Batters



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Showing page 165 of 4181 (83616 total matches)
YEAR TEAM
ID 		NAME PLATE
APP 		ON
BASE 		AT
BATS 		TOTAL
BASES 		OB
AVG 		SLG
AVG 		OPS OPP
PIT
OB 		OPP
PIT
SLG 		OPP
PIT
OOPS 		EXPCT
OB
AVG 		EXPCT
SLG
AVG 		EXPCT
OPS 		LG
OPS 		PME
OB 		PME
SLG 		PME PF PME
OB
PF 		PME
SLG
PF 		PME
PF
1969 DET Willie Horton 568 188 508 236 0.331 0.465 0.796 0.308 0.356 0.664 0.319 0.410 0.730 0.687 6.6 27.5 34.0 1.00 6.6 27.6 34.1
2009 HOU Lance Berkman 563 224 460 234 0.398 0.509 0.907 0.340 0.429 0.769 0.369 0.469 0.838 0.735 16.2 17.7 33.8 1.00 16.3 17.9 34.1
1962 KC1 Ed Charles 604 212 535 243 0.351 0.454 0.805 0.308 0.374 0.681 0.329 0.414 0.743 0.716 13.1 21.6 34.7 1.02 12.9 21.2 34.1
1990 KCA Bo Jackson 456 156 405 212 0.342 0.523 0.866 0.318 0.388 0.706 0.330 0.455 0.786 0.712 5.4 27.5 33.0 0.99 5.6 28.4 34.1
2019 KCA Hunter Dozier 586 204 523 273 0.348 0.522 0.870 0.314 0.432 0.746 0.331 0.477 0.808 0.761 9.9 24.4 34.3 1.01 9.8 24.3 34.1
1984 NYN Darryl Strawberry 602 206 522 244 0.342 0.467 0.810 0.320 0.364 0.684 0.331 0.416 0.747 0.685 6.5 27.8 34.3 1.02 6.5 27.6 34.1
2004 OAK Eric Chavez 577 229 475 238 0.397 0.501 0.898 0.340 0.431 0.771 0.368 0.466 0.834 0.769 16.5 17.5 34.0 1.00 16.5 17.6 34.1
1989 PHI John Kruk 312 120 281 133 0.385 0.473 0.858 0.314 0.355 0.669 0.349 0.414 0.763 0.674 13.6 22.8 36.3 1.03 12.8 21.4 34.1
1941 PHI Nick Etten 626 252 540 245 0.403 0.454 0.856 0.353 0.397 0.750 0.378 0.425 0.803 0.682 15.5 14.9 30.4 0.95 17.4 16.7 34.1
1941 PIT Elbie Fletcher 641 270 520 238 0.421 0.458 0.879 0.353 0.410 0.762 0.387 0.434 0.821 0.682 22.0 12.3 34.3 1.01 21.9 12.2 34.1
1973 SDN Nate Colbert 598 205 529 238 0.343 0.450 0.793 0.305 0.363 0.668 0.324 0.407 0.730 0.694 11.4 23.1 34.5 1.01 11.3 22.8 34.1
1977 SEA Leroy Stanton 505 172 454 232 0.341 0.511 0.852 0.315 0.394 0.709 0.328 0.452 0.780 0.732 6.4 26.9 33.4 0.99 6.5 27.5 34.1
1991 SFN Kevin Mitchell 423 143 371 191 0.338 0.515 0.853 0.299 0.375 0.674 0.319 0.445 0.763 0.686 8.2 26.3 34.6 0.98 8.1 25.9 34.1
1977 SFN Willie McCovey 548 201 478 239 0.367 0.500 0.867 0.334 0.400 0.734 0.350 0.450 0.800 0.721 9.0 24.1 33.1 1.00 9.3 24.8 34.1
2025 TBA Jonathan Aranda 422 166 370 181 0.393 0.489 0.883 0.314 0.394 0.707 0.353 0.441 0.795 0.718 16.9 17.2 34.0 1.00 16.9 17.3 34.1
2006 TBA Julio Lugo 322 119 289 144 0.370 0.498 0.868 0.323 0.429 0.752 0.346 0.464 0.810 0.775 12.4 20.5 32.9 0.98 12.9 21.2 34.1
1996 ATL Fred McGriff 691 252 617 305 0.365 0.494 0.859 0.335 0.414 0.749 0.350 0.454 0.804 0.735 10.2 24.8 35.0 1.04 9.9 24.1 34.0
1979 BAL Gary Roenicke 453 171 376 191 0.377 0.508 0.885 0.321 0.396 0.717 0.349 0.452 0.801 0.739 12.8 21.1 33.9 0.96 12.8 21.2 34.0
1980 BOS Jim Rice 542 182 504 254 0.336 0.504 0.840 0.316 0.386 0.702 0.326 0.445 0.771 0.727 5.3 30.2 35.4 1.03 5.1 29.0 34.0
1989 BOS Mike Greenwell 641 237 578 256 0.370 0.443 0.813 0.325 0.374 0.699 0.348 0.408 0.756 0.707 14.2 20.1 34.3 1.01 14.1 19.9 34.0
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On every pitcher versus batter matchup, we have a contest of the batter's ability and
the pitcher's ability. Although OPS and OOPS are not perfect statistics, they are
widely embraced and are relatively straightforward for most fans. They're approximations.
At some point, this process can be made smarter. Until then, this is where we are.

