Batters



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Showing page 163 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
2021 NYN Pete Alonso 637 219 561 291 0.344 0.519 0.863 0.315 0.414 0.730 0.330 0.466 0.796 0.723 8.9 29.2 38.1 0.92 8.0 26.4 34.4
1946 PHA Barney McCosky 362 151 308 136 0.417 0.442 0.859 0.337 0.382 0.719 0.377 0.412 0.789 0.687 18.5 14.3 32.9 0.98 19.3 15.0 34.4
1919 PHA George Burns 513 172 475 211 0.335 0.444 0.779 0.297 0.334 0.631 0.316 0.389 0.705 0.681 9.8 26.4 36.2 1.06 9.3 25.1 34.4
1953 PHI Del Ennis 643 227 578 280 0.353 0.484 0.837 0.324 0.401 0.725 0.338 0.443 0.781 0.743 9.3 24.1 33.3 0.99 9.6 24.9 34.4
1939 PIT Elbie Fletcher 429 162 370 179 0.378 0.484 0.861 0.340 0.406 0.746 0.359 0.445 0.804 0.713 8.9 24.4 33.2 0.97 9.2 25.3 34.4
1946 PIT Ralph Kiner 579 199 502 216 0.344 0.430 0.774 0.306 0.332 0.638 0.325 0.381 0.706 0.678 10.9 24.1 34.9 1.06 10.7 23.8 34.4
1998 TEX Ivan Rodriguez 617 221 579 297 0.358 0.513 0.871 0.326 0.425 0.751 0.342 0.469 0.811 0.769 9.9 25.5 35.4 1.03 9.6 24.8 34.4
1913 BOS Clyde Engle 569 201 497 190 0.353 0.382 0.736 0.299 0.317 0.616 0.326 0.350 0.676 0.652 17.9 17.6 35.5 1.02 17.3 17.0 34.3
2001 CHA Paul Konerko 650 227 582 295 0.349 0.507 0.856 0.323 0.419 0.741 0.336 0.463 0.799 0.760 8.7 26.2 34.9 1.01 8.6 25.7 34.3
1962 CHN Ernie Banks 657 201 610 307 0.306 0.503 0.809 0.311 0.379 0.690 0.308 0.441 0.749 0.716 -1.5 38.1 36.4 1.08 -1.6 36.1 34.3
1984 CLE Andre Thornton 690 252 587 284 0.365 0.484 0.849 0.314 0.391 0.706 0.340 0.438 0.777 0.722 17.5 27.7 45.2 1.09 13.3 21.0 34.3
1966 CLE Leon Wagner 599 200 549 242 0.334 0.441 0.775 0.301 0.369 0.670 0.318 0.405 0.722 0.671 9.7 19.9 29.7 0.95 11.2 23.0 34.3
1993 DET Cecil Fielder 672 247 573 266 0.368 0.464 0.832 0.324 0.405 0.729 0.346 0.435 0.781 0.742 14.4 17.6 32.1 0.98 15.4 18.8 34.3
1977 DET Jason Thompson 668 232 585 285 0.347 0.487 0.834 0.327 0.392 0.720 0.337 0.440 0.777 0.732 6.6 28.0 34.6 1.01 6.5 27.8 34.3
2002 FLO Cliff Floyd 362 150 296 159 0.414 0.537 0.952 0.347 0.431 0.778 0.381 0.484 0.865 0.738 16.8 16.6 33.4 0.94 17.3 17.0 34.3
2012 MIA Justin Ruggiano 320 119 288 154 0.372 0.535 0.907 0.297 0.381 0.678 0.334 0.458 0.792 0.715 12.0 22.3 34.3 1.00 12.0 22.3 34.3
1986 MON Andre Dawson 546 184 496 237 0.337 0.478 0.815 0.304 0.377 0.681 0.321 0.427 0.748 0.698 9.0 25.0 34.0 0.99 9.1 25.2 34.3
1953 NY1 Hank Thompson 454 181 388 220 0.399 0.567 0.966 0.354 0.448 0.802 0.377 0.507 0.884 0.743 10.1 23.2 33.3 0.98 10.4 23.9 34.3
1952 NYA Yogi Berra 603 214 534 255 0.355 0.478 0.832 0.330 0.377 0.706 0.342 0.427 0.769 0.690 7.5 27.4 34.9 1.01 7.4 26.9 34.3
1937 PHI Pinky Whitney 541 209 487 217 0.386 0.446 0.832 0.314 0.371 0.685 0.350 0.408 0.758 0.709 19.5 18.3 37.8 1.07 17.7 16.6 34.3
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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).