Batters



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Showing page 146 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
2005 MIL Geoff Jenkins 618 232 538 276 0.375 0.513 0.888 0.332 0.424 0.756 0.354 0.468 0.822 0.741 13.4 24.5 37.9 0.95 13.0 23.7 36.7
2002 MIL Jose Hernandez 582 207 525 251 0.356 0.478 0.834 0.312 0.397 0.709 0.334 0.438 0.771 0.738 12.8 21.8 34.5 0.95 13.6 23.2 36.7
2000 MIL Richie Sexson 251 100 213 119 0.398 0.559 0.957 0.326 0.420 0.745 0.362 0.489 0.851 0.770 15.3 20.7 36.0 1.00 15.6 21.1 36.7
1940 NY1 Mel Ott 647 261 536 245 0.403 0.457 0.860 0.340 0.395 0.735 0.372 0.426 0.798 0.697 20.4 16.5 36.9 1.01 20.3 16.4 36.7
2001 NYA Bernie Williams 633 250 540 282 0.395 0.522 0.917 0.341 0.434 0.775 0.368 0.478 0.846 0.760 17.1 23.5 40.8 1.08 15.4 21.1 36.7
1988 NYA Don Mattingly 651 230 599 277 0.353 0.462 0.816 0.319 0.376 0.695 0.336 0.419 0.756 0.712 11.1 26.0 37.3 1.05 10.9 25.6 36.7
1984 PIT Lee Lacy 520 184 474 220 0.354 0.464 0.818 0.310 0.372 0.682 0.332 0.418 0.750 0.685 11.6 21.5 33.1 0.94 12.9 23.8 36.7
1942 SLA Vern Stephens 621 212 575 249 0.341 0.433 0.774 0.309 0.339 0.648 0.325 0.386 0.711 0.681 10.1 27.1 37.2 1.00 10.0 26.7 36.7
2005 WAS Nick Johnson 547 223 453 217 0.408 0.479 0.887 0.330 0.417 0.747 0.369 0.448 0.817 0.741 21.2 14.0 35.2 0.98 22.1 14.6 36.7
1946 WS1 Stan Spence 652 235 578 287 0.360 0.497 0.857 0.345 0.384 0.728 0.353 0.440 0.793 0.687 5.0 32.9 37.9 0.98 4.8 31.9 36.7
1943 BOS Bobby Doerr 676 226 604 249 0.334 0.412 0.747 0.303 0.322 0.625 0.318 0.367 0.686 0.657 10.7 27.2 37.8 1.02 10.4 26.3 36.6
1990 BOS Ellis Burks 641 223 588 286 0.348 0.486 0.834 0.320 0.390 0.710 0.334 0.438 0.772 0.712 9.1 28.3 37.4 1.02 8.9 27.7 36.6
1957 BOS Jackie Jensen 627 230 544 255 0.367 0.469 0.836 0.312 0.378 0.690 0.339 0.424 0.763 0.704 17.1 24.7 41.7 1.09 15.0 21.7 36.6
1915 BOS Tris Speaker 654 264 548 225 0.404 0.411 0.814 0.351 0.355 0.706 0.377 0.383 0.760 0.640 17.2 15.3 32.5 0.96 19.4 17.2 36.6
1913 CHN Tommy Leach 538 207 456 192 0.385 0.421 0.806 0.309 0.349 0.658 0.347 0.385 0.732 0.671 20.4 16.4 36.8 1.00 20.3 16.3 36.6
2012 DET Austin Jackson 617 232 543 260 0.376 0.479 0.855 0.315 0.412 0.727 0.345 0.445 0.791 0.729 18.9 18.1 37.0 1.01 18.7 17.9 36.6
1941 DET Rudy York 687 246 590 269 0.358 0.456 0.814 0.316 0.366 0.682 0.337 0.411 0.748 0.726 14.2 26.7 40.8 1.05 12.7 24.0 36.6
1990 HOU Glenn Davis 381 136 327 171 0.357 0.523 0.880 0.297 0.365 0.662 0.327 0.444 0.771 0.700 11.4 25.9 37.3 0.98 11.2 25.4 36.6
2025 MIA Kyle Stowers 457 168 399 217 0.368 0.544 0.911 0.323 0.413 0.737 0.345 0.479 0.824 0.718 10.0 25.9 35.9 0.98 10.2 26.4 36.6
2025 MIL Andrew Vaughn 254 95 221 109 0.374 0.493 0.867 0.309 0.399 0.708 0.341 0.446 0.788 0.718 16.3 19.3 35.5 0.98 16.8 19.9 36.6
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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).