“Athletes who are chemically propelled to victory do not merely
overvalue winning, they misunderstand why winning is properly valued.”
George Will, “Barry Bonds‘ Enhancement,” Newsweek, May 21, 2007
From the day in February when pitchers and catchers reported, all the way through the release of the Mitchell Report last week, nary a day went by without something interesting for the fan to follow in 2007. So for this final column of the year, we’ll look both at on-field and off-field issues with a pair of topics that couldn’t be more different from one another. First, I’ll editorialize a bit on the Mitchell report, and then we’ll dive into the sometimes murky world of outfield defense as we continue our series on Simple Fielding Runs (SFR).
An Era Defined
Before we dig into today’s topic, it’s appropriate to spill a little more ink on the subject of the Mitchell report. Although mine is probably a minority opinion among analysts of the game at sites like ours, my take on the report and its contents is largely positive. Further, although many people look dimly on the historical aspects of the report while praising the recommendations, it is precisely that look back–in at least some of its gory details–that appropriately frames the discussion on how to move forward.
For me the most relevant passage in the entire discussion of PEDs is the quote on page 60 (in the PDF version) by Bart Giamatti. It gets to the core of all of the scrutiny by the media and the concern of fans, and is what should propel the part of Major League Baseball and the player’s association to more fully address the issue:
. . . acts of cheating are intended to alter the very conditions of play to favor one person. They are secretive, covert acts that strike at and seek to undermine the basic foundation of any contest declaring the winner–that all participants play under identical rules and conditions. Acts of cheating destroy that necessary foundation and thus strike at the essence of a contest. They destroy faith in the games’ integrity and fairness; if participants and spectators alike cannot assume integrity and fairness, and proceed from there, the contest cannot in its essence exist.
In the final analysis, this assault on the integrity of the game is the reason that PEDs pose such a threat to the sport, and why it was incumbent on Major League Baseball to give at least some accounting of the scope and nature of the problem. In a cynical age–where increasing revenues are thought to excuse any behavior and justify our looking away–that view may seem simple-minded. But an issue that is allowed to eat away at the very essence of the sport (whether or not we give it much consideration) will surely eventually destroy it. It is for this reason fans need to understand, in broad terms anyway, how large the problem was at its height in order to come to terms with how much that foundation was under assault. It seems to me that the Mitchell Report performs this function nicely by striking a balance between chasing down many possible violations, providing convincing evidence of the reality and extent of the problem, and framing the report in a historical context.
Contrary to the opinion of my colleague Joe Sheehan, I don’t believe that the report “put little to no blame on upper management of teams or the game itself” and “instead elected to blame the MLBPA and its members as obstructionists.” As I read it, pages 108 through 159 are one long indictment of all levels within the operating structure of Major League Baseball. The report documents that, from the top to bottom, the chain of authority continuously buried their collective head in the sand, cowered in the face of the MLBPA, didn’t enforce or even communicate their own policies, and generally encouraged the proliferation of a problem that was becoming increasingly obvious, a problem documented by the concurrent media stories mentioned in the report.
In my view, Mitchell strongly makes the case that it wasn’t only the MLBPA being obstructionist (which they were, especially in regards to the laughable “reasonable cause” testing in place before the 2002 CBA) that allowed the problem to get out of hand. By citing example after example of situations involving people who could have and should have taken action in various ways, it highlights the questionable behavior of a whole host of non-players at nearly every level. It starts at the very top with Bud Selig himself (who, the report noted, said in 1995 at a time he should have and probably did know better “[i]f baseball has a problem, I must say candidly that we were not aware of it… It certainly hasn’t been talked about much”), and continues down the line with Fay Vincent, Sandy Alderson, Tony La Russa, Dave McKay, Lee Thomas (the former Phillies GM), Jeff Cooper (the former Phillies head trainer), Randy Smith (when he was Padres GM), Brian Sabean (Giants GM), Kevin Hallinan (baseball’s security director), Bruce Bochy, Phil Garner, Tom Kelly, Dr. Robert Millman (physician who served as the medical director for MLB), and Rob Manfred, not to mention various clubhouse attendants, equipment managers, and other club athletic trainers. One gets the impression from reading these pages that all of the relevant information and warning signs were well-known within the industry by the mid 1990s, and that the failure to act and head off another stain on the game can be laid in equal proportions at the feet of both MLB and the MLBPA.
