Batters



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Showing page 376 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
2022 CHN Ian Happ 641 219 573 252 0.342 0.440 0.781 0.320 0.405 0.724 0.331 0.422 0.753 0.711 7.2 10.6 17.7 1.03 6.9 10.1 16.9
1984 CHN Jody Davis 579 182 523 220 0.314 0.421 0.735 0.302 0.360 0.661 0.308 0.390 0.698 0.685 3.6 16.2 19.8 1.03 3.1 13.8 16.9
2021 CHN Rafael Ortega 330 118 296 137 0.358 0.463 0.820 0.319 0.392 0.710 0.338 0.427 0.765 0.723 6.4 10.5 16.9 1.02 6.4 10.5 16.9
1928 CIN George Kelly 449 149 402 175 0.332 0.435 0.767 0.319 0.370 0.690 0.326 0.403 0.728 0.730 2.9 13.3 16.2 0.97 3.0 13.9 16.9
2007 CIN Jeff Keppinger 276 108 241 115 0.391 0.477 0.868 0.320 0.419 0.740 0.356 0.448 0.804 0.753 9.8 6.9 16.7 1.00 9.9 7.0 16.9
1975 CIN Ken Griffey 540 209 463 186 0.387 0.402 0.789 0.341 0.385 0.726 0.364 0.393 0.757 0.691 12.4 3.8 16.2 0.96 12.9 4.0 16.9
2003 CIN Ken Griffey 201 74 166 94 0.368 0.566 0.934 0.332 0.409 0.741 0.350 0.488 0.838 0.745 3.6 13.4 17.1 1.01 3.6 13.2 16.9
2007 CIN Scott Hatteberg 417 164 361 171 0.393 0.474 0.867 0.343 0.429 0.771 0.368 0.451 0.819 0.753 10.5 8.6 19.1 1.03 9.3 7.6 16.9
1996 CLE Brian Giles 143 62 121 74 0.434 0.612 1.045 0.353 0.442 0.795 0.393 0.527 0.920 0.793 5.8 10.5 16.3 0.99 6.0 10.9 16.9
1972 CLE Graig Nettles 624 203 557 220 0.325 0.395 0.720 0.311 0.353 0.663 0.318 0.374 0.692 0.645 4.4 11.5 16.1 1.01 4.6 12.1 16.9
1986 CLE Tony Bernazard 636 228 562 256 0.358 0.456 0.814 0.338 0.417 0.755 0.348 0.436 0.784 0.735 6.6 10.4 17.0 1.01 6.6 10.3 16.9
2023 DET Kerry Carpenter 459 156 418 197 0.340 0.471 0.811 0.324 0.406 0.730 0.332 0.438 0.771 0.728 3.6 13.7 17.4 1.03 3.5 13.3 16.9
1977 DET Rusty Staub 695 233 623 279 0.335 0.448 0.783 0.331 0.397 0.728 0.333 0.422 0.756 0.732 1.3 15.8 17.1 1.01 1.3 15.6 16.9
2013 HOU Chris Carter 585 187 506 228 0.320 0.451 0.770 0.308 0.392 0.700 0.314 0.421 0.735 0.723 3.4 15.4 18.7 0.99 3.1 13.9 16.9
1986 HOU Jose Cruz 536 188 479 193 0.351 0.403 0.754 0.319 0.371 0.690 0.335 0.387 0.722 0.698 8.6 7.9 16.5 1.00 8.8 8.1 16.9
1979 KCA Hal McRae 444 155 393 183 0.349 0.466 0.815 0.325 0.406 0.731 0.337 0.436 0.773 0.739 5.4 11.9 17.3 1.02 5.3 11.6 16.9
2016 LAN Adrian Gonzalez 633 221 568 247 0.349 0.435 0.784 0.322 0.404 0.726 0.335 0.420 0.755 0.732 8.6 9.1 17.5 0.98 8.3 8.8 16.9
2020 LAN Mookie Betts 246 90 219 123 0.366 0.562 0.927 0.326 0.442 0.768 0.346 0.502 0.848 0.746 4.8 13.0 17.9 1.06 4.5 12.3 16.9
1987 MIL Greg Brock 602 222 532 233 0.369 0.438 0.807 0.331 0.416 0.747 0.350 0.427 0.777 0.756 11.2 6.1 17.3 1.04 10.9 6.0 16.9
2017 MIL Jesus Aguilar 311 103 279 141 0.331 0.505 0.837 0.314 0.411 0.725 0.323 0.458 0.781 0.746 2.6 13.1 15.7 1.03 2.8 14.1 16.9
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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).