Batters



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Showing page 637 of 4181 (83612 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
1986 HOU Phil Garner 347 114 313 130 0.328 0.415 0.744 0.315 0.384 0.699 0.322 0.399 0.721 0.698 2.3 4.6 6.9 1.00 2.3 4.4 6.7
1988 KCA Bo Jackson 468 134 439 207 0.286 0.472 0.758 0.314 0.391 0.705 0.300 0.431 0.731 0.712 -6.4 17.7 11.3 1.05 -8.1 14.8 6.7
2025 KCA Carter Jensen 69 27 60 33 0.391 0.550 0.941 0.320 0.411 0.731 0.356 0.481 0.836 0.716 2.5 4.1 6.6 1.01 2.5 4.2 6.7
2018 KCA Jon Jay 266 95 238 89 0.357 0.374 0.732 0.323 0.413 0.737 0.340 0.394 0.734 0.734 8.4 -1.7 6.7 1.00 8.4 -1.7 6.7
2023 KCA Nelson Velazquez 147 44 133 77 0.299 0.579 0.878 0.314 0.398 0.712 0.307 0.489 0.795 0.727 -1.4 11.1 9.7 1.10 -1.9 8.6 6.7
1998 KCA Tim Spehr 36 16 25 11 0.444 0.440 0.884 0.323 0.420 0.743 0.384 0.430 0.814 0.770 4.2 2.8 7.0 1.05 4.0 2.7 6.7
1964 LAA Jim Piersall 276 96 255 97 0.348 0.380 0.729 0.304 0.378 0.682 0.326 0.379 0.705 0.693 6.0 0.1 6.1 0.95 6.7 0.0 6.7
2019 LAN Chris Taylor 414 137 366 169 0.331 0.462 0.792 0.325 0.439 0.764 0.328 0.450 0.778 0.751 1.3 4.0 5.3 0.94 1.7 5.0 6.7
2019 LAN Edwin Rios 56 22 47 29 0.393 0.617 1.010 0.322 0.415 0.738 0.358 0.516 0.874 0.750 2.0 4.8 6.8 1.00 2.0 4.7 6.7
2005 LAN Milton Bradley 316 109 283 137 0.345 0.484 0.829 0.342 0.439 0.781 0.344 0.461 0.805 0.745 0.4 6.6 7.0 0.98 0.1 6.6 6.7
2012 MIA Rob Brantly 113 42 100 46 0.372 0.460 0.832 0.317 0.388 0.705 0.344 0.424 0.768 0.716 3.1 3.6 6.7 1.02 3.1 3.6 6.7
1995 MIL Jeff Cirillo 384 142 328 145 0.370 0.442 0.812 0.336 0.440 0.776 0.353 0.441 0.794 0.769 6.4 0.1 6.5 1.01 6.4 0.2 6.7
2004 MIL Russell Branyan 182 59 158 83 0.324 0.525 0.849 0.334 0.415 0.749 0.329 0.470 0.799 0.753 -0.9 8.9 8.0 1.00 -1.3 8.0 6.7
2018 MIN Jake Cave 309 96 283 134 0.311 0.474 0.784 0.322 0.405 0.727 0.316 0.439 0.756 0.734 -1.8 9.6 7.8 1.03 -2.1 8.8 6.7
2016 MIN Joe Mauer 576 209 494 192 0.363 0.388 0.751 0.320 0.410 0.730 0.342 0.399 0.741 0.743 12.5 -5.5 7.0 1.00 12.6 -5.9 6.7
2016 MIN Miguel Sano 495 158 437 202 0.319 0.462 0.781 0.321 0.430 0.751 0.320 0.446 0.766 0.744 -0.5 7.4 6.9 1.03 -0.5 7.2 6.7
1945 NY1 Whitey Lockman 148 59 129 62 0.399 0.481 0.879 0.361 0.405 0.765 0.380 0.443 0.822 0.691 2.8 4.9 7.7 0.99 2.5 4.3 6.7
1966 NYA Billy Bryan 74 20 69 29 0.270 0.420 0.690 0.303 0.356 0.659 0.287 0.388 0.675 0.671 0.4 6.7 7.1 1.04 0.4 6.3 6.7
2017 NYA Brett Gardner 682 237 594 254 0.348 0.428 0.775 0.329 0.423 0.752 0.338 0.425 0.763 0.751 6.2 0.1 6.3 0.99 6.5 0.3 6.7
1922 NYA Wally Schang 492 191 408 168 0.388 0.411 0.800 0.357 0.416 0.773 0.373 0.414 0.786 0.735 7.7 -1.0 6.7 0.99 7.8 -1.1 6.7
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Columns:
--------

Note: The batter's composite OB% and SLG% is obtained by the sum of all individual
plate appearances. For each PA, the OB% and SLG% used is versus pitchers of the same
hand as the one he's facing.

OPP_PIT_OB: the opposing pitcher OB% against, when facing batters of the same hand
OPP_PIT_SLG: the opposing pitcher SLG% against, when facing batters of the same hand
OPP_PIT_OOPS: the opposing pitcher OB% + SLG% against, when facing batters of the same hand

EXPCT_OB_AVG: the average of the opposing pitcher's OPP_PIT_OB and the batter's OB% (vs. L or R)
EXPCT_SLG_AVG: the average of the opposing pitcher's OPP_PIT_SLG and the batter's SLG% (vs. L or R)
EXPCT_OPS: the average of the opposing pitcher's OOPS and the batter's OPS (vs. L or R)

LG_OPS: the average league OPS, with the league of the home park being the league

PME_OB: the cumulative result of the plate appearance minus the EXPCT_OB_AVG
PME_SLG: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG
PME: the cumulative result of the plate appearance minus the EXPCT_OPS

PF: the composite park factor the batter experienced, based on lefty-righty and park

PME_OB_PF: the cumulative result of the plate appearance minus the EXPCT_OB_AVG, with PF
PME_SLG_PF: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG, with PF
PME_PF: the cumulative result of the plate appearance minus the EXPCT_OPS, with PF


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).