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Showing page 159 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
2008 MIN Joe Mauer 633 261 536 242 0.412 0.451 0.864 0.339 0.420 0.759 0.375 0.436 0.811 0.754 23.3 8.2 31.5 0.97 25.8 9.1 34.9
2017 OAK Khris Davis 652 219 566 299 0.336 0.528 0.864 0.314 0.423 0.737 0.325 0.476 0.800 0.752 7.2 29.4 36.6 1.02 6.9 28.0 34.9
1927 PIT Joe Harris 486 186 411 194 0.383 0.472 0.855 0.319 0.373 0.692 0.351 0.423 0.773 0.714 15.5 20.3 35.8 1.02 15.1 19.8 34.9
1933 PIT Paul Waner 694 253 618 282 0.365 0.456 0.821 0.331 0.391 0.722 0.348 0.423 0.771 0.673 11.6 20.5 32.0 0.99 12.6 22.4 34.9
1996 SFN Matt Williams 455 167 404 206 0.367 0.510 0.877 0.316 0.395 0.710 0.341 0.452 0.794 0.735 11.7 23.4 35.1 1.00 11.6 23.3 34.9
1948 SLA Al Zarilla 596 226 529 255 0.379 0.482 0.861 0.345 0.382 0.727 0.362 0.432 0.794 0.726 10.0 26.7 36.9 1.06 9.5 25.2 34.9
2009 TBA Jason Bartlett 567 219 500 245 0.386 0.490 0.876 0.324 0.415 0.739 0.355 0.453 0.808 0.762 17.5 18.7 36.3 0.97 16.8 18.0 34.9
2021 ANA Jared Walsh 585 199 530 270 0.340 0.509 0.850 0.316 0.402 0.718 0.328 0.456 0.784 0.730 6.9 28.7 35.7 1.02 6.7 28.0 34.8
1971 ATL Ralph Garr 693 251 639 282 0.362 0.441 0.804 0.319 0.369 0.688 0.341 0.405 0.746 0.679 15.0 22.8 37.9 1.02 13.8 20.9 34.8
1995 CAL Chili Davis 522 224 424 218 0.429 0.514 0.943 0.348 0.433 0.782 0.389 0.474 0.863 0.769 20.9 17.3 38.4 1.04 18.9 15.7 34.8
1957 CHN Dale Long 456 175 397 203 0.384 0.511 0.895 0.336 0.414 0.750 0.360 0.463 0.823 0.719 11.6 21.2 32.8 0.96 12.3 22.5 34.8
1995 CHN Sammy Sosa 629 214 564 282 0.340 0.500 0.840 0.313 0.402 0.716 0.327 0.451 0.778 0.735 8.6 27.5 36.1 1.03 8.3 26.5 34.8
1988 CHN Vance Law 621 221 556 229 0.356 0.412 0.768 0.290 0.358 0.648 0.323 0.385 0.708 0.669 20.4 14.7 35.1 1.01 20.2 14.6 34.8
1918 CLE Braggo Roth 450 168 375 154 0.373 0.411 0.784 0.301 0.309 0.610 0.337 0.360 0.697 0.635 16.3 19.4 35.8 1.03 15.8 18.9 34.8
1994 LAN Mike Piazza 441 163 405 219 0.370 0.541 0.910 0.316 0.417 0.733 0.343 0.479 0.822 0.743 11.8 25.3 37.1 0.95 11.1 23.7 34.8
1980 LAN Ron Cey 630 214 551 249 0.340 0.452 0.792 0.301 0.365 0.667 0.321 0.409 0.729 0.691 12.0 23.5 35.6 1.01 11.7 23.0 34.8
2009 MIN Jason Kubel 578 213 514 277 0.369 0.539 0.907 0.338 0.429 0.767 0.353 0.484 0.837 0.762 8.8 28.1 37.0 1.04 8.3 26.4 34.8
1916 NY1 Benny Kauff 632 217 552 225 0.343 0.408 0.751 0.297 0.337 0.633 0.320 0.372 0.692 0.623 14.8 19.1 33.8 1.00 15.2 19.7 34.8
1995 NYN Bobby Bonilla 351 135 317 190 0.385 0.599 0.984 0.349 0.433 0.782 0.367 0.516 0.883 0.735 5.3 29.7 35.1 0.99 5.3 29.4 34.8
2006 PIT Freddy Sanchez 632 238 582 275 0.377 0.473 0.849 0.314 0.417 0.731 0.345 0.445 0.790 0.757 19.8 16.3 36.1 1.03 19.1 15.7 34.8
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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).