysis of Swinging K's, I've often wondered about anything that can be gleaned from players who have a tendency to strike out looking vs. strike out swinging. Is one "worse" than another? Does looking suggest poor pitch recognition and does swinging suggest less of an ability to make contact?"

Good question so I thought I'd run the numbers. Here are the top and bottom 20 in percentage of strikeouts that are called versus swinging (miss, foul tip, swinging strike blocked).

Name PA C S C/S
Jody Gerut 191 13 7 0.650
D'Angelo Jimenez 119 14 9 0.609
Brian Giles 674 38 26 0.594
Todd Linden 187 32 22 0.593
Eric Young 163 7 5 0.583
Frank Thomas 124 18 13 0.581
Jeff Bagwell 123 12 9 0.571
Alberto Castillo 115 12 10 0.545
Bobby Hill 105 9 8 0.529
Bobby Kielty 433 35 32 0.522
Chipper Jones 432 29 27 0.518
Oscar Robles 399 17 16 0.515
Kenny Lofton 406 21 20 0.512
Jeff DaVanon 271 22 22 0.500
Marco Scutaro 423 24 24 0.500
Scott Podsednik 568 37 38 0.493
Frank Menechino 180 16 17 0.485
JD Drew 311 24 26 0.480
Maicer Izturis 210 10 11 0.476
Jose Offerman 118 8 9 0.471



Name PA C S C/S
Dontrelle Willis 101 0 12 0.000
Jose Macias 190 1 23 0.042
Angel Berroa 652 5 103 0.046
Aaron Guiel 121 1 20 0.048
Jorge Cantu 631 4 79 0.048
Jorge Piedra 124 1 14 0.067
Kazuo Matsui 295 3 40 0.070
Miguel Olivo 281 6 74 0.075
Juan Uribe 540 6 71 0.078
Vladimir Guerrero 594 4 44 0.083
Todd Greene 134 2 19 0.095
Ivan Rodriguez 525 9 84 0.097
Mike Sweeney 514 6 55 0.098
Johnny Estrada 383 4 34 0.105
Torii Hunter 416 7 58 0.108
Brian Jordan 251 5 41 0.109
Reed Johnson 439 9 73 0.110
Carl Everett 547 11 88 0.111
Garret Anderson 603 10 74 0.119
Eric Bruntlett 121 3 22 0.120

Well, I think from this that clearly players who take fewer pitches overall are the ones who also don't strike out looking very often. Hard to believe that Angel Berroa was caught looking just 5 times.

The top list also I'm sure has a higher OPS since those in the bottom don't walk much.

So to answer the question it doesn't appear as if striking out looking is an indication of poor pitch recognition nor does striking out swinging suggest a reduced ability to make contact. I think it's simply more tied to aggressiveness.

Baseball Trivia

Here is the raw data associated with my article "Swinging, Fouling, Taking, and Other Baseball Trivia" published on Baseball Analysts today.

The list includes the 341 players in 2005 with 200 or more plate appearances.

The columns include:

PA = plate appearances
P/PA = pitches per plate appearance
1st/PA = how often the batter swings at the first pitch
GB% = percentage of balls in play that are grounders
FB% = percentage of balls in play that are fly balls
P% = percentage of balls in play that are popups
LD% = percentage of balls in play that are line drives
F/P = fouls per pitch
B/P = pitches taken for balls per pitch
C/P = called strikes per pitch
X/P = pitches put into play per pitch
PD = measure of plate discipline as decribed in the article

For those of you familiar with The Hardball Times Baseball Annual and FanGraphs.com will notice that the ground ball, fly ball, line drive, and popup percentages are slightly different here because the data comes from a different source.

And that points out how different scorers and scoring systems can attribute the same balls in play differently.


