Batters



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Showing page 37 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 PHI Dick Allen 506 190 438 251 0.375 0.573 0.949 0.301 0.361 0.662 0.338 0.467 0.805 0.684 18.8 46.8 65.6 1.02 18.3 45.7 64.0
1937 PHI Dolph Camilli 570 253 475 279 0.444 0.587 1.031 0.339 0.393 0.732 0.391 0.490 0.881 0.709 29.9 46.3 76.2 1.13 25.1 38.8 63.9
2002 PHI Pat Burrell 684 257 586 319 0.376 0.544 0.920 0.313 0.403 0.716 0.344 0.474 0.818 0.738 21.4 41.9 63.4 0.98 21.6 42.2 63.9
2001 SEA Bret Boone 690 255 623 360 0.370 0.578 0.947 0.324 0.426 0.751 0.347 0.502 0.849 0.760 15.4 47.2 62.6 0.98 15.7 48.2 63.9
1960 SLN Ken Boyer 616 228 552 310 0.370 0.562 0.932 0.308 0.386 0.695 0.339 0.474 0.813 0.704 19.0 48.4 67.5 1.05 18.0 45.8 63.9
1987 TOR George Bell 667 234 610 369 0.351 0.605 0.956 0.325 0.424 0.749 0.338 0.514 0.852 0.756 8.7 55.4 64.1 1.00 8.7 55.2 63.9
2007 ATL Chipper Jones 600 255 513 310 0.425 0.604 1.029 0.349 0.445 0.794 0.387 0.525 0.912 0.753 22.8 40.8 63.7 1.00 22.8 40.9 63.8
2017 COL Nolan Arenado 680 253 606 355 0.372 0.586 0.958 0.313 0.422 0.735 0.343 0.504 0.847 0.746 19.9 50.1 70.0 1.06 18.1 45.7 63.8
1982 MON Gary Carter 653 247 557 284 0.378 0.510 0.888 0.303 0.369 0.672 0.341 0.439 0.780 0.688 24.4 39.0 63.5 1.00 24.5 39.2 63.8
2000 NYN Mike Piazza 545 217 482 296 0.398 0.614 1.012 0.330 0.429 0.759 0.364 0.522 0.886 0.770 18.4 44.7 63.1 0.99 18.6 45.2 63.8
1995 CIN Reggie Sanders 567 225 484 280 0.397 0.579 0.975 0.319 0.405 0.724 0.358 0.492 0.850 0.735 22.0 42.1 64.2 0.98 21.8 41.8 63.7
1995 OAK Mark McGwire 422 186 317 217 0.441 0.685 1.125 0.334 0.432 0.765 0.387 0.558 0.945 0.769 22.6 40.3 63.0 0.97 22.9 40.7 63.7
1952 CLE Larry Doby 611 233 519 281 0.381 0.541 0.923 0.326 0.369 0.695 0.354 0.455 0.809 0.690 17.0 45.0 61.9 0.98 17.5 46.2 63.6
2005 CLE Travis Hafner 578 236 486 289 0.408 0.595 1.003 0.332 0.428 0.759 0.370 0.511 0.881 0.753 22.2 40.2 62.4 0.97 22.6 41.0 63.6
2016 DET Miguel Cabrera 679 267 595 335 0.393 0.563 0.956 0.318 0.429 0.747 0.355 0.496 0.852 0.743 25.6 40.4 66.0 1.04 24.7 38.9 63.6
2017 HOU Jose Altuve 662 271 590 323 0.409 0.547 0.957 0.317 0.430 0.747 0.363 0.489 0.852 0.752 30.6 34.5 65.1 0.97 29.9 33.7 63.6
1915 PTF Ed Konetchy 638 223 577 275 0.350 0.477 0.826 0.293 0.322 0.615 0.321 0.399 0.720 0.651 18.0 44.4 62.3 0.99 18.4 45.3 63.6
1994 SDN Tony Gwynn 475 215 419 238 0.453 0.568 1.021 0.339 0.408 0.747 0.396 0.488 0.884 0.743 26.9 33.8 60.6 0.96 28.2 35.5 63.6
1956 CHA Minnie Minoso 665 281 545 286 0.423 0.525 0.947 0.332 0.391 0.723 0.377 0.458 0.835 0.731 30.0 36.1 66.1 1.04 28.8 34.7 63.5
1994 CIN Kevin Mitchell 380 163 310 211 0.429 0.681 1.110 0.315 0.410 0.725 0.372 0.546 0.918 0.743 21.7 41.6 63.3 1.01 21.8 41.7 63.5
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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).