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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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....