PA P/PA 1st/PA GB% FB% P% LD% Miss/P F/P B/P C/P X/P PD
Bobby Abreu 719 4.39 10.3% 47.2% 28.4% 2.8% 21.6% 6.2% 12.6% 42.1% 23.6% 14.9% 154
Russ Adams 545 3.74 13.9% 46.4% 26.7% 8.7% 18.2% 4.0% 15.2% 37.4% 21.2% 21.4% 134
Edgardo Alfonzo 402 3.54 23.1% 37.2% 29.2% 12.1% 21.5% 4.2% 19.3% 35.0% 17.1% 23.9% 102
Moises Alou 490 3.40 41.0% 41.8% 28.9% 7.5% 21.9% 8.1% 16.1% 41.8% 10.2% 23.3% 118
Garret Anderson 603 3.28 33.3% 42.1% 29.8% 9.1% 19.0% 10.6% 16.2% 32.0% 15.3% 25.5% 82
Marlon Anderson 260 3.88 20.8% 44.9% 30.1% 5.6% 19.4% 7.6% 15.6% 35.8% 19.9% 19.4% 106
Danny Ardoin 248 3.92 30.2% 48.7% 23.3% 12.0% 16.0% 12.7% 16.8% 35.1% 17.5% 15.6% 82
Garrett Atkins 573 3.61 19.4% 46.3% 25.5% 4.9% 23.3% 4.3% 13.8% 38.4% 21.2% 21.8% 146
Rich Aurilia 468 3.74 26.9% 41.6% 30.9% 8.8% 18.7% 7.7% 15.6% 37.7% 17.2% 20.8% 111
Brad Ausmus 451 3.65 33.7% 55.3% 23.3% 5.8% 15.6% 5.9% 16.7% 38.8% 15.9% 21.4% 118
Rod Barajas 450 3.80 26.4% 29.0% 34.5% 17.8% 18.4% 8.0% 22.6% 31.7% 16.4% 20.3% 71
Clint Barmes 377 3.57 18.6% 36.7% 30.4% 12.9% 20.1% 5.1% 18.0% 31.3% 20.5% 23.8% 93
Michael Barrett 477 3.57 32.9% 44.8% 27.7% 7.6% 20.1% 7.6% 15.8% 36.9% 16.8% 21.8% 108
Jason Bartlett 252 3.94 19.4% 47.9% 28.9% 11.1% 12.1% 5.3% 18.3% 35.2% 21.1% 19.1% 102
Jason Bay 707 3.87 25.7% 37.1% 36.0% 6.3% 20.7% 9.6% 16.1% 39.2% 17.4% 17.1% 105
David Bell 617 3.56 30.6% 41.3% 24.8% 13.3% 20.6% 7.0% 18.7% 35.5% 15.3% 22.8% 95
Mark Bellhorn 355 4.21 25.9% 40.8% 32.5% 8.4% 18.3% 11.9% 14.4% 42.5% 18.0% 12.8% 111
Ronnie Belliard 587 3.52 39.4% 45.3% 28.6% 8.6% 17.5% 7.3% 19.1% 33.9% 15.7% 23.2% 88
Adrian Beltre 650 3.96 29.7% 45.7% 29.7% 6.8% 17.8% 9.6% 20.1% 35.1% 15.1% 19.5% 81
Carlos Beltran 650 3.78 24.9% 46.6% 30.4% 8.9% 14.1% 6.7% 16.0% 38.3% 18.2% 20.3% 116
Gary Bennett 228 3.91 29.4% 43.5% 23.8% 11.9% 20.8% 6.3% 18.4% 37.9% 18.2% 19.0% 105
Lance Berkman 565 3.86 25.3% 46.0% 27.9% 7.3% 18.8% 7.0% 15.5% 43.5% 15.2% 18.6% 133
Angel Berroa 652 3.35 37.6% 51.6% 21.3% 9.8% 17.4% 11.3% 22.1% 26.3% 15.2% 23.6% 54
Yuniesky Betancourt 228 3.18 23.7% 37.7% 33.0% 12.6% 16.8% 7.3% 16.8% 31.9% 16.4% 26.3% 91
Wilson Betemit 274 3.92 24.1% 50.8% 24.4% 3.6% 21.3% 9.8% 16.7% 38.5% 16.3% 18.6% 100
Larry Bigbie 304 3.77 24.0% 57.1% 23.6% 0.9% 18.4% 10.8% 16.0% 38.2% 16.1% 18.6% 98
Craig Biggio 651 3.47 30.7% 44.2% 29.4% 12.6% 13.8% 9.4% 17.5% 32.6% 16.1% 22.5% 83
Casey Blake 583 4.28 16.6% 37.2% 36.0% 9.9% 16.9% 7.2% 16.6% 37.2% 21.7% 16.6% 107
Hank Blalock 705 3.74 33.3% 38.3% 29.3% 8.3% 24.1% 10.7% 17.2% 39.2% 12.9% 19.7% 97
Willie Bloomquist 267 3.40 35.2% 45.6% 24.0% 9.2% 21.2% 6.8% 19.5% 31.5% 16.9% 23.9% 82
Geoff Blum 351 3.81 21.1% 39.7% 28.5% 12.3% 19.5% 6.7% 16.7% 38.8% 16.7% 20.7% 114
Aaron Boone 565 3.62 34.9% 44.3% 25.2% 10.7% 19.8% 10.3% 15.1% 36.5% 15.9% 21.1% 99
Bret Boone 360 3.78 24.7% 39.9% 31.6% 9.5% 19.0% 9.4% 13.4% 37.7% 19.7% 19.4% 114
Milton Bradley 316 3.58 41.5% 47.3% 22.8% 9.1% 20.7% 8.2% 17.0% 39.1% 13.4% 21.4% 107
Russell Branyan 242 4.15 43.8% 30.1% 37.4% 8.1% 24.4% 19.1% 16.2% 41.6% 9.7% 12.7% 81
Ben Broussard 505 3.67 28.3% 41.2% 29.1% 8.6% 21.0% 9.7% 19.2% 35.3% 15.1% 20.2% 84
Emil Brown 609 3.74 31.5% 41.8% 29.0% 7.0% 22.2% 9.8% 16.6% 36.3% 17.1% 19.6% 94
John Buck 430 3.51 38.6% 43.2% 33.2% 8.4% 15.2% 10.0% 20.4% 33.5% 14.6% 20.6% 76
Chris Burke 359 3.69 29.0% 42.2% 29.9% 13.1% 14.9% 8.7% 18.0% 35.3% 15.6% 20.3% 91
Jeromy Burnitz 671 3.95 30.6% 43.0% 29.5% 11.2% 16.3% 10.3% 17.3% 37.6% 15.3% 19.0% 94
Pat Burrell 669 4.27 28.6% 29.0% 33.9% 12.5% 24.6% 9.5% 14.9% 42.2% 18.8% 14.4% 119
Sean Burroughs 317 3.69 20.8% 56.3% 22.3% 5.3% 16.2% 6.6% 17.2% 35.5% 18.2% 21.3% 103
Marlon Byrd 259 4.14 24.3% 39.9% 26.1% 12.8% 21.3% 8.9% 16.5% 35.3% 20.7% 17.6% 96
Eric Byrnes 456 3.67 33.6% 32.5% 26.7% 20.6% 20.3% 8.1% 18.9% 35.9% 15.7% 20.6% 91
Miguel Cabrera 685 3.84 26.4% 39.3% 31.0% 6.7% 23.1% 9.5% 18.6% 35.5% 17.1% 19.1% 87
Orlando Cabrera 587 3.50 27.9% 42.3% 30.0% 10.7% 16.9% 5.9% 16.8% 34.9% 17.0% 24.3% 106
Miguel Cairo 367 3.49 25.6% 47.3% 25.6% 9.9% 17.3% 4.8% 17.9% 32.3% 19.2% 24.6% 98
Mike Cameron 343 4.06 21.6% 44.0% 33.3% 6.7% 16.0% 8.8% 15.7% 37.9% 20.6% 16.2% 106
Robinson Cano 551 3.05 35.8% 51.5% 23.7% 6.7% 18.3% 6.2% 16.9% 31.2% 17.4% 27.7% 93
Jorge Cantu 631 3.29 32.5% 40.5% 33.1% 5.5% 20.7% 11.1% 19.7% 29.3% 13.5% 25.3% 65
Jamey Carroll 358 4.09 19.8% 56.1% 19.3% 3.0% 22.0% 4.5% 17.2% 35.9% 22.6% 18.1% 113
Sean Casey 587 3.53 18.9% 51.6% 26.5% 3.3% 18.5% 3.7% 16.2% 36.3% 19.6% 23.6% 125
Juan Castro 292 3.16 37.3% 53.3% 21.7% 8.6% 16.4% 7.6% 18.7% 27.8% 18.3% 26.5% 73
Jose Castillo 398 3.51 38.2% 45.9% 30.7% 4.1% 19.3% 10.5% 20.6% 32.3% 13.4% 22.8% 71
Luis Castillo 524 3.94 16.6% 64.6% 13.4% 3.1% 19.0% 2.1% 12.4% 39.3% 23.3% 20.7% 187
Ramon Castro 240 3.86 23.3% 37.6% 32.5% 12.7% 17.2% 9.1% 15.0% 36.9% 21.4% 17.1% 105
Vinny Castilla 549 3.52 37.0% 44.4% 28.8% 10.1% 16.8% 10.7% 17.3% 35.3% 14.3% 21.9% 87
Frank Catalanotto 475 3.89 21.5% 44.3% 28.8% 6.9% 20.0% 4.4% 17.7% 36.8% 19.8% 20.3% 115
Eric Chavez 694 3.93 33.7% 38.4% 34.1% 10.7% 16.8% 9.3% 19.1% 36.3% 16.3% 18.6% 88
Hee-Seop Choi 368 4.02 30.7% 39.0% 31.3% 5.7% 24.0% 10.6% 17.4% 38.8% 15.6% 16.7% 95
Ryan Church 301 3.75 30.9% 49.0% 23.8% 6.9% 20.3% 11.9% 18.0% 36.2% 15.1% 17.9% 83
Alex Cintron 348 3.18 44.5% 44.0% 30.1% 7.3% 18.5% 8.1% 22.2% 30.0% 11.3% 27.6% 68
Jeff Cirillo 219 3.56 23.3% 51.8% 24.7% 8.2% 15.3% 5.3% 13.3% 38.3% 19.4% 21.8% 142
Brady Clark 674 3.55 23.4% 38.4% 29.8% 9.6% 22.2% 4.6% 16.0% 35.4% 19.7% 23.2% 118
Tony Clark 393 3.92 32.6% 43.8% 31.5% 6.0% 18.7% 13.2% 16.9% 37.6% 14.4% 17.5% 86
Royce Clayton 573 3.84 28.6% 59.4% 19.6% 3.0% 18.2% 8.1% 18.3% 34.9% 17.8% 19.5% 91
JD Closser 272 3.99 27.9% 43.5% 27.2% 10.5% 18.8% 9.2% 16.7% 39.3% 16.7% 17.7% 104
Jeff Conine 384 3.87 24.5% 40.4% 27.0% 7.4% 25.3% 5.3% 13.9% 40.0% 20.3% 19.3% 143
Alex Cora 273 3.85 22.0% 54.2% 29.5% 3.5% 13.2% 3.1% 17.4% 33.0% 22.8% 21.6% 110
Humberto Cota 320 3.60 41.6% 38.9% 29.4% 10.0% 21.7% 16.3% 22.2% 30.8% 11.1% 19.3% 55
Craig Counsell 670 4.08 14.3% 50.3% 23.1% 9.5% 17.3% 2.9% 13.8% 39.4% 23.9% 18.9% 163
Carl Crawford 687 3.23 40.6% 44.8% 29.6% 6.5% 19.1% 9.4% 20.4% 29.0% 14.1% 25.8% 67
Joe Crede 471 3.61 36.9% 36.8% 30.4% 18.5% 14.2% 9.9% 23.6% 32.2% 11.2% 22.0% 66
Coco Crisp 656 3.48 34.9% 49.2% 26.6% 7.9% 16.6% 6.1% 15.5% 36.1% 17.4% 23.3% 114
Bobby Crosby 371 4.19 15.9% 55.5% 22.1% 6.8% 15.7% 7.6% 13.0% 39.7% 21.1% 18.1% 132
Deivi Cruz 275 3.30 32.4% 52.4% 21.8% 10.0% 15.7% 9.4% 20.3% 29.0% 14.3% 25.4% 67
Jose Cruz 437 3.92 32.5% 42.2% 32.2% 9.3% 16.3% 13.0% 15.1% 41.5% 14.2% 15.8% 101
Michael Cuddyer 470 3.81 26.4% 52.6% 21.6% 8.4% 17.4% 9.6% 15.7% 35.2% 20.3% 18.8% 96
Johnny Damon 688 3.71 24.9% 44.5% 28.7% 9.6% 17.2% 5.4% 20.4% 35.2% 16.4% 22.2% 94
Jeff DaVanon 271 4.06 28.8% 47.8% 33.9% 4.3% 14.0% 5.2% 16.0% 42.6% 18.1% 17.0% 138
David DeJesus 523 3.78 18.4% 46.7% 23.5% 8.3% 21.5% 4.0% 14.1% 37.3% 23.2% 20.1% 142
Carlos Delgado 616 3.85 31.7% 40.4% 33.5% 6.7% 19.5% 12.1% 17.6% 37.6% 13.8% 17.7% 87
David Dellucci 518 4.22 21.6% 41.1% 34.5% 7.6% 16.8% 9.8% 14.0% 41.3% 19.9% 14.5% 119
Victor Diaz 313 3.67 45.0% 49.5% 33.0% 2.0% 15.5% 15.5% 19.6% 33.9% 12.2% 17.8% 66
Ryan Doumit 257 3.32 40.9% 50.3% 26.5% 8.1% 15.1% 12.5% 16.5% 33.5% 14.3% 21.8% 79
