Batters



Reset All Picks
Showing page 43 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
1987 PHI Mike Schmidt 613 238 522 286 0.388 0.548 0.936 0.308 0.394 0.702 0.348 0.471 0.819 0.728 24.8 40.0 64.8 1.04 23.5 37.8 61.3
1988 TOR Fred McGriff 623 234 536 296 0.376 0.552 0.928 0.326 0.381 0.706 0.351 0.467 0.817 0.712 15.5 46.4 61.8 1.01 15.4 46.0 61.3
2002 HOU Jeff Bagwell 691 277 571 296 0.401 0.518 0.919 0.315 0.401 0.716 0.358 0.460 0.818 0.738 29.6 32.9 62.6 1.02 28.9 32.2 61.2
2010 PHI Jayson Werth 652 253 554 295 0.388 0.532 0.921 0.323 0.400 0.722 0.355 0.466 0.821 0.720 21.3 36.7 57.9 0.98 22.5 38.8 61.2
1941 BRO Joe Medwick 579 210 538 278 0.363 0.517 0.879 0.307 0.341 0.648 0.335 0.429 0.764 0.682 16.0 47.1 63.2 1.03 15.5 45.5 61.1
1955 BRO Roy Campanella 522 204 446 260 0.391 0.583 0.974 0.316 0.392 0.708 0.354 0.487 0.841 0.731 19.4 41.9 61.4 1.01 19.3 41.7 61.1
2019 COL Nolan Arenado 662 251 588 343 0.379 0.583 0.962 0.308 0.412 0.720 0.343 0.498 0.841 0.752 23.7 50.1 73.9 1.11 19.6 41.4 61.1
1919 DET Harry Heilmann 594 212 536 257 0.357 0.479 0.836 0.304 0.339 0.643 0.330 0.409 0.740 0.681 15.8 37.7 53.5 0.93 18.0 43.1 61.1
2008 NYA Alex Rodriguez 594 233 510 292 0.392 0.573 0.965 0.323 0.413 0.737 0.358 0.493 0.851 0.754 20.6 40.9 61.4 1.00 20.5 40.7 61.1
2022 SDN Manny Machado 644 236 578 307 0.366 0.531 0.898 0.308 0.397 0.705 0.337 0.464 0.801 0.711 18.9 38.9 57.8 0.94 20.0 41.1 61.1
1988 CHN Andre Dawson 640 220 591 298 0.344 0.504 0.848 0.289 0.355 0.644 0.317 0.430 0.746 0.669 17.5 44.4 61.9 1.01 17.2 43.8 61.0
1963 DET Al Kaline 616 230 551 283 0.373 0.514 0.887 0.298 0.371 0.668 0.336 0.442 0.778 0.688 23.3 39.5 62.8 1.02 22.6 38.4 61.0
2008 HOU Lance Berkman 665 279 554 314 0.420 0.567 0.986 0.345 0.431 0.776 0.383 0.499 0.881 0.740 24.5 38.2 62.7 1.02 23.8 37.2 61.0
1989 MIL Robin Yount 690 264 614 314 0.383 0.511 0.894 0.318 0.383 0.701 0.350 0.447 0.798 0.707 22.3 39.1 61.4 1.01 22.2 38.8 61.0
2019 MIN Nelson Cruz 521 204 454 290 0.392 0.639 1.030 0.325 0.449 0.775 0.358 0.544 0.902 0.761 17.1 43.3 60.4 0.99 17.3 43.7 61.0
2001 MON Vladimir Guerrero 671 253 599 339 0.377 0.566 0.943 0.315 0.408 0.723 0.346 0.487 0.833 0.753 21.0 47.7 68.7 1.06 18.6 42.4 61.0
2000 NYN Edgardo Alfonzo 650 276 544 295 0.425 0.542 0.967 0.332 0.431 0.763 0.378 0.487 0.865 0.770 30.0 30.6 60.6 1.00 30.2 30.8 61.0
2025 NYN Juan Soto 715 282 577 303 0.394 0.525 0.920 0.316 0.409 0.724 0.355 0.467 0.822 0.718 28.2 33.5 61.7 1.01 27.9 33.1 61.0
1960 BOS Ted Williams 390 176 310 200 0.451 0.645 1.096 0.327 0.384 0.712 0.389 0.515 0.904 0.711 24.1 40.7 64.8 1.06 22.6 38.3 60.9
1956 BRO Duke Snider 652 258 542 324 0.396 0.598 0.993 0.345 0.433 0.778 0.370 0.515 0.886 0.718 16.6 45.3 61.9 1.01 16.3 44.6 60.9
No results found.

*** The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at 20 Sunset Rd., Newark, DE 19711. ***










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).