In the testimony of Kirk Radomski and Brian McNamee the evidence paints a picture of at least some of the avenues through which PEDs entered the game, how they were distributed, and the informal networks and word of mouth through which they spread. Although clearly just a small sample of the activities that were and–to some extent–still are no doubt going on, it appears their main purpose was to directly support some of the recommendations, including the establishment of a Department of Investigations, screening and testing of clubhouse personnel, logging of packages, and an expanded power to interview players. From that perspective, the names of the 90 or so players was less an attack on players in general and instead critical support of the arguments the report was making. That is, the names and documents were provided to unambiguously authenticate the nature and extent of the problem (and it also gives us insight into the demographic breakdown in terms of positions, age, and performance level) thereby underscoring the need to implement changes. But just as importantly, what the report did correctly was that in providing a smaller number of names (whether by design or lack of cooperation) that was not overwhelming and encouraging the commissioner to “forego imposing discipline on players for past violations,” it balanced the need to come clean about the past with the need to move forward.
In the end, my take is that not only was the report necessary and should serve as the bookend to the unconstrained problem of PEDs in baseball, but fans can be encouraged as we move forward. Although no urine test for HGH is available, the report points out that the testing programs at both at the minor (p. 94) and major league levels have been effective, showing an overall decrease in the number of positives, as illustrated in the table below that shows the results of the minor league testing program that began in 2001:
Minor League Test Results Year Tests Pos Pct 2001 4850 439 9.1% 2002 4719 227 4.8% 2003 4772 173 3.6% 2004 4801 78 1.6% 2005 5961 106 1.8% 2006 6433 23 0.4%
Here’s hoping we can look forward to a continued restoration of the level playing field upon which baseball, like all sports, is built.
Ranging into the Outfield
And now on to today’s second topic. Several weeks ago, a comment on the throwing arm of new Twins outfielder Delmon Young led to the creation of a fielding system I dubbed “Simple Fielding Runs” (SFR), based on the kind of play-by-play data available in Retrosheet-style play by play logs. Both in the initial column and in last week’s column, I made first cuts at developing the system for infielders. Finally, however, the patience of some readers (and you know who you are) will be rewarded as we take a stab at the outfield and, as a bonus, try and complete the picture with outfielder’s throwing prowess.
As an aside, I should mention that although I wasn’t aware of it, Angels fan and blogger Sean Smith had published results from a similar system back in April, and it now appears he’s providing 2007 results. It’ll be interesting to see how closely these two systems correlate.
As mentioned above, this system is based on play-by-play data, and therefore does not have the benefit of zone data, which for the likes of Baseball Info Solutions (BIS) or STATS, Inc. form the major component of systems like Mitchell Lichtman’s Ultimate Zone Rating (UZR), ESPN’s Zone Rating (ZR), David Pinto‘s Probabilistic Model of Range (PMR), and Shane Jensen’s Spatial Aggregate Fielding Evaluation (SAFE). In order to compensate for this deficiency, we instead create virtual “areas of responsibility” based on the fielder who fielded the ball. While this leads to partitioning rules for infielders whose areas of responsibility are more likely to overlap, we don’t have the same problem to the same degree in the outfield, where the areas are larger and there is less overlap. This allows us to simplify the methodology somewhat, with the result that we’ll assign only balls that were fielded by an outfielder to his area of responsibility. In order to come up with a measuring stick to use to measure an outfielder, we’ll then compare how frequently batted balls in his area are turned into outs as compared to his peers by constructing a baseline matrix in much the same way we did for infielders. And not only will we look at the conversion of balls into outs, but we’ll also see how many bases that typically translates into in order to convert the difference into runs.