JD Drew 311 3.88 36.3% 47.3% 30.7% 8.3% 13.7% 7.7% 15.5% 43.4% 15.6% 17.1% 129
Jason Dubois 202 3.82 45.0% 46.0% 32.7% 5.3% 15.9% 18.7% 21.0% 34.4% 10.6% 14.7% 60
Adam Dunn 671 4.24 25.2% 36.1% 35.8% 10.3% 17.8% 10.1% 15.3% 42.5% 17.9% 13.5% 115
Ray Durham 560 3.58 28.9% 46.6% 26.5% 6.7% 20.2% 5.5% 15.9% 38.7% 16.5% 22.3% 125
Jermaine Dye 579 3.99 15.9% 38.4% 31.3% 12.3% 18.1% 8.2% 16.2% 35.2% 20.8% 18.7% 99
Damion Easley 304 3.76 25.3% 41.0% 27.3% 10.1% 21.6% 6.1% 13.4% 38.1% 21.1% 20.1% 134
David Eckstein 713 4.01 9.5% 48.0% 24.1% 7.9% 20.2% 1.9% 16.7% 34.5% 24.7% 20.9% 128
Jim Edmonds 567 4.17 33.7% 33.9% 39.3% 6.9% 19.8% 10.2% 16.3% 42.1% 16.6% 14.3% 109
Mike Edwards 258 3.59 22.9% 51.5% 18.9% 6.3% 23.3% 5.6% 16.3% 34.1% 21.3% 22.2% 107
Brad Eldred 208 3.60 44.7% 30.4% 37.4% 14.8% 17.4% 24.4% 17.1% 31.0% 11.5% 15.4% 51
Jason Ellison 386 3.41 31.1% 56.5% 21.3% 8.6% 13.7% 5.3% 15.8% 34.0% 18.9% 24.0% 111
Mark Ellis 486 3.99 10.9% 45.2% 28.9% 6.5% 19.4% 4.8% 15.4% 35.5% 23.7% 19.9% 121
Edwin Encarnacion 234 3.99 28.2% 40.4% 22.5% 13.2% 23.8% 10.0% 17.7% 38.4% 16.8% 16.3% 95
Juan Encarnacion 563 3.78 25.9% 40.8% 27.4% 11.7% 20.0% 11.3% 16.3% 33.7% 18.1% 19.3% 84
Morgan Ensberg 624 3.95 38.5% 37.4% 36.7% 10.7% 15.3% 9.7% 15.2% 40.5% 17.1% 16.9% 112
Darin Erstad 667 3.84 12.0% 47.0% 27.9% 6.7% 17.9% 6.9% 15.5% 34.2% 22.6% 19.9% 105
Johnny Estrada 383 3.29 37.3% 36.6% 36.6% 7.1% 19.6% 8.3% 24.2% 29.3% 11.6% 26.0% 62
Adam Everett 595 3.53 29.7% 41.9% 30.8% 12.7% 14.6% 9.8% 17.4% 31.7% 18.2% 21.8% 80
Carl Everett 547 3.73 34.7% 43.4% 31.9% 10.0% 14.7% 11.8% 20.5% 34.0% 13.2% 19.7% 72
Pedro Feliz 615 3.43 43.3% 43.7% 30.4% 10.1% 15.8% 10.5% 18.7% 33.9% 14.1% 22.5% 80
Robert Fick 260 3.71 27.7% 43.0% 30.0% 10.5% 16.5% 5.3% 13.0% 39.1% 20.4% 20.9% 147
Chone Figgins 720 3.91 21.1% 42.5% 31.7% 9.0% 16.8% 5.6% 15.4% 38.6% 19.6% 19.8% 126
Steve Finley 440 3.84 20.5% 39.7% 28.5% 15.3% 16.5% 7.1% 18.3% 34.9% 18.8% 20.2% 94
Cliff Floyd 626 3.70 37.2% 41.0% 31.3% 11.7% 16.1% 10.8% 19.9% 36.9% 11.5% 20.0% 82
Lew Ford 590 3.98 21.7% 54.3% 23.6% 8.1% 14.0% 5.6% 17.2% 36.3% 20.1% 19.0% 109
Julio Franco 265 4.08 21.9% 57.8% 23.3% 0.6% 18.3% 10.8% 18.1% 37.0% 17.0% 16.7% 88
Jeff Francoeur 274 3.41 47.4% 39.3% 31.8% 13.4% 15.4% 15.9% 21.9% 29.2% 9.7% 21.8% 53
Ryan Freel 432 4.06 18.8% 52.5% 18.5% 6.4% 22.3% 4.6% 13.7% 40.3% 22.6% 17.9% 152
Rafael Furcal 689 3.81 18.7% 49.4% 22.3% 9.1% 19.4% 4.5% 15.9% 37.2% 20.2% 20.9% 125
Nomar Garciaparra 247 3.19 46.2% 42.1% 26.3% 11.0% 20.6% 7.9% 20.9% 35.5% 8.4% 26.5% 85
Joey Gathright 218 3.74 37.6% 73.7% 11.4% 3.0% 12.0% 8.6% 20.5% 32.1% 16.1% 20.5% 76
Jason Giambi 545 4.21 21.5% 34.6% 34.6% 11.0% 19.4% 6.7% 14.6% 43.7% 20.2% 13.5% 141
Jay Gibbons 518 3.57 27.0% 37.0% 33.9% 13.2% 15.9% 6.7% 20.9% 33.9% 14.7% 23.5% 84
Brian Giles 674 3.92 22.8% 37.6% 30.7% 8.8% 22.9% 2.8% 12.9% 45.9% 19.5% 18.7% 202
Marcus Giles 654 3.71 40.2% 41.7% 36.9% 5.2% 16.1% 8.7% 21.0% 37.8% 11.8% 19.7% 87
Troy Glaus 634 4.12 24.8% 36.2% 39.4% 8.0% 16.3% 12.6% 14.1% 41.9% 15.7% 15.3% 108
Chris Gomez 254 3.71 17.7% 53.6% 21.1% 6.2% 19.1% 2.7% 13.8% 39.8% 20.9% 22.3% 166
Jonny Gomes 407 3.88 31.4% 28.2% 38.2% 12.0% 21.6% 13.1% 14.5% 38.1% 17.9% 15.3% 95
Alex Gonzalez 383 3.77 27.2% 49.6% 24.3% 6.4% 19.6% 8.8% 16.7% 34.4% 19.7% 19.4% 93
Alex Gonzalez 478 3.53 33.3% 38.2% 29.6% 15.8% 16.6% 10.1% 19.3% 30.3% 16.4% 21.9% 71
Luis Gonzalez 672 3.94 17.4% 39.4% 31.8% 11.8% 17.0% 6.1% 18.4% 39.9% 15.7% 18.9% 112
Luis Gonzalez 442 3.30 26.0% 52.8% 23.3% 4.5% 19.3% 8.3% 16.9% 32.5% 17.0% 24.2% 89
Ruben Gotay 317 3.89 22.7% 40.4% 33.3% 13.3% 12.9% 7.0% 15.6% 36.6% 19.9% 19.4% 111
Tony Graffanino 417 3.60 22.1% 50.5% 21.1% 7.9% 20.5% 5.8% 14.4% 35.4% 21.3% 22.1% 120
Khalil Greene 476 3.75 25.8% 34.4% 34.4% 12.5% 18.8% 10.0% 18.5% 33.5% 17.4% 19.8% 81
Nick Green 375 3.86 23.5% 39.2% 32.2% 9.4% 19.6% 8.8% 19.0% 35.0% 18.5% 16.9% 86
Shawn Green 656 3.60 29.6% 51.6% 24.3% 7.3% 16.8% 9.0% 17.8% 37.8% 14.1% 21.1% 97
Ken Griffey 555 3.79 34.1% 35.6% 30.9% 9.9% 23.7% 7.4% 19.6% 39.3% 13.8% 19.3% 100
Mark Grudzielanek 563 3.50 29.1% 47.4% 24.1% 6.5% 22.0% 8.4% 18.5% 32.4% 17.2% 22.9% 83
Vladimir Guerrero 594 3.25 43.6% 44.0% 32.5% 9.0% 14.5% 8.8% 20.1% 37.4% 7.0% 26.0% 89
Carlos Guillen 361 3.68 31.3% 45.2% 24.1% 7.9% 22.8% 7.0% 18.7% 35.8% 15.6% 22.0% 96
Jose Guillen 611 3.47 26.7% 44.4% 25.3% 9.6% 20.7% 9.7% 18.7% 34.0% 14.6% 21.9% 82
Cristian Guzman 492 3.19 33.3% 57.2% 19.2% 6.4% 17.9% 6.6% 14.3% 31.3% 20.6% 25.3% 103
Travis Hafner 578 4.16 31.8% 44.1% 35.7% 4.4% 15.8% 10.8% 17.3% 41.4% 14.4% 15.4% 101
Jerry Hairston 430 3.79 18.8% 45.5% 24.0% 10.3% 20.2% 4.7% 17.9% 32.6% 21.5% 20.9% 99
Bill Hall 546 4.16 22.3% 41.9% 26.3% 6.0% 25.8% 8.4% 16.3% 36.6% 20.2% 17.8% 102
Toby Hall 463 3.30 30.9% 39.2% 28.3% 12.4% 20.1% 4.8% 24.1% 29.6% 14.2% 26.4% 70
JJ Hardy 427 3.57 12.2% 45.8% 25.7% 12.0% 17.1% 5.3% 11.3% 35.8% 24.1% 22.3% 148
Scott Hatteberg 523 3.86 16.4% 47.3% 25.1% 9.7% 17.9% 3.4% 12.7% 39.6% 22.8% 20.7% 169
Brad Hawpe 351 3.87 35.6% 52.1% 25.2% 7.6% 15.1% 10.8% 16.9% 39.7% 14.5% 17.6% 98
Todd Helton 626 4.13 27.2% 33.6% 35.7% 7.0% 23.7% 4.9% 21.0% 40.6% 15.6% 17.2% 108
Jose Hernandez 256 3.70 32.4% 50.6% 20.6% 10.0% 18.9% 13.3% 16.5% 33.2% 17.4% 19.0% 77
Ramon Hernandez 392 3.58 29.8% 47.4% 28.2% 6.3% 18.0% 6.9% 17.8% 35.0% 15.9% 23.7% 97
Richard Hidalgo 339 3.88 26.5% 32.8% 36.2% 16.2% 14.9% 8.8% 17.6% 36.6% 18.6% 17.9% 96
Aaron Hill 407 3.45 32.7% 41.0% 28.7% 8.6% 21.7% 6.0% 15.3% 37.2% 17.5% 23.3% 120
Shea Hillenbrand 645 3.37 24.5% 42.7% 25.3% 11.6% 20.5% 7.6% 17.8% 32.7% 16.9% 23.9% 88
Eric Hinske 537 3.88 28.3% 40.3% 32.9% 7.5% 19.3% 12.0% 18.1% 37.4% 14.1% 17.5% 85
Damon Hollins 369 3.75 20.9% 43.3% 27.0% 14.5% 15.2% 9.1% 16.5% 35.1% 18.3% 20.4% 94
Matt Holliday 526 3.63 38.2% 47.3% 27.7% 7.7% 17.3% 9.5% 20.1% 36.6% 11.9% 21.2% 85
Todd Hollandsworth 330 3.43 39.4% 46.3% 27.9% 10.4% 15.4% 12.4% 21.0% 33.2% 11.5% 21.3% 68
Ryan Howard 348 3.94 32.2% 44.4% 32.2% 1.9% 21.5% 14.8% 18.0% 37.4% 13.8% 15.9% 79
Orlando Hudson 501 3.58 21.6% 52.4% 25.1% 4.7% 17.9% 6.5% 15.8% 37.5% 17.3% 22.5% 116
Aubrey Huff 636 3.63 24.8% 47.6% 31.0% 7.3% 14.2% 7.4% 15.3% 38.0% 17.0% 21.8% 115
Torii Hunter 416 3.47 36.3% 48.9% 28.0% 9.3% 13.8% 10.2% 18.7% 35.8% 12.9% 21.7% 85
Raul Ibanez 690 3.96 20.6% 44.6% 28.6% 6.9% 19.9% 7.2% 17.4% 39.4% 16.7% 19.1% 110
Tadahito Iguchi 582 3.84 27.1% 50.8% 26.5% 6.3% 16.1% 9.9% 17.7% 36.6% 15.7% 18.6% 91
Omar Infante 434 3.52 24.4% 35.3% 32.7% 16.9% 15.2% 8.3% 16.2% 31.9% 20.4% 22.5% 89
Brandon Inge 694 4.00 18.9% 40.4% 33.8% 7.4% 18.4% 8.6% 14.9% 36.9% 21.2% 17.6% 108
Cesar Izturis 478 3.64 16.5% 54.0% 20.1% 6.5% 19.6% 3.1% 15.0% 35.1% 22.5% 22.9% 134
Maicer Izturis 210 3.73 12.4% 44.8% 29.1% 7.6% 18.6% 3.3% 13.2% 37.1% 23.4% 22.1% 154
Damian Jackson 313 3.92 25.9% 41.9% 25.2% 11.1% 21.8% 6.2% 15.4% 38.9% 19.5% 19.1% 124
Geoff Jenkins 618 3.75 36.9% 40.0% 29.1% 6.9% 24.0% 14.0% 20.6% 35.4% 11.2% 17.7% 70
Derek Jeter 752 3.82 32.3% 60.5% 20.5% 1.5% 17.6% 7.4% 18.8% 38.7% 15.0% 19.1% 101
Dan Johnson 434 4.18 22.1% 40.2% 32.6% 8.8% 18.4% 5.1% 14.8% 39.7% 21.9% 18.3% 137
Kelly Johnson 334 4.13 27.5% 45.9% 26.1% 8.7% 19.3% 8.7% 17.5% 39.6% 17.9% 15.9% 104
