Batters



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Showing page 155 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
1924 BOS Ira Flagstead 661 260 560 236 0.393 0.421 0.815 0.328 0.363 0.691 0.361 0.392 0.753 0.743 21.6 16.0 37.7 1.04 20.3 15.0 35.4
1986 CIN Dave Parker 700 231 637 304 0.330 0.477 0.807 0.320 0.372 0.692 0.325 0.425 0.750 0.698 3.4 33.5 36.8 1.04 3.3 32.2 35.4
1960 CIN Vada Pinson 707 240 652 308 0.339 0.472 0.812 0.324 0.378 0.702 0.332 0.425 0.757 0.704 5.4 31.0 36.5 1.05 5.2 30.1 35.4
1988 DET Alan Trammell 523 195 466 216 0.373 0.464 0.836 0.312 0.386 0.698 0.343 0.425 0.767 0.712 16.0 18.3 34.2 0.96 16.6 18.9 35.4
1921 DET Bobby Veach 692 257 613 324 0.371 0.529 0.900 0.362 0.430 0.792 0.367 0.479 0.846 0.754 3.3 30.4 33.7 0.97 3.5 31.9 35.4
1960 DET Eddie Yost 636 262 497 198 0.412 0.398 0.810 0.315 0.380 0.695 0.363 0.389 0.753 0.711 31.0 4.5 35.5 0.99 30.9 4.5 35.4
1975 HOU Cliff Johnson 393 145 340 172 0.369 0.506 0.875 0.312 0.366 0.678 0.340 0.436 0.776 0.691 11.2 23.5 34.7 0.98 11.4 24.0 35.4
2007 HOU Hunter Pence 484 174 456 246 0.360 0.539 0.899 0.329 0.422 0.751 0.344 0.481 0.825 0.753 7.4 26.6 34.0 0.97 7.7 27.7 35.4
2025 HOU Jeremy Pena 543 197 493 235 0.363 0.477 0.839 0.309 0.399 0.708 0.336 0.438 0.774 0.718 14.7 18.8 33.5 0.97 15.5 19.9 35.4
2021 LAN Corey Seager 409 161 353 184 0.394 0.521 0.915 0.317 0.405 0.722 0.355 0.463 0.818 0.723 15.5 20.2 35.7 0.99 15.4 20.0 35.4
1957 PHI Ed Bouchee 674 265 574 270 0.393 0.470 0.864 0.335 0.414 0.749 0.364 0.442 0.807 0.719 19.4 15.7 35.1 0.96 19.6 15.8 35.4
1964 WS2 Don Lock 598 207 512 236 0.346 0.461 0.807 0.302 0.372 0.673 0.324 0.416 0.740 0.693 13.3 22.9 36.2 1.02 13.0 22.4 35.4
2012 ARI Miguel Montero 573 224 486 213 0.391 0.438 0.829 0.313 0.379 0.692 0.352 0.408 0.760 0.715 22.4 15.4 37.8 1.09 20.9 14.4 35.3
1988 BOS Ellis Burks 615 224 540 260 0.364 0.481 0.846 0.317 0.391 0.707 0.341 0.436 0.777 0.712 14.6 24.5 39.1 1.05 13.2 22.1 35.3
1991 BOS Jack Clark 587 219 481 224 0.373 0.466 0.839 0.319 0.390 0.709 0.346 0.428 0.774 0.721 15.7 19.8 35.5 1.01 15.6 19.7 35.3
1972 CAL Bob Oliver 544 167 509 222 0.307 0.436 0.743 0.290 0.335 0.625 0.299 0.385 0.684 0.645 5.0 27.3 32.3 0.95 5.5 29.8 35.3
1967 CAL Jim Fregosi 651 225 590 233 0.346 0.395 0.741 0.292 0.343 0.635 0.319 0.369 0.688 0.651 17.3 15.4 32.7 0.97 18.7 16.6 35.3
1911 CHN Jimmy Sheckard 708 304 539 209 0.429 0.388 0.817 0.342 0.380 0.723 0.386 0.384 0.770 0.682 30.8 2.3 33.1 0.96 32.8 2.5 35.3
1972 CHN Jose Cardenal 593 211 533 242 0.356 0.454 0.810 0.301 0.357 0.659 0.329 0.406 0.734 0.676 16.2 25.6 41.8 1.08 13.7 21.6 35.3
1915 CHN Vic Saier 568 198 496 220 0.349 0.444 0.792 0.306 0.349 0.655 0.328 0.396 0.724 0.630 12.0 23.6 35.6 1.03 11.9 23.4 35.3
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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).