We do have another kind of problem in dealing with outfielders that does not plague infielders, however, and that of course is the differences in the ballparks in which they play. Obviously, ceteris paribus, in outfields that are more spacious more balls will drop for hits. In a recent article by Lichtman, he showed that Coors Field has an outfield area of roughly 118,000 square feet at the high end, while the outfield at Philadelphia’s Citizen’s Bank Park is at just 104,000; that’s a difference of almost 12 percent. Fenway Park, of course, is an exception, since although its dimensions are short to left field the number of batted balls that become hits is very high due to the frequency with which they hit off the Green Monster. To account for these differences, we’ll create three-year park factors; after applying our matrix, we’ll then apply the park factors to each and every batted ball fielded by an outfielder. Let’s now dig down into each of these areas.
Peer Pressure
As with infielders, the baseline matrix we create will incorporate several attributes, including the position to which the ball was hit, the type of hit (line drive, ground ball, fly ball, and popup), and the handedness of the batter. By aggregating all balls hit to each position and then calculating the percentage of times the batter reached base along with the number of bases gained per ball hit, we can create a matrix like that shown in Table 1 for left fielders in 2007:
Table 1: Left Fielders in 2007 Type Bats Balls Runners TB Out% Bases/Ball F L 5526 794 1249 .856 0.23 F R 4466 870 1457 .805 0.33 ----------------------------------------------------- G L 796 794 913 .003 1.15 G R 3108 3108 3739 .000 1.20 ----------------------------------------------------- L L 2647 2096 2652 .208 1.00 L R 5295 4546 6063 .141 1.15 ----------------------------------------------------- P L 1 1 1 .000 1.00 P R 2 2 3 .000 1.50 ----------------------------------------------------- Total 21841 12211 16077 .441 0.74
For left fielders, line drives hit by right-handed hitters are more difficult to convert into outs than are line drives by lefties. The same can be said of fly balls, presumably both because of the way the ball comes off the bat of a lefty when hit to the opposite field and because the ball is typically hit softer, a point backed up by the fact that the table shows that right-handed hitters gain more bases than their sinister counterparts on average in all three scenarios. Given this, you won’t be surprised to learn that a matrix for right-fielders shows exactly the opposite, with lefties having more success, and that for center fielders the differences are minimal.
To illustrate how the matrix is used, we’ll take the example of Pirates left fielder Jason Bay. In 2007 Bay fielded 286 fly balls, 117 grounders, and 282 line drives while playing left field in 138 equivalent nine-inning games. Table 2–further broken down by batter handedness–lists the number of runners that reached base, the total number of bases gained by those runners compared with how many runners and bases we would expect for an average left fielder in 2007, along with the run value assigned to each scenario:
Table 2: Jason Bay in 2007 Hit Bats Balls Runners TB ExRunners ExTB SFR G L 14 14 19 14 16 -1.0 G R 103 103 117 103 124 2.3 -------------------------------------------------------------- F L 124 14 20 18 28 4.2 F R 162 40 71 32 53 -9.5 -------------------------------------------------------------- L L 72 59 73 57 72 -1.1 L R 210 189 255 180 240 -8.4 -------------------------------------------------------------- Total 685 419 555 404 533 -13.4
In order to compare Bay to the average left fielder and convert these values into runs, we then multiply the difference between the number of expected hits and actual hits by 0.74 (the linear weights value of an out converted into a single) and add this to the difference between the expected number of total bases and actual total bases minus the expected number of hits multiplied by 0.33. We’re essentially crediting each hit saved or given up as a single and then converting as many of those to doubles as the number of total bases saved or given up indicates, since the difference between the value of a double (1.07) and a single is 0.33.