Nick Johnson 547 4.10 15.7% 44.3% 28.3% 7.6% 19.8% 5.4% 16.4% 41.1% 19.4% 16.6% 130
Reed Johnson 439 3.90 18.5% 51.4% 20.7% 6.9% 21.0% 9.7% 18.0% 31.7% 19.8% 18.7% 79
Andruw Jones 672 3.82 34.1% 41.8% 33.7% 9.6% 15.0% 12.9% 16.7% 36.8% 13.7% 19.0% 86
Chipper Jones 432 4.02 25.5% 42.4% 35.5% 3.0% 19.1% 5.8% 13.5% 46.9% 16.0% 17.7% 168
Jacque Jones 585 3.57 40.0% 59.4% 22.7% 4.4% 13.4% 13.4% 16.6% 37.7% 11.1% 20.0% 86
Brian Jordan 251 3.52 33.5% 52.1% 28.7% 6.9% 12.2% 11.5% 18.3% 34.8% 13.5% 21.3% 80
Austin Kearns 448 3.98 33.0% 48.1% 23.9% 6.7% 21.4% 10.8% 16.5% 39.2% 16.8% 16.1% 99
Jason Kendall 676 3.93 6.4% 51.3% 23.3% 4.2% 21.2% 2.7% 13.2% 35.6% 26.1% 21.3% 154
Adam Kennedy 460 3.73 30.9% 43.8% 32.4% 6.4% 17.2% 6.6% 20.4% 35.4% 14.5% 21.1% 90
Jeff Kent 637 3.55 32.3% 31.6% 37.7% 11.9% 18.9% 9.1% 16.2% 40.0% 13.0% 21.2% 109
Bobby Kielty 433 3.89 28.2% 44.3% 29.3% 6.7% 19.7% 6.5% 16.2% 38.1% 20.2% 18.8% 116
Ryan Klesko 520 3.77 34.2% 42.6% 30.8% 11.5% 15.1% 9.0% 17.9% 42.7% 11.4% 18.6% 109
Paul Konerko 664 4.16 22.9% 33.0% 34.8% 11.3% 20.9% 8.1% 17.3% 38.8% 18.3% 17.2% 105
Corey Koskie 404 4.14 27.2% 47.0% 28.6% 7.9% 16.5% 11.1% 17.2% 39.4% 15.5% 16.0% 96
Mark Kotsay 629 3.43 37.2% 41.2% 30.7% 6.1% 22.0% 4.6% 19.4% 35.1% 15.0% 25.1% 100
Mike Lamb 349 3.72 30.1% 42.9% 26.8% 12.6% 17.6% 9.2% 22.0% 34.0% 14.3% 20.1% 75
Jason Lane 561 3.63 35.8% 31.0% 39.9% 13.7% 15.6% 10.9% 19.7% 34.6% 13.8% 20.5% 78
Ryan Langerhans 373 3.75 33.8% 41.4% 28.9% 6.3% 23.4% 10.8% 14.4% 39.4% 15.7% 18.5% 108
Adam LaRoche 502 3.46 35.9% 44.9% 33.1% 5.6% 16.4% 10.3% 17.1% 36.8% 13.3% 21.7% 92
Jason LaRue 422 3.78 38.6% 42.7% 29.2% 7.5% 20.6% 14.1% 16.5% 38.4% 12.7% 16.9% 86
Matt Lawton 585 3.85 21.4% 53.4% 22.2% 7.3% 17.1% 5.9% 13.7% 40.8% 19.9% 19.0% 143
Matthew LeCroy 350 4.21 22.6% 40.9% 32.7% 7.7% 18.6% 12.2% 18.3% 38.5% 15.6% 15.0% 87
Ricky Ledee 266 3.71 27.4% 37.8% 34.0% 8.0% 20.2% 9.9% 15.3% 38.4% 16.7% 19.1% 105
Carlos Lee 688 3.79 25.4% 34.9% 33.9% 13.3% 17.9% 7.3% 18.5% 36.2% 16.6% 21.0% 96
Derrek Lee 691 4.03 29.2% 39.6% 33.9% 6.3% 20.1% 8.4% 16.4% 40.3% 16.3% 18.2% 112
Travis Lee 441 3.91 24.7% 43.7% 29.5% 6.8% 20.1% 7.6% 16.9% 37.6% 17.8% 19.9% 105
Mike Lieberthal 443 3.42 28.4% 37.8% 29.8% 12.7% 19.6% 5.3% 17.7% 35.1% 16.2% 24.8% 105
Paul Lo Duca 496 3.68 19.4% 45.9% 23.9% 8.0% 22.2% 3.0% 15.7% 36.6% 20.5% 23.6% 135
Kenny Lofton 406 3.58 22.9% 51.1% 22.7% 3.9% 22.4% 2.6% 14.3% 39.4% 19.3% 22.9% 160
Nook Logan 356 3.37 35.7% 57.1% 15.6% 11.7% 15.6% 8.1% 14.6% 32.9% 17.8% 23.7% 99
Terrence Long 489 3.46 27.0% 47.1% 24.3% 7.2% 21.3% 6.3% 18.5% 36.0% 15.1% 23.8% 100
Felipe Lopez 648 3.96 15.9% 53.7% 25.5% 2.5% 18.6% 5.6% 13.4% 38.4% 23.0% 18.7% 139
Javy Lopez 423 3.44 28.8% 45.3% 18.5% 12.5% 23.7% 8.1% 19.2% 32.5% 16.7% 22.7% 82
Jose Lopez 203 3.76 26.6% 41.7% 26.8% 12.5% 19.0% 6.3% 22.4% 30.0% 17.8% 22.0% 72
Mark Loretta 463 3.93 19.9% 41.0% 26.3% 8.2% 24.5% 3.3% 16.5% 38.0% 20.1% 20.8% 132
Mike Lowell 558 3.65 24.2% 32.2% 37.7% 11.9% 18.1% 5.5% 16.8% 36.4% 18.5% 22.3% 112
Julio Lugo 690 3.62 28.3% 49.0% 25.8% 6.4% 18.9% 6.2% 14.3% 37.1% 18.7% 22.1% 125
John Mabry 274 3.48 50.0% 46.6% 26.7% 5.2% 21.5% 13.8% 20.6% 36.1% 9.0% 20.1% 72
Rob Mackowiak 512 3.67 33.8% 50.0% 26.0% 4.4% 19.7% 10.2% 20.3% 35.6% 13.6% 19.7% 80
Tino Martinez 348 3.82 22.4% 48.2% 27.7% 11.1% 13.0% 7.9% 14.9% 38.9% 18.8% 19.2% 117
Victor Martinez 622 3.80 34.9% 48.3% 28.6% 4.4% 18.7% 6.6% 18.8% 37.6% 15.9% 20.4% 102
Mike Matheny 485 3.53 39.6% 40.9% 30.1% 7.2% 21.7% 11.0% 21.3% 31.1% 14.2% 21.4% 66
Luis Matos 433 3.56 23.8% 44.7% 27.2% 10.7% 17.5% 7.1% 16.7% 32.6% 20.0% 21.9% 94
Hideki Matsui 704 3.71 25.7% 47.0% 31.3% 5.4% 16.3% 5.2% 16.1% 39.2% 17.6% 21.7% 127
Kazuo Matsui 295 3.70 22.0% 54.5% 19.3% 6.9% 19.3% 8.1% 17.9% 33.2% 18.3% 21.4% 88
Gary Matthews 526 3.82 27.9% 50.4% 24.4% 7.7% 17.5% 7.8% 15.8% 38.3% 18.3% 19.4% 112
Joe Mauer 554 3.86 13.2% 53.5% 24.5% 1.9% 20.1% 5.3% 14.5% 38.3% 20.9% 20.4% 133
Brian McCann 204 3.73 31.9% 42.8% 30.8% 8.2% 18.2% 8.1% 17.9% 37.4% 14.5% 21.4% 99
Quinton McCracken 246 3.80 26.4% 49.2% 25.7% 4.3% 20.9% 7.5% 16.6% 36.4% 17.9% 20.3% 104
Dallas McPherson 220 3.83 41.8% 35.5% 44.0% 2.8% 17.7% 17.7% 20.0% 34.2% 10.9% 16.7% 62
Kevin Mench 615 3.81 21.6% 37.0% 32.3% 13.0% 17.7% 7.3% 16.3% 36.7% 18.1% 21.1% 107
Jason Michaels 343 4.10 17.8% 41.2% 30.4% 6.0% 22.4% 4.9% 14.6% 41.4% 20.3% 17.8% 145
Doug Mientkiewicz 313 4.00 17.3% 50.4% 27.5% 8.3% 13.8% 4.3% 17.1% 39.6% 18.8% 19.5% 127
Aaron Miles 347 3.31 31.1% 55.9% 15.8% 6.4% 21.9% 5.6% 18.4% 27.4% 19.6% 26.0% 78
Damian Miller 431 3.86 30.4% 50.0% 23.0% 3.4% 23.6% 10.4% 20.1% 35.9% 14.8% 18.0% 81
Kevin Millar 519 3.90 25.4% 33.7% 36.8% 10.4% 19.1% 4.7% 20.6% 36.5% 18.7% 18.9% 99
Chad Moeller 216 3.67 40.7% 46.8% 27.3% 13.0% 13.6% 11.4% 17.5% 35.5% 14.4% 19.5% 84
Dustan Mohr 293 3.80 41.0% 31.0% 37.4% 10.9% 20.7% 16.2% 17.5% 37.1% 13.1% 15.7% 76
Bengie Molina 449 3.52 25.6% 41.6% 33.9% 7.6% 16.8% 5.7% 20.1% 33.4% 15.9% 24.1% 89
Jose Molina 203 3.91 43.3% 50.3% 21.8% 10.2% 17.7% 10.8% 20.9% 33.4% 15.4% 18.5% 72
Yadier Molina 421 3.24 36.3% 54.1% 25.4% 6.8% 13.9% 6.1% 18.0% 33.0% 14.9% 27.1% 94
Craig Monroe 623 3.60 35.0% 49.1% 25.6% 8.7% 16.7% 9.3% 18.7% 35.3% 14.4% 21.8% 87
Melvin Mora 664 3.97 21.2% 38.8% 31.7% 10.8% 18.9% 8.3% 17.4% 36.4% 17.4% 18.7% 98
Justin Morneau 543 3.53 34.1% 41.1% 30.9% 10.0% 18.0% 10.5% 19.2% 35.3% 13.4% 21.3% 82
Mike Morse 258 3.67 33.3% 46.7% 29.1% 3.3% 20.9% 9.9% 16.1% 35.9% 17.3% 19.2% 95
Bill Mueller 590 3.64 26.9% 43.0% 31.9% 7.1% 18.0% 4.2% 17.2% 38.5% 18.4% 21.1% 124
Xavier Nady 356 3.56 25.8% 44.2% 29.6% 5.8% 20.4% 9.6% 17.5% 34.6% 16.9% 20.5% 88
Phil Nevin 414 3.94 29.5% 43.4% 28.1% 9.7% 18.8% 12.8% 17.5% 36.0% 15.8% 17.7% 82
David Newhan 249 3.55 29.3% 53.9% 23.3% 6.7% 16.1% 9.4% 16.0% 36.8% 15.8% 20.5% 99
Lance Niekro 302 3.44 36.1% 39.6% 29.6% 13.0% 17.8% 11.6% 20.1% 32.9% 12.8% 22.2% 71
Laynce Nix 240 3.54 34.6% 48.9% 24.7% 8.1% 18.3% 9.2% 21.3% 32.1% 14.7% 22.1% 72
Trot Nixon 470 3.69 25.5% 39.4% 35.5% 8.5% 16.6% 6.6% 13.1% 42.0% 17.5% 20.6% 147
Miguel Olivo 281 3.67 34.2% 48.4% 24.7% 11.6% 15.3% 17.1% 18.1% 30.2% 14.9% 18.5% 59
Magglio Ordonez 343 3.50 37.3% 44.0% 28.9% 6.9% 20.2% 7.8% 17.8% 38.5% 12.7% 23.1% 104
David Ortiz 713 4.00 30.7% 32.7% 40.7% 8.2% 18.3% 9.1% 17.1% 42.5% 13.8% 17.3% 112
Lyle Overbay 622 3.96 32.5% 52.0% 26.6% 2.3% 19.1% 8.5% 16.3% 40.6% 16.1% 18.2% 113
Pablo Ozuna 217 3.16 47.9% 75.6% 7.8% 5.0% 12.2% 5.8% 20.8% 28.1% 15.2% 26.2% 72
Orlando Palmeiro 231 3.76 24.7% 45.0% 26.5% 7.9% 20.6% 2.8% 16.4% 32.8% 24.3% 21.8% 118
Rafael Palmeiro 422 3.65 31.8% 41.6% 32.9% 7.5% 18.0% 5.9% 17.4% 41.3% 13.2% 21.9% 122
Corey Patterson 483 3.37 49.1% 51.0% 22.0% 11.7% 14.7% 14.5% 20.4% 30.9% 11.1% 21.1% 61
Jay Payton 435 3.36 38.2% 41.2% 30.8% 10.4% 17.6% 6.0% 19.2% 33.6% 16.1% 25.0% 92
Carlos Pena 295 3.93 42.7% 38.8% 30.3% 11.5% 19.4% 17.9% 17.9% 36.9% 12.1% 14.3% 71
Wily Mo Pena 335 3.77 44.5% 47.4% 22.4% 7.7% 22.4% 19.0% 16.0% 35.8% 12.9% 15.5% 70
Jhonny Peralta 570 4.00 22.8% 46.2% 31.2% 5.0% 17.6% 10.0% 17.7% 37.9% 17.0% 16.8% 94
Antonio Perez 287 4.19 20.9% 44.0% 28.5% 5.5% 22.0% 8.5% 17.8% 37.4% 18.2% 16.7% 98
Neifi Perez 609 3.23 33.7% 46.8% 24.0% 11.6% 17.7% 5.4% 19.7% 30.6% 15.0% 27.6% 84
