Batters



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Showing page 671 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
1972 SFN Gary Matthews 71 25 62 33 0.352 0.532 0.884 0.315 0.382 0.697 0.333 0.457 0.790 0.676 1.3 4.7 6.0 1.00 1.3 4.7 5.9
1911 SLA Joe Kutina 107 30 101 46 0.280 0.456 0.736 0.304 0.320 0.625 0.292 0.388 0.680 0.688 -1.5 7.0 5.5 0.99 -1.6 7.6 5.9
1951 SLA Ken Wood 367 107 333 143 0.292 0.430 0.722 0.321 0.356 0.677 0.307 0.393 0.699 0.719 -5.4 12.3 6.9 1.01 -5.8 11.8 5.9
1971 SLN Joe Hague 443 146 380 149 0.329 0.392 0.722 0.322 0.371 0.693 0.326 0.382 0.707 0.679 1.6 4.1 5.7 1.02 1.6 4.3 5.9
2017 SLN Kolten Wong 411 154 354 146 0.375 0.413 0.787 0.334 0.436 0.769 0.354 0.424 0.778 0.746 8.5 -3.8 4.7 0.98 9.1 -3.2 5.9
1976 SLN Mike Anderson 230 85 199 71 0.370 0.356 0.726 0.307 0.365 0.672 0.338 0.361 0.699 0.677 7.2 -0.6 6.6 1.11 6.5 -0.5 5.9
2015 TBA J. P. Arencibia 73 23 71 43 0.315 0.605 0.920 0.314 0.419 0.733 0.315 0.512 0.827 0.726 0.1 6.5 6.6 0.96 0.0 5.9 5.9
2023 TBA Jose Siri 364 97 338 167 0.266 0.494 0.760 0.312 0.410 0.722 0.289 0.452 0.741 0.729 -8.3 14.1 5.8 0.99 -8.3 14.2 5.9
2014 TOR Jose Reyes 655 214 610 243 0.327 0.398 0.725 0.319 0.387 0.706 0.323 0.393 0.716 0.703 3.0 3.6 6.6 1.04 2.8 3.1 5.9
2022 WAS Yadiel Hernandez 327 102 305 125 0.312 0.410 0.722 0.306 0.379 0.685 0.309 0.394 0.703 0.711 1.0 5.0 6.0 1.03 1.0 4.8 5.9
1951 WS1 Clyde Kluttz 180 70 159 61 0.389 0.384 0.773 0.330 0.362 0.692 0.359 0.373 0.732 0.719 4.8 0.9 5.7 0.99 5.0 0.9 5.9
1920 WS1 Jim O'Neill 322 100 296 119 0.310 0.402 0.712 0.320 0.361 0.681 0.315 0.381 0.697 0.723 -1.5 6.3 4.8 0.98 -1.2 7.1 5.9
1915 WS1 Joe Judge 51 24 41 19 0.471 0.463 0.934 0.358 0.334 0.692 0.415 0.399 0.813 0.640 2.9 3.0 5.9 1.00 2.9 3.0 5.9
1942 WS1 Mickey Vernon 685 230 621 241 0.336 0.388 0.724 0.332 0.367 0.698 0.334 0.377 0.711 0.681 1.5 6.5 8.0 1.04 0.5 5.3 5.9
2023 ANA Taylor Ward 409 137 356 150 0.335 0.421 0.756 0.313 0.412 0.725 0.324 0.416 0.740 0.728 4.5 1.9 6.4 0.99 4.6 1.2 5.8
2022 ARI Emmanuel Rivera 148 45 132 56 0.304 0.424 0.728 0.304 0.379 0.683 0.304 0.401 0.706 0.710 0.9 4.0 4.9 1.01 1.2 4.6 5.8
2000 ARI Jay Bell 649 224 565 247 0.346 0.438 0.783 0.331 0.432 0.764 0.338 0.435 0.773 0.769 4.6 1.2 5.8 1.00 4.6 1.2 5.8
1989 ATL Dale Murphy 647 198 574 207 0.306 0.360 0.666 0.291 0.357 0.649 0.299 0.359 0.658 0.674 4.7 1.2 5.9 1.00 4.6 1.1 5.8
1980 ATL Glenn Hubbard 487 156 431 161 0.320 0.374 0.694 0.303 0.365 0.667 0.311 0.369 0.681 0.691 4.4 1.7 6.1 1.02 4.3 1.6 5.8
1955 BAL Bob Hale 190 71 182 74 0.374 0.407 0.780 0.345 0.378 0.723 0.359 0.392 0.751 0.713 2.8 2.6 5.4 0.98 3.2 2.7 5.8
No results found.

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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).