Batters



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Showing page 145 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
1910 NYA John Knight 472 167 414 173 0.354 0.418 0.772 0.287 0.306 0.593 0.320 0.362 0.682 0.613 15.3 22.4 37.7 1.04 15.0 21.9 36.9
1980 OAK Tony Armas 666 206 628 314 0.309 0.500 0.809 0.312 0.382 0.693 0.310 0.441 0.751 0.727 -0.6 37.5 36.9 1.00 -0.6 37.5 36.9
1968 SFN Jim Ray Hart 535 173 480 213 0.323 0.444 0.767 0.289 0.331 0.620 0.306 0.387 0.693 0.637 9.1 27.1 36.3 0.98 9.3 27.5 36.9
1922 SLA Baby Doll Jacobson 629 232 554 257 0.369 0.464 0.833 0.330 0.389 0.719 0.349 0.426 0.776 0.735 12.3 20.8 33.1 0.96 13.7 23.2 36.9
2023 BOS Rafael Devers 656 230 580 290 0.351 0.500 0.851 0.320 0.404 0.724 0.335 0.452 0.788 0.728 10.0 28.4 38.4 1.07 9.6 27.2 36.8
1914 BRO Zack Wheat 604 221 533 240 0.366 0.450 0.816 0.316 0.343 0.659 0.341 0.397 0.738 0.641 14.9 28.3 43.2 1.06 12.7 24.1 36.8
2004 DET Ivan Rodriguez 575 220 527 269 0.383 0.510 0.893 0.328 0.439 0.768 0.356 0.475 0.830 0.769 15.5 18.9 34.4 0.96 16.6 20.2 36.8
1992 DET Rob Deer 448 151 393 215 0.337 0.547 0.884 0.319 0.386 0.705 0.328 0.466 0.795 0.711 4.1 32.2 36.3 0.99 4.2 32.6 36.8
1986 KCA George Brett 529 212 441 212 0.401 0.481 0.881 0.331 0.400 0.731 0.366 0.440 0.806 0.735 18.4 18.1 36.3 1.00 18.7 18.3 36.8
1972 MIL George Scott 629 201 578 246 0.320 0.426 0.745 0.293 0.331 0.624 0.306 0.378 0.685 0.645 8.4 27.8 36.1 0.99 8.6 28.3 36.8
2018 NYA Aaron Judge 498 195 413 218 0.392 0.528 0.919 0.312 0.414 0.726 0.352 0.471 0.823 0.733 19.8 23.1 43.0 1.06 16.9 19.8 36.8
1940 NYA Charlie Keller 609 249 500 254 0.409 0.508 0.917 0.360 0.421 0.781 0.385 0.464 0.849 0.745 14.8 21.8 36.7 1.01 14.8 21.9 36.8
2021 NYA Giancarlo Stanton 579 205 510 263 0.354 0.516 0.870 0.312 0.418 0.729 0.333 0.467 0.800 0.730 12.2 25.0 37.2 1.01 12.1 24.7 36.8
1950 NYA Phil Rizzuto 735 299 617 271 0.407 0.439 0.846 0.342 0.403 0.745 0.374 0.421 0.796 0.754 23.7 11.6 35.4 0.98 24.6 12.1 36.8
1911 PHA Home Run Baker 660 238 593 299 0.361 0.504 0.865 0.364 0.394 0.757 0.362 0.449 0.811 0.688 0.6 32.9 33.5 0.97 0.7 36.1 36.8
1958 PHI Harry Anderson 585 217 515 270 0.371 0.524 0.895 0.346 0.411 0.756 0.358 0.468 0.826 0.729 7.4 29.3 36.7 1.01 7.4 29.4 36.8
1985 SLN Cesar Cedeno 82 38 76 57 0.463 0.750 1.213 0.320 0.414 0.734 0.392 0.582 0.974 0.689 10.5 24.3 34.8 0.98 11.1 25.7 36.8
2022 TOR Vladimir Guerrero 706 239 638 306 0.339 0.480 0.818 0.309 0.397 0.706 0.324 0.438 0.762 0.700 10.4 26.1 36.7 1.00 10.4 26.2 36.8
1949 CHA Luke Appling 619 269 492 194 0.435 0.394 0.829 0.340 0.364 0.704 0.387 0.379 0.767 0.727 29.3 7.2 36.5 1.00 29.5 7.2 36.7
1911 CHN Heinie Zimmerman 582 196 535 249 0.337 0.465 0.802 0.316 0.352 0.668 0.326 0.409 0.735 0.682 6.1 30.3 36.4 0.99 6.2 30.5 36.7
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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).