Jason Phillips 434 3.54 31.6% 46.2% 24.5% 10.1% 19.2% 8.3% 21.2% 33.5% 13.3% 23.3% 78
Mike Piazza 442 3.48 31.0% 45.9% 28.7% 7.3% 18.1% 10.6% 16.3% 36.5% 14.0% 21.8% 93
AJ Pierzynski 497 3.56 43.5% 45.9% 26.9% 6.9% 20.3% 10.0% 24.5% 31.1% 10.4% 22.5% 62
Juan Pierre 719 3.71 11.4% 59.5% 18.6% 3.8% 18.1% 2.5% 14.0% 34.3% 23.3% 23.5% 142
Scott Podsednik 568 3.89 18.5% 58.9% 19.6% 6.1% 15.3% 3.0% 13.3% 37.9% 23.8% 20.0% 160
Placido Polanco 551 3.50 15.4% 47.9% 22.4% 4.4% 25.3% 3.4% 14.6% 35.9% 19.8% 25.0% 137
Jorge Posada 546 3.82 26.9% 41.9% 32.8% 7.3% 18.0% 7.8% 15.2% 40.8% 17.2% 18.6% 122
Albert Pujols 700 3.88 20.1% 41.4% 29.9% 10.0% 18.7% 5.6% 15.5% 40.5% 17.5% 20.2% 132
Nick Punto 439 4.03 32.6% 54.6% 23.3% 6.0% 16.1% 4.8% 18.2% 36.6% 19.6% 17.9% 110
Aramis Ramirez 506 3.62 30.4% 38.3% 34.6% 8.4% 18.8% 8.8% 19.2% 36.2% 13.0% 22.3% 89
Manny Ramirez 650 4.06 30.5% 38.3% 34.0% 6.8% 20.9% 9.4% 18.9% 38.9% 15.4% 16.9% 95
Joe Randa 609 3.70 17.1% 37.6% 33.6% 6.7% 22.3% 6.0% 15.3% 36.9% 19.9% 21.2% 119
Tike Redman 344 3.51 23.5% 51.5% 25.6% 5.4% 17.5% 3.5% 15.8% 35.0% 19.9% 24.6% 125
Jeremy Reed 544 3.72 22.8% 49.8% 24.8% 7.9% 17.6% 5.4% 18.0% 38.5% 16.5% 20.8% 113
Desi Relaford 238 3.95 17.6% 45.9% 23.5% 14.7% 15.9% 5.8% 15.1% 40.0% 19.9% 18.2% 132
Edgar Renteria 692 3.65 29.5% 48.5% 25.5% 6.2% 19.9% 7.1% 16.1% 37.3% 17.6% 21.1% 111
Jason Repko 301 3.88 20.6% 45.5% 28.8% 12.1% 13.6% 10.9% 16.6% 34.4% 18.4% 17.0% 86
Jose Reyes 733 3.62 26.5% 48.7% 26.0% 8.6% 16.6% 5.4% 16.8% 31.5% 20.5% 23.6% 98
Alex Rios 519 3.56 25.8% 48.8% 28.3% 4.2% 18.7% 8.0% 17.4% 35.3% 17.6% 20.9% 96
Juan Rivera 376 3.64 26.9% 44.7% 27.5% 11.0% 16.8% 6.4% 15.9% 37.3% 17.4% 22.6% 115
Brian Roberts 640 3.68 33.8% 36.6% 31.8% 7.4% 24.2% 5.5% 21.3% 35.8% 16.1% 20.8% 92
Dave Roberts 480 3.85 14.6% 54.2% 22.1% 4.9% 18.8% 3.5% 11.3% 39.6% 24.2% 20.0% 183
Oscar Robles 399 4.02 7.0% 46.8% 24.6% 6.9% 21.6% 2.1% 12.0% 38.3% 26.3% 20.7% 186
Alex Rodriguez 715 3.91 30.6% 45.4% 32.4% 6.0% 16.2% 10.6% 15.7% 39.7% 16.2% 16.9% 104
Ivan Rodriguez 525 3.33 34.7% 48.9% 25.8% 5.5% 19.8% 12.2% 22.2% 29.0% 11.5% 24.1% 58
Luis Rodriguez 203 3.63 22.7% 45.3% 29.8% 6.8% 18.0% 3.4% 17.2% 35.5% 20.4% 21.8% 118
Scott Rolen 223 3.75 32.7% 33.7% 31.4% 11.8% 23.1% 7.3% 19.9% 37.9% 14.5% 20.2% 96
Jimmy Rollins 732 3.42 23.8% 44.9% 26.2% 8.0% 20.8% 5.1% 14.2% 36.5% 18.5% 24.6% 130
Aaron Rowand 640 3.59 34.5% 52.2% 25.3% 6.2% 16.3% 10.8% 21.0% 32.9% 13.4% 20.6% 71
Olmedo Saenz 352 3.84 25.0% 39.8% 27.8% 10.4% 21.6% 9.9% 16.8% 35.4% 18.3% 19.2% 91
Freddy Sanchez 492 3.56 17.1% 45.8% 29.0% 5.4% 19.8% 4.3% 15.7% 34.9% 20.2% 24.3% 120
Reggie Sanders 329 3.70 38.6% 34.2% 42.3% 6.8% 16.7% 14.8% 17.1% 38.0% 11.0% 18.3% 82
Brian Schneider 408 3.52 25.0% 47.1% 25.8% 7.1% 20.0% 9.1% 17.7% 34.9% 14.4% 23.0% 90
Marco Scutaro 423 3.77 19.6% 43.4% 30.4% 8.8% 17.4% 2.9% 15.3% 36.5% 23.1% 21.3% 138
Richie Sexson 656 3.97 30.5% 40.1% 34.3% 7.1% 18.5% 13.9% 15.5% 41.5% 13.4% 15.2% 97
Gary Sheffield 675 3.92 15.7% 41.7% 31.2% 10.1% 17.0% 6.0% 14.0% 40.0% 20.0% 19.6% 137
Chris Shelton 431 4.26 9.0% 37.0% 37.0% 4.6% 21.3% 7.8% 18.1% 35.6% 21.2% 16.6% 94
Grady Sizemore 706 3.80 22.4% 46.0% 28.0% 6.8% 19.2% 6.9% 17.3% 36.8% 18.8% 19.2% 104
JT Snow 410 3.68 32.9% 38.7% 29.4% 11.0% 21.0% 6.5% 19.0% 37.8% 15.1% 20.6% 102
Chris Snyder 373 4.01 17.4% 52.5% 24.0% 6.6% 16.9% 8.1% 12.9% 38.9% 22.9% 16.4% 127
Alfonso Soriano 682 3.65 32.1% 33.8% 40.4% 7.9% 17.8% 12.6% 19.9% 33.2% 12.8% 20.9% 70
Sammy Sosa 424 3.64 37.5% 44.5% 28.4% 10.4% 16.7% 14.5% 16.7% 37.2% 11.8% 19.5% 82
Junior Spivey 293 4.05 29.7% 39.9% 30.9% 10.1% 19.1% 11.4% 18.9% 37.2% 15.9% 15.1% 84
Matt Stairs 466 3.93 29.0% 39.2% 32.8% 9.6% 18.4% 8.2% 15.2% 40.9% 16.9% 18.2% 120
Shannon Stewart 599 3.58 15.4% 46.3% 28.9% 8.3% 16.5% 6.0% 18.9% 32.9% 18.9% 22.7% 91
Cory Sullivan 424 3.88 31.6% 47.7% 24.2% 2.6% 25.5% 8.9% 20.8% 34.6% 14.7% 18.9% 80
BJ Surhoff 321 3.51 26.8% 47.3% 27.1% 7.2% 18.4% 5.9% 17.4% 33.5% 17.8% 24.7% 99
Ichiro Suzuki 739 3.57 19.8% 55.1% 18.8% 5.3% 20.8% 4.7% 19.0% 34.5% 16.5% 24.3% 101
Mark Sweeney 267 4.09 28.5% 37.9% 34.9% 6.5% 20.7% 9.0% 16.0% 42.6% 16.0% 15.6% 117
Mike Sweeney 514 3.54 36.0% 36.1% 36.8% 10.6% 16.6% 10.0% 21.3% 34.3% 10.6% 23.2% 75
Nick Swisher 522 4.14 17.0% 38.2% 36.8% 9.1% 15.9% 8.2% 15.0% 39.5% 20.4% 16.4% 117
So Taguchi 424 3.55 31.4% 46.2% 28.5% 4.4% 20.9% 6.9% 22.6% 31.7% 14.9% 22.7% 74
Willy Taveras 635 3.52 29.1% 61.5% 20.8% 5.8% 11.8% 7.3% 17.8% 31.8% 16.9% 22.4% 87
Mark Teahen 491 3.91 28.5% 52.8% 20.7% 2.6% 23.9% 9.7% 19.0% 34.1% 18.7% 18.0% 82
Mark Teixeira 730 3.72 30.5% 39.6% 31.9% 7.8% 20.7% 9.0% 16.8% 39.8% 14.4% 19.4% 106
Miguel Tejada 704 3.54 23.7% 47.0% 25.8% 7.5% 19.7% 7.6% 18.6% 34.3% 15.7% 23.4% 90
Jim Thome 242 3.96 29.3% 43.4% 24.3% 11.0% 21.3% 11.8% 15.0% 42.4% 16.0% 14.4% 108
Yorvit Torrealba 224 3.79 22.3% 62.2% 20.5% 3.8% 13.5% 10.3% 13.8% 36.3% 19.9% 18.5% 103
Chad Tracy 553 3.83 9.8% 33.3% 38.2% 7.4% 21.1% 5.5% 16.3% 35.3% 21.8% 20.6% 111
Michael Tucker 307 3.56 42.3% 50.5% 27.5% 7.2% 15.3% 8.9% 16.4% 39.1% 14.2% 20.6% 106
Juan Uribe 540 3.54 33.7% 40.2% 29.6% 15.3% 14.8% 10.0% 18.7% 33.7% 14.2% 22.2% 81
Chase Utley 628 4.02 17.8% 35.4% 34.2% 9.5% 20.9% 7.1% 16.0% 37.9% 20.7% 17.6% 113
Javier Valentin 254 3.76 31.9% 31.6% 33.2% 10.7% 24.6% 8.0% 20.0% 39.6% 12.5% 19.8% 97
Jason Varitek 539 4.14 20.6% 47.9% 28.6% 7.0% 16.5% 8.1% 17.0% 38.3% 20.0% 16.1% 105
Jose Vidro 347 3.51 33.7% 45.3% 23.9% 8.4% 22.5% 5.7% 19.1% 36.8% 14.3% 23.6% 102
Jose Vizcaino 205 3.59 40.0% 46.7% 29.3% 6.7% 17.3% 7.8% 21.7% 33.7% 15.9% 20.9% 78
Omar Vizquel 651 3.87 18.7% 46.8% 23.7% 7.9% 21.8% 3.5% 15.7% 36.9% 21.3% 21.1% 133
Larry Walker 367 3.56 37.1% 53.0% 22.5% 7.5% 17.0% 10.3% 13.8% 40.5% 14.8% 19.5% 115
Todd Walker 433 3.64 29.3% 40.4% 33.2% 7.8% 18.6% 4.5% 18.4% 36.6% 16.7% 23.0% 110
Daryle Ward 453 3.51 35.5% 39.8% 32.2% 9.9% 18.4% 9.2% 18.0% 34.9% 14.5% 22.8% 88
Rickie Weeks 414 4.15 22.5% 49.4% 25.1% 8.6% 16.9% 11.0% 15.0% 38.2% 18.9% 15.6% 101
Vernon Wells 678 3.38 35.3% 40.8% 27.5% 12.7% 19.0% 8.4% 15.2% 36.8% 15.5% 23.8% 107
Jayson Werth 395 4.62 16.2% 41.4% 33.0% 6.2% 19.4% 9.3% 18.6% 37.5% 21.2% 12.5% 93
Rondell White 400 3.53 30.5% 54.5% 21.8% 8.5% 15.2% 8.8% 17.3% 33.9% 16.1% 23.4% 89
Brad Wilkerson 661 4.21 14.4% 32.4% 37.1% 9.0% 21.5% 8.7% 14.6% 40.8% 19.6% 15.4% 120
Bernie Williams 546 3.57 28.4% 44.4% 29.7% 9.1% 16.8% 6.8% 12.5% 40.6% 18.4% 21.4% 145
Craig Wilson 238 4.05 40.8% 43.4% 30.2% 6.2% 20.2% 15.2% 17.1% 37.2% 15.8% 13.5% 79
Jack Wilson 639 3.53 21.6% 48.5% 27.4% 8.3% 15.8% 4.7% 18.3% 33.0% 18.2% 24.4% 99
Preston Wilson 576 3.91 34.5% 52.1% 22.6% 6.4% 18.9% 12.3% 18.0% 35.4% 17.0% 16.7% 80
Randy Winn 683 3.55 27.5% 49.2% 26.7% 5.0% 19.1% 7.0% 18.2% 35.5% 16.1% 22.3% 97
Tony Womack 351 3.89 24.8% 58.5% 22.8% 6.6% 12.1% 5.1% 19.1% 31.2% 20.2% 21.2% 88
David Wright 657 3.98 18.9% 41.1% 32.5% 5.4% 21.1% 6.0% 17.9% 39.3% 18.4% 17.8% 113
Dmitri Young 509 3.62 40.7% 47.2% 27.8% 5.9% 18.9% 12.4% 22.0% 33.4% 11.1% 20.5% 67
Michael Young 732 3.72 28.0% 45.0% 26.0% 4.5% 24.5% 6.8% 18.0% 37.7% 15.9% 21.3% 105
Gregg Zaun 512 4.25 20.9% 45.8% 25.2% 10.6% 18.4% 4.5% 18.4% 39.5% 19.9% 17.0% 118