What is the batter's average ability on any plate appearance in a season? It's his OPS for the
season. Likewise, the pitcher's OOPS on the play is his seasonal OOPS. What is the expected
outcome? It's the average of the two, of course.

However, we have two issues to deal with -- the handedness (L or R) of the batter and pitcher
and the park where each event occurred.

1) Hand: For each and every PA, the expected outcome is affected by the hand of the batter and
pitcher. But, we only care about the batter's and pitcher's seasonal OPS/OOPS when it matches
the same scenario as the specific PA.

For example: If a left-handed batter is facing a right-handed pitcher, we only care about how
the batter did versus right-handed pitchers that year, and how the pitcher did versus left-handed
batters. Those are the specific OPS/OOPS values used from which to build the expected outcome.

Ex.: A LHB faces a RHP. The batter's OPS versus righties that year was 0.800. The pitcher's OOPS
versus lefties was 0.700. The expected outcome is the average of the two, 0.750.

Suppose the batter makes an out. His on-base average on the play was 0.000 and his slugging average
is also 0.000. On the play, the batter attained a negative PME, 0.000 minus 0.750 = -0.750. Meanwhile,
the pitcher attained a positive PME of 0.750 minus 0.000 = 0.750. All plays balance in this way.

What if the batter singles? His OB% was 1.000 and his SLG% is 1.000. That's an OPS of 2.000. His PME
is 2.000 minus 0.750 = 1.250, and the pitcher's PME is 0.750 minus 2.000 = -1.250.

All ~16 million plays in MLB from 1910-2025 were assessed in this manner.



2) Park: The parks where events occurred are important as well. Using the enhanced Park Factors at
this site -- those which break down PFs by L-L, L-R, R-L, R-R by using a base counting method -- a
composite PF is derived based on all of the PAs a batter had that season. After the seasonal PME is
compiled by adding all of the plays that year, the PME is divided by the PF* to obtain the final PME.

* The PME is compiled at the home and road level and divided by the corresponding PF. The PFs may
not seem correct but are indicative of the season. For example, the Rockies of 2001 had a composite
PF of 1.22. Todd Helton's (as a lefty) was more like 1.18. On the road, he was 0.97 -- for a
composite of 1.08 (1.18 + 0.97) / 2, the value shown. Before applying the PF, his home PME was about
96 and road was 9. Thus, most of the PME reduction was caused at home. It drops by ~16% (twice 1.08)
while his road PME stays relatively constant. His park-adjusted PME drops from ~105 to 91.


NOTE: This analysis concerns only what the batter does at the plate. Things like base running and
the quality of the opposing defense is not factored in (aside from taking extra bases on a hit).