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Showing page 156 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
2010 CIN Scott Rolen 537 192 471 234 0.358 0.497 0.854 0.315 0.392 0.707 0.336 0.444 0.781 0.720 11.4 25.0 36.3 1.02 11.1 24.3 35.3
1918 CIN Sherry Magee 459 165 403 166 0.359 0.412 0.771 0.290 0.315 0.605 0.325 0.364 0.688 0.629 15.9 19.5 35.4 1.01 15.9 19.4 35.3
2003 COL Jay Payton 658 231 600 307 0.351 0.512 0.863 0.320 0.415 0.734 0.335 0.463 0.798 0.745 10.4 29.4 39.8 1.07 9.2 26.1 35.3
2004 DET Carlos Guillen 583 220 522 283 0.377 0.542 0.920 0.345 0.442 0.787 0.361 0.492 0.853 0.769 9.3 26.2 35.4 0.97 9.3 26.1 35.3
1939 DET Charlie Gehringer 487 202 406 221 0.415 0.544 0.959 0.359 0.425 0.784 0.387 0.485 0.872 0.752 13.7 23.5 37.4 1.05 12.9 22.2 35.3
2014 LAN Adrian Gonzalez 660 221 591 285 0.335 0.482 0.817 0.316 0.383 0.699 0.325 0.433 0.758 0.691 6.2 29.8 36.0 0.99 6.1 29.2 35.3
2022 MIN Carlos Correa 590 216 522 244 0.366 0.467 0.834 0.310 0.398 0.709 0.338 0.433 0.771 0.700 16.6 17.9 34.4 0.99 17.0 18.4 35.3
2019 MIN Mitch Garver 359 131 311 196 0.365 0.630 0.995 0.324 0.454 0.778 0.345 0.542 0.887 0.761 7.4 27.5 34.8 0.99 7.5 27.9 35.3
1977 MON Ellis Valentine 541 179 508 256 0.331 0.504 0.835 0.308 0.388 0.696 0.320 0.446 0.766 0.721 6.2 29.5 35.7 1.01 6.1 29.2 35.3
1912 NY1 Larry Doyle 629 242 559 263 0.385 0.470 0.855 0.347 0.388 0.736 0.366 0.429 0.795 0.700 11.7 23.0 34.8 0.97 11.9 23.3 35.3
1993 SEA Jay Buhner 675 255 563 268 0.378 0.476 0.854 0.329 0.411 0.741 0.354 0.444 0.797 0.742 16.3 17.9 34.3 0.99 16.8 18.4 35.3
2011 SFN Pablo Sandoval 466 166 426 235 0.356 0.552 0.908 0.330 0.412 0.742 0.343 0.482 0.825 0.706 6.2 29.7 35.9 0.91 6.1 29.2 35.3
1959 SFN Willie McCovey 219 94 192 126 0.429 0.656 1.085 0.335 0.410 0.745 0.382 0.533 0.915 0.721 10.3 23.5 33.9 0.95 10.7 24.5 35.3
2009 ANA Torii Hunter 506 185 451 229 0.366 0.508 0.873 0.316 0.397 0.713 0.341 0.452 0.793 0.762 12.6 24.6 37.1 1.05 12.0 23.3 35.2
1945 BOS Bob Johnson 593 212 529 225 0.358 0.425 0.783 0.309 0.335 0.644 0.333 0.380 0.714 0.665 14.3 23.8 38.1 1.04 13.2 22.0 35.2
1949 BOS Dom DiMaggio 706 284 605 254 0.402 0.420 0.822 0.344 0.366 0.710 0.373 0.393 0.766 0.727 20.7 16.1 36.8 1.02 19.8 15.4 35.2
1947 BRO Pee Wee Reese 589 241 476 203 0.409 0.426 0.836 0.321 0.380 0.701 0.365 0.403 0.768 0.724 25.9 11.1 37.0 1.04 24.6 10.6 35.2
1996 CAL Jim Edmonds 483 181 431 246 0.375 0.571 0.946 0.352 0.433 0.785 0.363 0.502 0.865 0.793 5.6 29.9 35.5 1.01 5.6 29.6 35.2
1982 CHA Greg Luzinski 686 265 583 263 0.386 0.451 0.837 0.322 0.405 0.727 0.354 0.428 0.782 0.727 22.1 13.6 35.8 1.01 21.7 13.4 35.2
1980 CHN Bill Buckner 615 217 578 264 0.353 0.457 0.810 0.323 0.366 0.689 0.338 0.412 0.749 0.691 9.3 26.0 35.4 0.99 9.2 25.9 35.2
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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).