What I like about this system is that it has the potential to account for the ability of outfielders to cut off balls in the gaps and hold runners to singles and doubles. In fact, Bay scored well in this category in 2007, as he picked up over a run on grounders by holding the opposition to 136 total bases in these scenarios, as opposed to an expected total of 140.
It should be remembered, though, that by assigning only those balls fielded by the outfielder to his area of responsibility, we are not accounting for the fact that some outfielders expand their zones by covering more ground and taking balls away from either middle infielders (pop flies) or other outfielders (on fly balls, line drives, and ground balls). To that extent, this metric doesn’t give additional credit to “ball hogs” which, depending on your point of view, is either a good or bad thing.
As you can see from Table 2, Bay also did well on fly balls hit by left-handers, although his performance on fly balls (-9.5) and line drives (-8.4) by righties sinks his overall total to -13.4 runs.
More Context
After running the algorithm described above on all outfielders, we end up with an “unadjusted” SFR total. The total is unadjusted, since we haven’t yet considered the ballparks.
To account for this context we can create a matrix like Table 1 for each park. For example, at PNC Park from 2005-2007 we find the breakdown for left field to look like this:
Table 3: PNC Park Left Field 2005-2007 Type Bats Balls Runners TB Out% Bases/Ball F L 412 71 112 .828 0.27 F R 580 124 227 .786 0.39 ----------------------------------------------------- G L 52 52 60 .000 1.15 G R 317 317 371 .000 1.17 ----------------------------------------------------- L L 201 158 198 .214 0.99 L R 638 553 710 .133 1.11 ----------------------------------------------------- Totals 2200 1275 1678 .420 0.76
You can see that overall the out percentage was lower at PNC than for the league as a whole. However, comparing this to the league average wouldn’t be sufficient since this data set includes an over-representation of Pirates left fielders and, with the unbalanced schedule, all left fielders in the NL Central. If Bay and these others are simply worse than average fielders, we may end up assuming that at PNC it is tougher to turn batted balls into outs. By comparing these percentages to those for all Pirates road games during the period we can calculate park factors for each scenario, as shown in Table 4:
Table 4: PNC Park Factors for Left Field 2005-2007 Road Home Type Bats Balls Runners TB Balls Runners TB ReachPF TBPF F L 398 53 83 412 71 112 1.29 1.30 F R 496 101 174 580 124 227 1.05 1.12 ----------------------------------------------------------------------------- G L 47 47 54 52 52 60 1.00 1.00 G R 332 332 401 317 317 371 1.00 0.97 ----------------------------------------------------------------------------- L L 201 158 195 201 158 198 1.00 1.02 L R 604 521 706 638 553 710 1.00 0.95
Before anyone emails me, yes, there were exactly the same number of line drives hit by left-handed batters at home and on the road (201) and exactly the same number of runners reached base (158). Strange but true.
The two columns on the right indicate the ratio of home to road; a ReachPF value 1.29 indicates that runners reached base more frequently on fly balls hit by lefties at PNC park than on the road. Right-handed hitters did likewise, but the magnitude (1.05) of the difference is smaller. Overall, left field at PNC ranked fifth in baseball in highest ReachPF in 2005-2007 with a value of 1.18 while left field at Fenway Park took the top spot at 1.31. Right field at the Great American Ballpark had the lowest ReachPF at 0.85.
Armed with these ratios we can now apply the park factors to each and every ball fielded by an outfielder. In other words, rather than apply one factor for a player’s home park globally after making the calculations we did in Table 2, we’ll instead apply individual park factors at the most granular level. To do this, we simply multiply the expected number of runners and total bases by the appropriate park factors and subtract the actual number of runners and total bases. So for a park factor greater than 1.00 we expect more runners to reach, so we end up not penalizing a fielder for seemingly allowing more runners to reach base. To calculate the run values we use the same algorithm as described above.
