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Showing page 162 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
2007 MIL Corey Hart 566 198 505 272 0.350 0.539 0.888 0.327 0.429 0.756 0.339 0.484 0.822 0.753 6.4 28.1 34.4 1.00 6.4 28.2 34.5
1995 MIL John Jaha 357 139 316 183 0.389 0.579 0.968 0.330 0.423 0.753 0.360 0.501 0.861 0.769 10.5 24.7 35.2 1.02 10.3 24.2 34.5
1939 PHI Morrie Arnovich 561 220 491 204 0.392 0.415 0.808 0.313 0.364 0.677 0.353 0.390 0.742 0.713 22.1 12.7 34.8 1.01 21.9 12.6 34.5
1985 SDN Carmelo Martinez 610 220 514 223 0.361 0.434 0.795 0.307 0.369 0.676 0.334 0.401 0.735 0.689 16.4 16.4 32.9 0.98 17.2 17.2 34.5
1983 SFN Joel Youngblood 415 147 373 186 0.354 0.499 0.853 0.307 0.366 0.673 0.330 0.432 0.763 0.694 9.9 24.8 34.7 1.01 9.8 24.7 34.5
1929 SLA Heinie Manush 638 249 576 287 0.390 0.498 0.889 0.353 0.421 0.774 0.372 0.460 0.831 0.747 11.8 22.6 34.4 1.00 11.8 22.7 34.5
2012 WAS Ian Desmond 547 183 513 262 0.335 0.511 0.845 0.309 0.401 0.710 0.322 0.456 0.778 0.715 6.8 27.7 34.5 1.01 6.8 27.7 34.5
1914 BRO Casey Stengel 484 192 412 176 0.397 0.427 0.824 0.318 0.346 0.665 0.357 0.387 0.744 0.641 19.0 16.0 35.0 1.06 18.7 15.7 34.4
1921 BSN Tony Boeckel 663 239 592 261 0.360 0.441 0.801 0.315 0.382 0.697 0.338 0.411 0.749 0.726 15.0 17.5 32.4 0.96 15.9 18.6 34.4
1926 CHA Bibb Falk 658 264 566 270 0.401 0.477 0.878 0.349 0.422 0.772 0.375 0.450 0.825 0.730 17.0 16.0 33.0 0.94 17.7 16.7 34.4
1965 CHN Ernie Banks 680 223 612 277 0.328 0.453 0.781 0.302 0.369 0.671 0.315 0.411 0.726 0.681 8.8 25.9 34.7 1.00 8.7 25.7 34.4
1968 CIN Alex Johnson 634 217 603 238 0.342 0.395 0.737 0.286 0.332 0.618 0.314 0.363 0.677 0.637 17.7 19.2 36.9 1.05 16.5 17.9 34.4
1974 HOU Bob Watson 593 219 524 216 0.369 0.412 0.782 0.306 0.358 0.663 0.338 0.385 0.722 0.688 18.8 14.5 33.3 0.95 19.4 15.0 34.4
2015 KCA Eric Hosmer 667 242 599 275 0.363 0.459 0.822 0.313 0.394 0.707 0.338 0.427 0.765 0.728 16.6 19.8 36.5 1.03 15.6 18.7 34.4
1999 KCA Jermaine Dye 673 238 608 320 0.354 0.526 0.880 0.336 0.434 0.770 0.345 0.480 0.825 0.784 5.7 27.5 33.3 0.99 5.9 28.4 34.4
2025 LAN Will Smith 436 176 362 180 0.404 0.497 0.901 0.308 0.410 0.717 0.356 0.453 0.809 0.718 21.0 16.3 37.3 1.06 19.4 15.0 34.4
1980 MON Ellis Valentine 343 126 311 163 0.367 0.524 0.891 0.304 0.370 0.674 0.336 0.447 0.782 0.691 10.8 23.9 34.7 0.98 10.7 23.7 34.4
1996 NYA Bernie Williams 641 250 551 295 0.390 0.535 0.925 0.357 0.454 0.811 0.373 0.495 0.868 0.793 10.8 22.7 33.5 0.99 11.1 23.3 34.4
1920 NYA Del Pratt 654 233 574 246 0.356 0.429 0.785 0.315 0.356 0.671 0.336 0.392 0.728 0.723 13.4 21.0 34.4 1.00 13.4 21.0 34.4
2006 NYN Carlos Delgado 618 223 524 287 0.361 0.548 0.909 0.341 0.440 0.781 0.351 0.494 0.845 0.757 6.0 27.9 33.9 0.98 6.1 28.3 34.4
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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).