Batters



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Showing page 173 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
1971 MON Ron Hunt 638 253 520 186 0.397 0.358 0.754 0.301 0.354 0.656 0.349 0.356 0.705 0.679 30.3 1.1 31.4 0.98 31.9 1.2 33.1
2025 NYA Giancarlo Stanton 281 98 249 148 0.349 0.594 0.943 0.301 0.387 0.689 0.325 0.491 0.816 0.718 6.7 25.4 32.1 0.97 6.9 26.2 33.1
1993 NYN Bobby Bonilla 582 205 502 262 0.352 0.522 0.874 0.336 0.410 0.746 0.344 0.466 0.810 0.722 4.7 28.1 32.8 0.99 4.7 28.4 33.1
1952 PHA Gus Zernial 621 215 549 248 0.346 0.452 0.798 0.313 0.350 0.664 0.330 0.401 0.731 0.690 10.2 28.1 38.4 1.06 8.8 24.2 33.1
1910 PHI Johnny Bates 583 218 499 209 0.374 0.419 0.793 0.325 0.345 0.670 0.349 0.382 0.731 0.657 14.3 18.4 32.8 0.99 14.4 18.6 33.1
1960 PIT Roberto Clemente 620 220 570 261 0.355 0.458 0.813 0.310 0.392 0.702 0.332 0.425 0.758 0.704 13.9 19.2 33.1 1.00 13.9 19.2 33.1
2000 SDN Ryan Klesko 590 232 494 255 0.393 0.516 0.909 0.348 0.445 0.793 0.371 0.481 0.851 0.770 13.3 18.5 31.9 0.92 13.8 19.2 33.1
1984 SFN Jack Clark 249 108 203 109 0.434 0.537 0.971 0.307 0.361 0.668 0.370 0.449 0.819 0.685 15.7 17.5 33.3 1.00 15.6 17.4 33.1
1934 WS1 Heinie Manush 604 232 556 291 0.384 0.523 0.907 0.364 0.423 0.786 0.374 0.473 0.847 0.744 6.2 27.8 34.1 1.02 6.0 27.0 33.1
1967 ATL Joe Torre 534 184 477 212 0.345 0.444 0.789 0.296 0.360 0.655 0.320 0.402 0.722 0.669 13.0 20.2 33.1 1.02 13.0 20.1 33.0
1996 ATL Ryan Klesko 605 219 528 280 0.362 0.530 0.892 0.339 0.419 0.757 0.350 0.474 0.825 0.735 7.0 29.9 36.8 1.05 6.3 26.8 33.0
1946 BOS Bobby Doerr 659 225 583 265 0.341 0.455 0.796 0.305 0.340 0.645 0.323 0.397 0.721 0.687 11.7 33.9 45.7 1.12 8.4 24.5 33.0
1949 BRO Duke Snider 615 221 552 272 0.359 0.493 0.852 0.342 0.396 0.738 0.351 0.444 0.795 0.719 5.2 27.2 32.4 1.02 5.3 27.7 33.0
1934 BRO Len Koenecke 536 218 460 234 0.407 0.509 0.915 0.356 0.431 0.787 0.381 0.470 0.851 0.722 13.6 18.5 32.1 0.97 14.0 19.0 33.0
1970 CHA Bill Melton 589 200 514 251 0.340 0.488 0.828 0.312 0.377 0.689 0.326 0.433 0.759 0.697 8.1 28.4 36.4 1.06 7.3 25.8 33.0
1995 CHA Robin Ventura 577 221 492 245 0.383 0.498 0.881 0.340 0.410 0.750 0.362 0.454 0.816 0.769 12.5 21.2 33.6 0.98 12.3 20.8 33.0
1949 CHN Andy Pafko 592 218 519 233 0.368 0.449 0.817 0.320 0.380 0.700 0.344 0.415 0.759 0.719 14.5 17.9 32.4 0.99 14.8 18.2 33.0
1923 CHN Arnold Statz 721 268 654 288 0.372 0.440 0.812 0.324 0.377 0.701 0.348 0.408 0.756 0.730 17.2 20.6 37.8 1.08 15.0 18.0 33.0
1983 CHN Jody Davis 550 173 510 245 0.315 0.480 0.795 0.303 0.364 0.666 0.309 0.422 0.731 0.694 3.4 29.7 33.0 1.02 3.4 29.7 33.0
1952 CLE Bobby Ávila 684 247 597 248 0.361 0.415 0.777 0.317 0.357 0.674 0.339 0.386 0.725 0.690 15.3 17.2 32.6 0.95 15.5 17.4 33.0
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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).