Monday, February 20, 2006

Thought Police

If you think America's freedoms are imperiled by the Patriot Act, you should probably read this.

Barrett Being Defensive?

Alerted to this interesting article in the Chicago Sun-Times on Michael Barrett and his defense.

I especially enjoyed this comment:

Barrett points to the numbers (other than throwing out runners) to support his claim.

"I study the stats,'' he said. "Over the last two years, I feel like my defense has been right there in terms of fielding percentage. Calling games, I feel good about.

"For whatever reason, there is no reflection in winning the Gold Glove in your catcher earned-run average. You would think that would have an impact.''

Barrett suspects that his catcher ERA might be worse this season than last year because of the uncertain health of some pitchers.

Well. Perhaps people don't look at Catcher's ERA because it hasn't been shown that catcher's have much if any impact on ERA.

Barrett made this comment because his CERA in 2005 was 4.45, which was similar to Mike Matheny. Barrett threw out 21 of 91 runners while Matheny threw out 39 of 102.

Barrett did have an interesting idea however.

"I wish baseball would design a catcher rating system like a quarterback rating system,'' he said.

How would it work?

"Your catcher ratio would be based off your percentage of throwing guys out, your percentage of wild pitches vs. passed balls and all the things added together,'' he said. "Then you would get a real glimpse of where I'm at.''

Of course, included in such a system would be a recognition that some catchers prevent runners from attempting stolen bases in the first place, a fact that the caught stealing percentage alone doesn't capture.

Certainly good catchers prevent both wild pitches and passed balls but the percentage of wild pitches to passed balls I don't think would tell you much. A WP/PB ratio of 2.0 would be better than 1.0 in some sense but good catchers would have fewer opportunities overall. A better approach might be WP+PB/Total Pitches if you could control for the pitcher (think Tim Wakefield).

I like the way he's thinking though.

Sunday, February 19, 2006

First Salvo

Came across this post on Baseball Musings related to my article "Competitive Balance and the CBA" and some clarifications I made in "Odds and Ends".

This is a first indication that large market owners may push for bigger changes to the revenue sharing system. As I mentioned in my articles, perhaps the first place to start is to ensure that the revenues collected by teams from revenue sharing are actually invested in the club per the clause of the current CBA that reads as follows.

(5) Other Undertakings
(a) A principal objective of the revenue sharing plan is to promote the growth of the Game and the industry on an individual Club and on an aggregate basis. Accordingly, each Club shall use its revenue sharing receipts (from the Base Plan, the Central Fund Component and the Commissioner’s Discretionary Fund) in an effort to
improve its performance on the field. The Commissioner shall enforce this obligation by requiring, among other things, each Payee Club, no later than April 1, to report on the performance-related uses to which it put its revenue sharing receipts in the preceding revenue sharing year. Consistent with his authority under the Major League
Constitution, the Commissioner may impose penalties on any Club that violates this obligation.

To date there have been penalties assessed.

Of course, what John Henry is talking about the in article I referenced is not specifically this issue but rather the issue of an increasing luxury tax. However, as Maury Brown pointed out in his excellent article in the THT Baseball Annual 2006, there is no penalty for first time offenders this year. So if you're not the Angels, Red Sox, or Yankees (the only teams to pay in thus far) you have nothing to worry about in 2006. And if there is no agreement by the start of the 2007 season, there will be no luxury tax next year. Also, the penalties did not increase this season for second and third time offenders and so Henry's concern cannot be that luxury taxes are increasing this season.

IMO, perhaps the luxury tax threshold should be lowered (it is $136.5M this season) along with the penalty or institute a tiered system so the Yankees are still penalized heavily for keeping $200M payrolls but other teams who are in excess of say, 150% of average, are as well.

Tuesday, February 14, 2006

Paleontology for the Masses

Before Christmas I had the wonderful opportunity to attend the Prehistoric Journey Symposium at the Denver Museum of Nature and Science. The symposium was open to the public, although museum members were given reduced admission, and featured seven paleontologists who each lectured for 40 minutes on a subject of current research in which they're active. In this post I'll review two of the seven lectures.

The lectures were attended by approximately 150 people, many of whom are associated with the museum either as employees or through its paleontology certification program (which now has over 100 graduates). The museum first held this symposium in 1995 when it features such heavyweights as Stephen Jay Gould and Lynn Margulis.

It was interesting to note in the opening remarks by one of the curators that since the Prehistoric Journey opened in 1995 the number of named dinosaur genera has doubled and that the museum now has over 110,000 specimens. Paleontology is alive and well it would seem.

I had never attended lectures by professional paleontologists before and was surprised both by their humor and the clarity with which they presented their material. Although I shouldn't have been, I was a bit surprised by the extensive use, of statistics within each specialty and, as you might be able to tell from this blog, a few good numbers always warm my heart.

More seriously, however, I think what I was most struck with was the ingenuity with which these scientists apply the scientific method in order to carry out their research and how extensive is their reliance on life today ("the present is the key to the past" in the words of Charles Lyell) in reconstructing the ecology, environment, and biology of these long dead creatures.

Ichthyosaurs
As an illustration of that point Dr. Ryosuke Motani of the University of California-Davis in his talk "When Reptiles become Fish-Shaped: A Story of Ichthyosaurs" did a great job of inferring some of the characteristics of these marine reptiles, mostly known from just two locations in England and Germany, from the characteristics of modern marine mammals and fish as well as from simple physics and the science of optics.

Ichthyosaurs (205-150 million years ago), evolved in early Triassic and when they first appear their body shape indicate that they used an eel-like swimming motion (anguilliform) which is characteristic of vertebrates living in shallow waters on the continental shelf, for example Catsharks. However, as they evolved into the Jurassic they took on their more characteristic dolphin-like shape. That shape is more characteristic of animals who cruise the open ocean. Motani then used the ratio of caudal fin height to length which puts a limit on the amount of force that an animal is able to create, and was able to estimate that the Ichthyosaurs of the Jurassic were probably not able to swim as fast as dolphins but rather more like that the speed of tuna.

He was also able to estimate an f-number (focal ratio which measures the amount of light, and therefore the relative brightness of an optical system) of the Ichthyosaur eye by estimating the focal length and the aperture diameter using the fossil skulls of Ichthyosaurs as well as analogs in modern animals. The result is Montain’s conclusion that over time Ichthyosaurs evolved better eyesight akin to owls and rats (with an f/0.8 to f/1.1) who are nocturnal feeders. It was interesting that he plotted the ratio of eye size to body size in a variety of animals and showed that Ichthyosaurs across the board were higher than most other animals.



Finally, Montani pieced together clues that include the fact that Ichthyosaurs possessed a type of bone that allows for compressibility, vision, squid eating diet preserved in fossils, even their body size to estimate that Ophthalmosaurus (Jurassic Ichthyosaurs) were fairly deep divers (although not as deep as dolphins) that could descend more than 600 meters and stay submerged for 20 minutes, a task that would take advantage of their keen eyesight to allow them to see to depths of at least 1,000 meters.

Is it Getting Hotter?
There were several other themes to the day which although not explicitly called for in the program, made an impression as they were touched on again and again. The most prevalent of these was climate change. Dr. Linda Ivany of Syracuse University in her talk titled “Marine Ecosystems and Climate Change: The Beginning of the Icehouse World in Antarctica”.

Ivany described her and her colleagues’ research in exploring the relationship over time between predators and prey (for example clams) in Antarctica and how those changes were affected by climate change. To do so she traveled to Antarctica to collect fossils on Seymour Island in what is known at the La Meseta formation, one of the few places that has no permanent ice cover and is littered with Eocene fossils.



She concluded that the same type of low predation environment in the seas around Antarctica which includes mainly starfish, slow moving anti-freeze fish, anemones, and worms, has persisted for the last 36 million years. To come to this conclusion, however, she couldn’t look directly at prey fossils since they are more rare and often don’t fossilize at all. Instead she collected around 1,000 fossil clams from a variety of stratigraphic horizons and locations on the island and measured shell characteristics such as thickness in order to infer the types of predators that were present.

Interestingly, she found that between two levels 15 of the fossil clam species that disappear are more heavily defended than the 16 remaining species. When plotted against data from the deep sea drilling project this matches nicely with the global cooling trend that intensified during that time. So it would appear that shell crushing predators did die out as a result of a cooler ocean.

She also looked shell chemistry in order to measure water temperature using the ratio of oxygen isotopes. This too showed temperature differences over time that supported the data from the predator/prey analysis. Her data even illustrate the warming trend of 41-42 million years ago.