For Jason Bay, that means applying the factor to the 99 different scenarios that he encountered in 2007, taking into consideration the ballpark, position, hit type, and batter hand. When we do so and then sum all of these adjustments by hit type and batter handedness, we come up with the results in Table 5 for Bay in left field in 2007:
Table 5: Jason Bay, Left Field 2007 UnAdjusted Park Adjusted Type Bats Balls Runners TB ExRunner ExTB SFR ExRunner ExTB SFR F L 124 14 20 18 28 4.2 20 31 6.0 F R 162 40 71 32 53 -9.5 32 55 -8.8 ----------------------------------------------------------------------------- G L 14 14 19 14 16 -1.0 14 16 -1.0 G R 103 103 117 103 124 2.3 103 122 1.5 ----------------------------------------------------------------------------- L L 72 59 73 57 72 -1.1 57 72 -0.9 L R 210 189 255 180 240 -8.4 181 234 -10.1 ----------------------------------------------------------------------------- Totals 685 419 555 404 533 -13.4 407 530 -13.3
As you can see, his total didn’t change much at all, going from -13.4 unadjusted to -13.3 when we consider the park. That is not the case, of course, for other outfielders. For example, Manny Ramirez goes from a -21.0 unadjusted SFR to -2.6 once his park context is accounted for, with the entire magnitude of the difference coming from the Fenway Park adjustment. Overall, Bay ranked third from the bottom for left fielders, ahead of only Pat Burrell (-14.4) and Chris Duncan (-17.7).
The Results
With the calculations complete we can now show the leaders and trailers for each outfield position in 2007:
Table 6: Outfielders playing in 80 or more adjusted games in 2007 Name Pos AdjG Balls SFR ------------------------------------------------ Matt Holliday Left 154.3 672 18.9 Eric Byrnes Left 108.4 464 17.5 Alfonso Soriano Left 118.7 551 12.9 Shannon Stewart Left 128.7 603 9.9 Ryan Church Left 80.3 408 9.5 Garret Anderson Left 80.6 334 6.7 Carl Crawford Left 132.4 626 5.4 Craig Monroe Left 89.9 4 0.8 Hideki Matsui Left 109.1 499 -0.5 Jay Payton Left 100.7 514 -1.2 Craig Monroe Left 89.9 381 -2.2 Manny Ramirez Left 110.8 464 -2.6 Carlos Lee Left 152.6 663 -3.4 Geoff Jenkins Left 108.8 537 -4.3 Luis Gonzalez Left 111.0 464 -5.6 Adam Dunn Left 132.6 605 -7.2 Barry Bonds Left 93.6 370 -8.2 Josh Willingham Left 131.6 562 -9.7 Raul Ibanez Left 123.9 578 -12.1 Jason Bay Left 138.0 685 -13.3 Pat Burrell Left 114.7 469 -14.4 Chris Duncan Left 83.3 405 -17.7 ------------------------------------------------ Coco Crisp Center 135.3 715 34.9 Carlos Beltran Center 138.2 712 21.7 Melky Cabrera Center 119.7 682 14.7 David DeJesus Center 150.8 760 12.6 Grady Sizemore Center 157.2 793 12.2 Nook Logan Center 84.4 446 11.6 Vernon Wells Center 142.9 628 6.7 Gary Matthews Jr. Center 127.3 715 0.4 Jim Edmonds Center 92.1 479 -1.7 Corey Patterson Center 117.8 534 -1.8 Andruw Jones Center 150.2 748 -3.2 Curtis Granderson Center 143.4 823 -3.3 Dave Roberts Center 84.3 435 -6.1 Mike Cameron Center 148.0 746 -6.1 Aaron Rowand Center 153.1 780 -6.8 Hunter Pence Center 94.1 494 -6.9 Juan Pierre Center 157.9 748 -8.3 Torii Hunter Center 146.4 780 -8.4 Ichiro Suzuki Center 148.9 812 -8.8 Bill Hall Center 114.4 592 -12.4 Chris Young Center 140.7 729 -12.9 ------------------------------------------------ Luke Scott Right 91.3 391 19.5 Vladimir Guerrero Right 103.6 415 14.1 Austin Kearns Right 153.6 729 14.0 J.D. Drew Right 118.1 427 13.8 Jeremy Hermida Right 109.8 509 13.1 Magglio Ordonez Right 136.1 540 10.1 Corey Hart Right 96.7 472 8.4 Andre Ethier Right 87.2 383 8.0 Nick Markakis Right 155.9 642 7.7 Alex Rios Right 139.7 550 6.8 Brad Hawpe Right 138.1 547 3.3 Jeff Francoeur Right 160.7 659 1.4 Shane Victorino Right 102.6 464 -0.5 Randy Winn Right 97.2 429 -1.2 Xavier Nady Right 83.3 334 -3.4 Shawn Green Right 102.4 405 -5.8 Delmon Young Right 126.6 540 -6.1 Ken Griffey Jr. Right 129.8 617 -6.6 Jermaine Dye Right 128.8 604 -7.0 Michael Cuddyer Right 136.3 569 -7.2 Jose Guillen Right 141.6 624 -10.2 Bobby Abreu Right 148.7 631 -13.5 Mark Teahen Right 128.6 673 -17.1 Brian Giles Right 118.3 477 -23.1
This list appears fairly reasonable, with many of the players you might expect bubbling up towards the top, and those like Bay, Bill Hall, and Brian Giles having a characteristically poor showing. What may be surprising is that Ichiro Suzuki would do so poorly at -8.8, although it turns out he also did poorly in UZR in 2007.
Finally, we’re ready to do what started this three-week excursion in the first place: combine these SFR numbers with the throwing arm numbers developed for this year’s annual in an article titled “Expanding the Cannon: Quantifying the Impact of Outfield Throwing Arms.” The result is a total defensive metric for outfielders that incorporates both traditional fielding and throwing into a single number.
So as we enter the holiday break I’ll leave you with Table 7, which shows each player’s park adjusted SFR along with their Equivalent Throwing Runs (EqThr), which is a total defensive contribution. And for the record, Delmon Young with his combination of poor fielding (-6.1) and good throwing (+9.3) comes out a little to the good, at +3.2 runs overall.
Once again, I’d like to say thanks to all our readers for your support and encouragement in 2007. Here’s wishing everyone a very Merry Christmas and we’ll see you in the New Year.
Table 7: Total Defense for Outfielders in 2007 Name Pos AdjG Balls SFR EqThr Total Defense Alfonso Soriano Left 118.7 551 12.9 12.7 25.6 Matt Holliday Left 154.3 672 18.9 1.3 20.2 Eric Byrnes Left 108.4 464 17.5 -1.5 15.9 Garret Anderson Left 80.6 334 6.7 2.4 9.1 Ryan Church Left 80.3 408 9.5 -0.8 8.7 Hideki Matsui Left 109.1 499 -0.5 3.6 3.1 Carl Crawford Left 132.4 626 5.4 -2.3 3.1 Craig Monroe Left 89.9 4 0.8 2.1 2.9 Shannon Stewart Left 128.7 603 9.9 -8.1 