Batters



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Showing page 160 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
1997 SFN J. T. Snow 637 246 531 271 0.386 0.510 0.897 0.345 0.425 0.770 0.365 0.468 0.833 0.740 13.3 22.1 35.4 1.02 13.1 21.7 34.8
1991 SFN Robby Thompson 573 198 492 220 0.346 0.447 0.793 0.297 0.371 0.668 0.321 0.409 0.731 0.686 13.9 18.6 32.5 0.99 14.9 19.9 34.8
2001 TEX Ivan Rodriguez 470 163 442 239 0.347 0.541 0.888 0.316 0.420 0.736 0.331 0.480 0.812 0.760 7.2 26.6 33.8 0.98 7.4 27.4 34.8
2002 ARI Junior Spivey 626 243 538 256 0.388 0.476 0.864 0.321 0.412 0.732 0.354 0.444 0.798 0.738 21.3 17.7 38.8 1.05 19.1 15.8 34.7
1914 BLF Jimmy Walsh 459 156 428 195 0.340 0.456 0.795 0.304 0.332 0.637 0.322 0.394 0.716 0.675 8.2 26.6 34.8 1.01 8.2 26.5 34.7
1945 BRO Augie Galan 698 295 576 255 0.423 0.443 0.865 0.357 0.403 0.760 0.390 0.423 0.813 0.691 22.9 11.6 34.5 1.00 23.0 11.7 34.7
1952 BRO Roy Campanella 533 186 468 212 0.349 0.453 0.802 0.304 0.356 0.660 0.326 0.405 0.731 0.693 12.0 22.6 34.6 1.00 12.0 22.7 34.7
1922 CHN Bob O'Farrell 481 207 392 175 0.430 0.446 0.877 0.330 0.393 0.723 0.380 0.420 0.800 0.743 24.3 10.8 35.1 1.00 24.0 10.7 34.7
1992 CIN Reggie Sanders 438 156 385 178 0.356 0.462 0.819 0.297 0.355 0.652 0.326 0.409 0.735 0.679 12.9 20.9 33.9 0.97 13.2 21.4 34.7
1976 DET Ron LeFlore 603 225 544 223 0.373 0.410 0.783 0.308 0.353 0.661 0.340 0.382 0.722 0.677 19.9 15.9 35.7 1.02 19.3 15.5 34.7
1988 MON Hubie Brooks 628 200 588 263 0.318 0.447 0.766 0.292 0.357 0.650 0.305 0.402 0.708 0.669 8.3 26.4 34.6 0.99 8.3 26.5 34.7
1983 NYN Keith Hernandez 389 164 320 139 0.422 0.434 0.856 0.324 0.371 0.695 0.373 0.402 0.775 0.694 23.8 10.1 33.9 0.98 24.4 10.3 34.7
2023 PHI Bryce Harper 546 219 457 228 0.401 0.499 0.900 0.329 0.422 0.751 0.365 0.460 0.825 0.739 19.8 17.8 37.6 1.04 18.3 16.4 34.7
1916 PHI Dode Paskert 625 212 555 223 0.339 0.402 0.741 0.294 0.325 0.619 0.317 0.363 0.680 0.623 14.2 21.5 35.5 1.03 13.9 21.0 34.7
1976 PIT Dave Parker 573 200 537 255 0.349 0.475 0.824 0.328 0.370 0.698 0.339 0.422 0.761 0.677 6.1 28.2 34.3 0.99 6.2 28.5 34.7
2009 PIT Garrett Jones 358 133 314 178 0.372 0.567 0.938 0.324 0.390 0.714 0.348 0.478 0.826 0.735 8.5 28.2 36.8 1.05 8.0 26.6 34.7
1973 PIT Richie Zisk 354 129 333 175 0.364 0.526 0.890 0.310 0.372 0.682 0.337 0.449 0.786 0.694 9.6 25.5 35.2 0.95 9.5 25.1 34.7
2013 SFN Brandon Belt 571 205 509 245 0.359 0.481 0.840 0.319 0.392 0.711 0.339 0.437 0.776 0.700 11.4 22.9 34.4 0.97 11.5 23.1 34.7
1981 SLN George Hendrick 441 157 394 191 0.356 0.485 0.841 0.307 0.359 0.666 0.331 0.422 0.753 0.679 10.8 24.7 35.4 0.95 10.6 24.2 34.7
2025 TEX Corey Seager 445 166 380 185 0.373 0.487 0.860 0.312 0.391 0.703 0.342 0.439 0.781 0.718 13.6 18.6 32.3 0.95 14.6 20.0 34.7
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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).