One of the things I found fascinating was that she reported that two species of clams showed vastly different temperatures for the ocean during the same period. A couple of her graduate students dug into it and realized that, like tree rings, the growth bands from which they were taking measurements may not be laid down uniformly during each year. In other words, one species grew only in the winter when the water was cooler while the other grew the entire year.

Finally, she also found evidence on Seymour Island of glacial expansion in the early Oligocene (34 million years ago) at a time that corresponds with global cooling.

Although the oceans have warmed considerably since then there are still no shell crushers in Antarctica. The reason she hypothesizes is that the circular currents around Antarctica serve to isolate it and therefore don’t allow crabs, starfish and other animals to colonize the continent.

Team Sacrifice Fly Data

As a companion to my article on THT this week I offer the complete list of team seasons for 2000 through 2005 regarding sacrifice flies sorted by team and year.

And you thought this was going to be a boring day.



Year Team Opp Scores OA Hold% Succ%
2000 ANA 65 43 5 0.262 0.896
2001 ANA 67 52 2 0.194 0.963
2002 ANA 89 64 0 0.281 1.000
2003 ANA 63 50 1 0.190 0.980
2004 ANA 53 40 4 0.170 0.909
2005 ANA 55 39 1 0.273 0.975
2000 ARI 70 57 1 0.171 0.983
2001 ARI 47 36 5 0.128 0.878
2002 ARI 59 50 2 0.119 0.962
2003 ARI 58 52 0 0.103 1.000
2004 ARI 51 37 3 0.216 0.925
2005 ARI 49 44 1 0.082 0.978
2000 ATL 53 44 2 0.132 0.957
2001 ATL 61 51 4 0.098 0.927
2002 ATL 60 47 2 0.183 0.959
2003 ATL 56 47 1 0.143 0.979
2004 ATL 52 48 0 0.077 1.000
2005 ATL 57 45 4 0.140 0.918
2000 BAL 63 50 2 0.175 0.962
2001 BAL 60 48 3 0.150 0.941
2002 BAL 62 47 3 0.194 0.940
2003 BAL 51 38 1 0.235 0.974
2004 BAL 73 62 6 0.068 0.912
2005 BAL 50 42 0 0.160 1.000
2000 BOS 63 47 3 0.206 0.940
2001 BOS 57 41 5 0.193 0.891
2002 BOS 62 50 4 0.129 0.926
2003 BOS 71 61 1 0.127 0.984
2004 BOS 69 55 2 0.174 0.965
2005 BOS 74 62 2 0.135 0.969
2000 CHA 71 58 1 0.169 0.983
2001 CHA 59 51 3 0.085 0.944
2002 CHA 70 52 2 0.229 0.963
2003 CHA 53 39 5 0.170 0.886
2004 CHA 47 42 1 0.085 0.977
2005 CHA 57 48 3 0.105 0.941
2000 CHN 54 42 2 0.185 0.955
2001 CHN 64 51 1 0.188 0.981
2002 CHN 51 38 2 0.216 0.950
2003 CHN 52 41 3 0.154 0.932
2004 CHN 55 48 2 0.091 0.960
2005 CHN 53 37 0 0.302 1.000
2000 CIN 64 57 2 0.078 0.966
2001 CIN 46 40 3 0.065 0.930
2002 CIN 47 37 4 0.128 0.902
2003 CIN 35 31 1 0.086 0.969
2004 CIN 36 24 1 0.306 0.960
2005 CIN 46 38 2 0.130 0.950
2000 CLE 68 49 6 0.191 0.891
2001 CLE 78 60 3 0.192 0.952
2002 CLE 46 39 1 0.130 0.975
2003 CLE 49 41 1 0.143 0.976
2004 CLE 56 42 3 0.196 0.933
2005 CLE 53 50 1 0.038 0.980
2000 COL 87 72 4 0.126 0.947
2001 COL 58 49 2 0.121 0.961
2002 COL 63 50 3 0.159 0.943
2003 COL 51 37 3 0.216 0.925
2004 COL 46 37 3 0.130 0.925
2005 COL 40 33 1 0.150 0.971
2000 DET 71 49 1 0.296 0.980
2001 DET 60 49 4 0.117 0.925
2002 DET 65 56 2 0.108 0.966
2003 DET 49 44 1 0.082 0.978
2004 DET 55 43 2 0.182 0.956
2005 DET 61 52 0 0.148 1.000
2000 FLO 68 50 4 0.206 0.926
2001 FLO 65 45 3 0.262 0.938
2002 FLO 59 48 2 0.153 0.960
2003 FLO 47 39 0 0.170 1.000
2004 FLO 54 39 4 0.204 0.907
2005 FLO 61 50 1 0.164 0.980
2000 HOU 77 60 5 0.156 0.923
2001 HOU 73 55 5 0.178 0.917
2002 HOU 45 37 3 0.111 0.925
2003 HOU 50 36 3 0.220 0.923
2004 HOU 66 52 1 0.197 0.981
2005 HOU 49 42 0 0.143 1.000
2000 KCA 84 68 1 0.179 0.986
2001 KCA 59 44 0 0.254 1.000
2002 KCA 65 50 6 0.138 0.893
2003 KCA 67 57 2 0.119 0.966
2004 KCA 54 38 2 0.259 0.950
2005 KCA 56 50 1 0.089 0.980
2000 LAN 52 44 1 0.135 0.978
2001 LAN 47 43 1 0.064 0.977
2002 LAN 47 41 1 0.106 0.976
2003 LAN 42 28 1 0.310 0.966
2004 LAN 46 35 2 0.196 0.946
2005 LAN 41 32 1 0.195 0.970
2000 MIL 53 47 2 0.075 0.959
2001 MIL 47 34 2 0.234 0.944
2002 MIL 51 34 2 0.294 0.944
2003 MIL 48 40 1 0.146 0.976
2004 MIL 44 40 1 0.068 0.976
2005 MIL 50 38 3 0.180 0.927
2000 MIN 56 49 2 0.089 0.961
2001 MIN 48 38 2 0.167 0.950
2002 MIN 61 52 0 0.148 1.000
2003 MIN 58 50 2 0.103 0.962
2004 MIN 55 40 4 0.200 0.909
2005 MIN 57 40 5 0.211 0.889
2000 MON 46 33 2 0.239 0.943
2001 MON 60 45 2 0.217 0.957
2002 MON 50 41 0 0.180 1.000
2003 MON 49 40 2 0.143 0.952
2004 MON 49 33 3 0.265 0.917
2000 NYA 55 47 0 0.145 1.000
2001 NYA 51 41 0 0.196 1.000
2002 NYA 52 41 2 0.173 0.953
2003 NYA 46 34 1 0.239 0.971
2004 NYA 60 50 3 0.117 0.943
2005 NYA 56 43 1 0.214 0.977
2000 NYN 59 50 1 0.136 0.980
2001 NYN 54 35 4 0.278 0.897
2002 NYN 37 30 2 0.135 0.938
2003 NYN 53 44 0 0.170 1.000
2004 NYN 47 34 0 0.277 1.000
2005 NYN 50 37 1 0.240 0.974
2000 OAK 55 43 2 0.182 0.956
2001 OAK 69 57 4 0.116 0.934
2002 OAK 40 36 2 0.050 0.947
2003 OAK 62 53 3 0.097 0.946
2004 OAK 55 42 1 0.218 0.977
2005 OAK 59 40 2 0.288 0.952
2000 PHI 46 35 1 0.217 0.972
2001 PHI 71 60 3 0.113 0.952
2002 PHI 51 39 4 0.157 0.907
2003 PHI 49 37 1 0.224 0.974
2004 PHI 61 46 1 0.230 0.979
2005 PHI 65 46 1 0.277 0.979
2000 PIT 52 36 2 0.269 0.947
2001 PIT 51 34 4 0.255 0.895
2002 PIT 50 40 0 0.200 1.000
2003 PIT 50 37 4 0.180 0.902
2004 PIT 47 41 1 0.106 0.976
2005 PIT 64 48 3 0.203 0.941
2000 SDN 50 42 0 0.160 1.000
2001 SDN 55 48 1 0.109 0.980
2002 SDN 51 40 3 0.157 0.930
2003 SDN 55 40 1 0.255 0.976
2004 SDN 83 65 5 0.157 0.929
2005 SDN 59 48 1 0.169 0.980
2000 SEA 66 60 2 0.061 0.968
2001 SEA 88 69 3 0.182 0.958
2002 SEA 88 72 3 0.148 0.960
2003 SEA 59 46 2 0.186 0.958
2004 SEA 59 48 4 0.119 0.923
2005 SEA 49 37 2 0.204 0.949
2000 SFN 79 66 0 0.165 1.000
2001 SFN 72 53 3 0.222 0.946
2002 SFN 63 52 1 0.159 0.981
2003 SFN 51 39 1 0.216 0.975
2004 SFN 61 51 1 0.148 0.981
2005 SFN 60 43 2 0.250 0.956
2000 SLN 66 53 3 0.152 0.946
2001 SLN 63 50 3 0.159 0.943
2002 SLN 64 49 5 0.156 0.907
2003 SLN 69 54 3 0.174 0.947
2004 SLN 72 66 3 0.042 0.957
2005 SLN 42 35 1 0.143 0.972
2000 TBA 53 40 4 0.170 0.909
2001 TBA 33 25 3 0.152 0.893
2002 TBA 46 36 1 0.196 0.973
2003 TBA 62 49 2 0.177 0.961
2004 TBA 69 54 2 0.188 0.964
2005 TBA 69 49 1 0.275 0.980
2000 TEX 64 47 0 0.266 1.000
2001 TEX 67 55 2 0.149 0.965
2002 TEX 66 49 1 0.242 0.980
2003 TEX 50 40 0 0.200 1.000
2004 TEX 72 57 3 0.167 0.950
2005 TEX 37 32 1 0.108 0.970
2000 TOR 46 34 2 0.217 0.944
2001 TOR 55 43 1 0.200 0.977
2002 TOR 64 54 2 0.125 0.964
2003 TOR 64 55 1 0.125 0.982
2004 TOR 49 42 2 0.102 0.955
2005 TOR 62 54 2 0.097 0.964
2005 WAS 54 44 4 0.111 0.917

Saturday, February 11, 2006

Infield Battle

You heard it here first (ok, second). Carrie Muskat of MLB.com was interviewed on MLB Radio the other day and said without equivocation that Ronny Cedeno and Matt Murton will be starting at shortstop and left field in 2005. Further, she said you won't see Marquis Grissom and Neifi Perez in those slots and that Dusty Baker has nothing against young players but that he's never before had young players who were ready to play.

I have to admit I, like many other Cubs fans, remain skeptical. Dusty's reputation is not undeserved

It should be an interesting situation, especially when throw Jerry Hairston in the mix at second base along with Todd Walker. Muskat sees that shaping up as the biggest competition of the spring. Hairston wasn't terrible last year but I think people tend to underrate both Walker's defensive and offensive contributions. They may also need Walker to play more third depending on the health of Aramis Ramirez and Muskat perhaps sees a platoon developing.

Last season according to Baseball Prospectus Walker was -6 fielding runs above average (FRAA) and +10 batting runs above average (BRAA) while Hairston was -1 FRAA and -7 BRAA. Of course Hairston is coming off of an ankle injury that certainly limited him last season.

Wednesday, February 08, 2006

Ryan Shealy

Marc Normandin has a nice profile of Ryan Shealy on his site today that shows the various projections for him.