1.9 Craig Monroe Left 89.9 381 -2.2 2.1 -0.1 Jay Payton Left 100.7 514 -1.2 0.8 -0.4 Manny Ramirez Left 110.8 464 -2.6 0.3 -2.3 Carlos Lee Left 152.6 663 -3.4 0.2 -3.2 Geoff Jenkins Left 108.8 537 -4.3 -2.0 -6.3 Josh Willingham Left 131.6 562 -9.7 1.5 -8.2 Luis Gonzalez Left 111.0 464 -5.6 -3.2 -8.8 Raul Ibanez Left 123.9 578 -12.1 0.9 -11.2 Barry Bonds Left 93.6 370 -8.2 -3.4 -11.6 Adam Dunn Left 132.6 605 -7.2 -5.3 -12.5 Jason Bay Left 138.0 685 -13.3 0.4 -12.9 Pat Burrell Left 114.7 469 -14.4 0.6 -13.9 Chris Duncan Left 83.3 405 -17.7 1.4 -16.3 ---------------------------------------------------------------------- Coco Crisp Center 135.3 715 34.9 -1.2 33.8 Carlos Beltran Center 138.2 712 21.7 -0.9 20.8 Melky Cabrera Center 119.7 682 14.7 0.8 15.5 David DeJesus Center 150.8 760 12.6 -4.5 8.1 Nook Logan Center 84.4 446 11.6 -3.5 8.1 Grady Sizemore Center 157.2 793 12.2 -4.6 7.6 Vernon Wells Center 142.9 628 6.7 -0.4 6.3 Jim Edmonds Center 92.1 479 -1.7 3.5 1.8 Curtis Granderson Center 143.4 823 -3.3 4.3 1.0 Andruw Jones Center 150.2 748 -3.2 4.0 0.8 Corey Patterson Center 117.8 534 -1.8 1.6 -0.3 Gary Matthews Jr. Center 127.3 715 0.4 -2.0 -1.6 Ichiro Suzuki Center 148.9 812 -8.8 6.0 -2.8 Dave Roberts Center 84.3 435 -6.1 -0.1 -6.3 Aaron Rowand Center 153.1 780 -6.8 0.4 -6.4 Hunter Pence Center 94.1 494 -6.9 -0.3 -7.2 Mike Cameron Center 148.0 746 -6.1 -2.1 -8.2 Bill Hall Center 114.4 592 -12.4 3.1 -9.2 Torii Hunter Center 146.4 780 -8.4 -2.6 -11.0 Chris Young Center 140.7 729 -12.9 0.0 -12.9 Juan Pierre Center 157.9 748 -8.3 -7.3 -15.6 ---------------------------------------------------------------------- Luke Scott Right 91.3 391 19.5 -0.4 19.1 Alex Rios Right 139.7 550 6.8 9.2 16.0 J.D. Drew Right 118.1 427 13.8 1.2 15.0 Jeff Francoeur Right 160.7 659 1.4 13.1 14.4 Austin Kearns Right 153.6 729 14.0 0.4 14.3 Vladimir Guerrero Right 103.6 415 14.1 -0.5 13.6 Jeremy Hermida Right 109.8 509 13.1 0.0 13.0 Nick Markakis Right 155.9 642 7.7 2.3 10.1 Magglio Ordonez Right 136.1 540 10.1 -2.3 7.8 Shane Victorino Right 102.6 464 -0.5 7.6 7.1 Andre Ethier Right 87.2 383 8.0 -1.1 6.9 Delmon Young Right 126.6 540 -6.1 9.3 3.2 Michael Cuddyer Right 136.3 569 -7.2 9.9 2.7 Corey Hart Right 96.7 472 8.4 -6.4 2.0 Brad Hawpe Right 138.1 547 3.3 -4.9 -1.6 Randy Winn Right 97.2 429 -1.2 -1.6 -2.7 Xavier Nady Right 83.3 334 -3.4 -3.7 -7.1 Jose Guillen Right 141.6 624 -10.2 2.0 -8.2 Shawn Green Right 102.4 405 -5.8 -4.0 -9.8 Mark Teahen Right 128.6 673 -17.1 6.7 -10.4 Jermaine Dye Right 128.8 604 -7.0 -5.2 -12.2 Bobby Abreu Right 148.7 631 -13.5 1.1 -12.4 Ken Griffey Jr. Right 129.8 617 -6.6 -7.8 -14.5 Brian Giles Right 118.3 477 -23.1 -7.8 -31.0
Thank you for reading
This is a free article. If you enjoyed it, consider subscribing to Baseball Prospectus. Subscriptions support ongoing public baseball research and analysis in an increasingly proprietary environment.
Subscribe now