To that list I'll add Ron Shandler's from his 2006 Baseball Forecaster.


AB R H HR RBI SB AVG OBP SLUG
147 20 42 7 22 1 .285 .342 .494

I saw him play both in Colorado Springs last season as well as in Denver. What was a bit disappointing was that his walk rate continue to take a step backwards as it has since A ball (although in Colorado he walked 13 times in 104 plate appearances). I'm hopeful that he can learn to play a bit of outfield since he won't get much playing time with Todd Helton at first other than in interleague games as a DH. As a result Shandler's forecasted at bats may be closer to the mark.

Matt Holiday is a pretty brutal left fielder and so the competition isn't that stiff. Brad Hawpe has a good arm in right field although he hasn't really shown the kind of power expected of him yet at the major league level.

Monday, February 06, 2006

Are you Insured?

Perhaps I'm missing something related to Jeff Bagwell's situation.

I like to listen to MLB Radio on my MP3 player and have begun listening more and more to Stay'in Hot which features former Giants outfielder Daryl Hamilton and Seth Everett. I've listened to them interview Bagwell regarding his situation and recently they seemed perplex as to why the Astros would have set that January 31st deadline before filing the insurance claim. (Of course Hamilton also mentioned the other day that "evangelical Christians" control the media...hmmmm)

I would think it would be obvious that the Astros wanted to know whether they are going to have that $15.5 million they can use to improve their club as early as possible. If they wait to see if Bagwell can actually play then it will be too late to really utilize that money for Roger Clemens or to pick up the salary of another pitcher or hitter in a trade situation. This is especially the case since nothing has really changed in Bagwell's medical situation. His shoulder is still damaged and it's not getting any better and so the probability of his being able to come back are not good.

But despite all of that Bagwell's projected performance is nowhere close to providing $15 million worth of value to the Astros. The last two seasons his OPS+ has been a bit above league average but was higher in 2004 (117) than in 2005 (96). From a business perspective, although perhaps not from a PR perspective, the Astros would be silly not to try and recoup that money.

I also find it a little ironic that those like Hamilton talk about how Bagwell "deserves" the chance because of all that he's done for the Astros. What about the fans of the Astros and Bagwell's teammates? Don't they deserve the chance to improve and win more games rather than have a $15 million pinch hitter on their bench? That team desparately needs offense and if they could use that money to bring in another hitter they would be well served.

Some might argue that Bagwell signed the back-loaded contract as a favor to the Astros. That may be the case but he's been handsomely paid since 2002 to the tune of $58 million over that time so it's not as if he pulled an Andre Dawson who signed for $700,000 with the Cubs in 1987 in order to get off the artificial turf and out of Canada.

Competitive Balance

I have new article this morning on THT related to Competitve Balance and the CBA.

Although I neglected to mention it in the article, Brian Borawski brought to my attention the fact that the luxury tax has had some positive effects. Last off-season when everyone thought the Yankees were going to sign Carlos Beltran, we were all surprised when they withdrew from the bidding. The Washington Post had this to say about it at the time:

Yankees officials acknowledge that they were constrained by two of the changes adopted three years ago -- revenue-sharing and a penalty against high-spending clubs known as the luxury tax. "We had priorities this winter -- primarily, improving our starting pitching -- and we feel we met those priorities," Yankees President Randy Levine said. "We're like every other team, even though our revenues are larger than other teams'. We're conscious of revenue sharing and the luxury tax."

Of course, Beltran ended up going to another large market team but the tax (targeted primarily at the Yankees) had its intended effect.

Sunday, February 05, 2006

Pitch Outcomes

In fielding a question on pitch outcomes I wrote a little function in SQL Server that extracts this info for retrosheet formatted data.

Here is what I got for 2005:


B 250600 36.6%
C 119237 17.4%
S 55486 8.1%
W 1913 0.3%
F 116247 17.0%
T 3413 0.5%
X 138622 20.2%

T is a foul tip and W is a swinging strike blocked. B is a ball, C a called strike, S a swinging strike, F is a foul, and X is a ball put into play. Fouls are not
differentiated between those that count as strikes and those with 2 strikes.

And for 2004...

B 259084 37.1%
C 119998 17.2%
S 60140 8.6%
W 0 0.0%
F 119532 17.1%
T 1233 0.2%
X 138593 19.8%

Obviously the 2004 data I have didn't differentiate between swinging strikes that were blocked and others.

Saturday, February 04, 2006

Gammons on Bloggers

I'm told that Peter Gammons had this to say in his latest column (an insider subscription is required)...

"The forum that the Internet has provided for statistics andstatistical analysis is one of the biggest changes in the way we follow baseball in this century. Granted, there are what one friend calls "stat Nazis who believe there is no human element." Granted, statistics are ways to lead us all to predictable truths. But what "Bill James Baseball Abstract" and the Hirdt Brothers' "Elias Baseball Analyst" opened our eyes to nearly 20 years ago have become daily necessities.

There cannot be a better, more thoughtful Internet journal than "Baseball Prospectus," which has the invaluable and unique resource of Will Carroll's "Under the Knife," bookmarked by every front office and media member. "Hardball Times" is daily must-reading, as well as "Baseball Analysts" and the "Baseball Think Factory." Now there are countless blogs, none better than David Pinto's "Baseball Musings," which also provide several significant tools.

Look, it may kill the scout in the field to hear that one can learn alot from statistical analysis annuals geared to Rotisserie heads, but it's certainly true, starting with the annuals published by "Baseball Prospectus" and "Hardball Times."

But take, for instance, Ron Shandler's "Baseball Forecaster." Shandler makes no bones about the fact that he gears his book to Rotisserie players. But as one pours through all the statistical data, there are fascinating statistical prognostication tools, from measures of a pitcher's dominance and command, to percentages of balls hit on the ground, in the air and on a line. Or measures of a hitter's ability to make contact...

.....To Joe Sheehan, Lee Sinins, David Pinto, Ron Shandler, Rob Neyer, all those tireless bloggers, thanks. You make my job far easier, and farmore interesting. And changed the way we look at the game.


Here here! Love to see the plug for the THT Annual.

Buck to the Hall?

Here's a good article on the upcoming selection of Negro League players to the Hall of Fame. There are 39 players on the list who can be voted on by 12 historians. If the player receives 9 votes, he'll get elected to the Hall and be inducted along with Bruce Sutter. You can view the complete list of eligible players here.

Larry Hogan, who is one of the historians and has worked on this project for some time was interviewed on MLB radio several days ago. BTW, you can get the MLB radio podcasts through iTunes.

Having lived in Kansas City, I've met Buck O'Neil on several occasions and am hoping that he gets in - not only because of his playing and managing career but also because of what he's meant to baseball overall. Steve Treder has written a nice piece on the Negro League Hall of Fame that O'Neil is active in in Kansas City.

What I've found most interesting about this effort is that the project collected most of the box scores from the 1920-1940s. I'm hoping that data will be made available in digital form so that we can really begin to analyze the accomplishments of these players, and hopefull, put them into some kind of historical perspective.

There is a book connected with the project called Shades of Glory co-authored by Hogan but it's not clear to me whether the statistics are a part of the book. There will also be a book tour that starts February 21st with stops in Atlanta; Birmingham, Ala; Chicago; Cleveland; Detroit; Kansas City, Kan.; and Washington, D.C.

Friday, February 03, 2006

Rating Defense

David Gassko has a nice article on THT today talking about the various defensive rating systems out there including UZR, PMR, ZR, DFTs, and Range. His conclusion is that UZR is the most complete system although no longer published because its creator Michael Lichtman now consults for the Cardinals. He notes that ZR and his own Range statistic compliment each other and PMR also tracks about as well as each of the others indepedantly.

I'll be interested to see how John Dewan's fielding bible will stack up against these other systems.

In many ways these statistics are the cutting edge of baseball performance analysis since they are measuring defense (if you count outfielder throwing) which is the third most impactful part of the game on the field behind offense and pitching. As the state of the art moves forward we're therefore likely to see diminishing returns in the amount of actionable information that is learned through analysis. Behind the big three we then have base running and speed, game strategy, and lineup construction among others.

Thursday, February 02, 2006

Free Agency and Aging

Another great article by Dave Studeman on THT this morning related to aging patterns of players over time. He uses a technique where he multiplies each player's Win Shares by age and then sums them up before dividing by the sum of the Win Shares.

The interesting question he then tackles is why the avg Win Shares by age increased drastically between 1977 and 1982. Not surprisingly (once you read it anyway) is that free agency had the effect of giving older players more playing time as GM's and owners foolishly overspent on players who were past their prime.

He has a nice list of free agent contracts signed by that first class of free agents.


Players Age Years Total Value
Reggie Jackson 31 5 $3,000,000
Joe Rudi 30 5 2,090,000
Don Gullett 26 6 2,000,000
Gene Tenace 30 5 1,815,000
Bobby Grich 28 5 1,750,000
Rollie Fingers 30 6 1,600,000
Dave Cash 29 5 1,500,000
Sal Bando 33 5 1,400,000
Gary Matthews 26 5 1,200,000
Don Baylor 28 6 1,020,000
Bill Campbell 28 5 1,000,000
Wayne Garland 26 10 1,000,000
Campy Campaneris 35 5 950,000

What this illustrates is how unaware the industry was at the time about aging patterns and normal career trajectories. Many of these players were past their prime and all of them were signed to contracts that guaranteed that their performance would be worse at the end than at the beginning.

Since that time sabermetrics has contributed by analyzing the subject by bringing to light the now common wisdom that peak ages are around 27 or 28 and that decline can be rapid. I looked at this question briefly awhile back and produced the following graph of normalized OPS for good players (those who racked up more than 6,000 plate appearances in their careers).



As you can see the decline is noticeable even from ages 29-31. This graph also understates the decline since the points on the right-hand part of the graph represent fewer players. In other words there is a selection bias taking place as players with lesser skills retire earlier.

Wednesday, February 01, 2006

The Evolving Closer

With the election of Bruce Sutter to the Hall of Fame there has been plenty written about the nature of the work that closers do and how that has evolved over time. There is no question that a closer's job is easier today than when the position was evolving throughout the 1980s.

Today I learned of an article by Gabriel Schechter at the Baseball Hall of Fame that takes a look at how hard closers had to work to get their saves. What he finds is that 43.3% of Sutter's saves came when he pitched 2 or more innings, Rollie Fingers at 40.7%, and Rich Gossage at 40.3%. Meanwhile Dennise Eckersley was at 7.2%, Trevor Hoffman at 1.5%, and Mariano Rivera at 2.4%.

Although I knew there were these diferences, the magnitude of the difference surprised me. The other interesting thing is that Lee Smith spanned the time in which the role became more specialized (circa 1989) and so he falls in the middle at 19.6% with the early part of his career at 34% and the latter at 1.9%. Great stuff.

However, what this analysis fails to take into account as Schechter readily admits is that the score and inherited runners also plays a big role in determining whether and how many saves a pitcher picks up. Some of this is explored in THT's The Bullpen Book.

Sacrifice Flies

The second of two articles on sacrifice flies that I wrote is up on THT this morning. The first article looks at scoring from the overall persepctive and that of outfielders while the second looks at it from the perspective of base runners.

Of course, this is a small part of the game but one for which I had never seen any data collected. Next up will be a look at the issue from the team level....