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Showing page 157 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
1968 CHN Ron Santo 682 241 577 243 0.353 0.421 0.775 0.287 0.329 0.615 0.320 0.375 0.695 0.637 23.0 26.2 49.1 1.08 16.5 18.8 35.2
1971 CIN Tony Perez 664 216 609 267 0.325 0.438 0.764 0.299 0.352 0.651 0.312 0.395 0.707 0.679 8.7 25.7 34.5 0.99 8.9 26.2 35.2
1924 CLE Tris Speaker 576 243 487 248 0.422 0.509 0.931 0.366 0.427 0.793 0.394 0.468 0.862 0.743 16.2 20.2 36.4 1.02 15.7 19.5 35.2
2023 MIA Luis Arraez 617 242 574 269 0.392 0.469 0.861 0.324 0.415 0.740 0.358 0.442 0.800 0.739 21.0 15.5 36.5 1.03 20.3 14.9 35.2
1982 PHI Gary Matthews 690 241 616 263 0.349 0.427 0.776 0.303 0.373 0.676 0.326 0.400 0.726 0.688 15.9 16.0 31.9 0.96 17.5 17.7 35.2
1924 SLN Ray Blades 514 187 456 222 0.364 0.487 0.851 0.319 0.382 0.701 0.341 0.434 0.776 0.721 11.5 23.8 35.3 1.01 11.5 23.7 35.2
1992 TEX Juan Gonzalez 632 192 584 309 0.304 0.529 0.833 0.321 0.390 0.711 0.312 0.460 0.772 0.711 -5.3 40.3 35.0 1.00 -5.3 40.5 35.2
1996 ATL Marquis Grissom 723 251 671 328 0.347 0.489 0.836 0.320 0.413 0.733 0.333 0.451 0.784 0.735 10.0 25.5 35.5 1.01 9.9 25.2 35.1
1914 BSN Red Smith 251 95 207 93 0.378 0.449 0.828 0.298 0.322 0.620 0.338 0.386 0.724 0.641 16.1 19.5 35.6 1.01 15.9 19.2 35.1
1941 CHA Luke Appling 678 270 592 231 0.398 0.390 0.788 0.319 0.368 0.687 0.359 0.379 0.738 0.726 26.9 6.1 33.0 0.93 28.6 6.5 35.1
2008 CIN Joey Votto 589 217 526 266 0.368 0.506 0.874 0.332 0.415 0.746 0.350 0.460 0.810 0.740 10.9 24.3 35.1 1.00 10.9 24.3 35.1
2018 DET Nick Castellanos 678 240 620 310 0.354 0.500 0.854 0.317 0.425 0.742 0.335 0.463 0.798 0.733 12.7 23.1 35.7 1.01 12.5 22.7 35.1
1977 DET Ron LeFlore 698 253 652 310 0.362 0.475 0.838 0.321 0.405 0.725 0.342 0.440 0.782 0.732 14.7 22.8 37.5 1.03 13.8 21.3 35.1
2002 FLO Kevin Millar 489 179 438 223 0.366 0.509 0.875 0.311 0.398 0.709 0.338 0.454 0.792 0.738 13.6 23.9 37.6 1.03 12.7 22.3 35.1
1997 MIL Jeromy Burnitz 577 219 494 273 0.380 0.553 0.932 0.347 0.440 0.787 0.363 0.496 0.859 0.766 9.5 28.3 37.7 1.04 8.8 26.4 35.1
1951 NYA Gil McDougald 473 183 402 196 0.387 0.488 0.874 0.333 0.369 0.702 0.360 0.428 0.788 0.719 12.8 23.6 36.4 0.92 12.3 22.8 35.1
2015 NYA Mark Teixeira 462 165 392 215 0.357 0.548 0.906 0.320 0.412 0.732 0.338 0.480 0.819 0.728 8.6 27.1 35.8 1.02 8.4 26.6 35.1
1983 OAK Rickey Henderson 622 257 513 216 0.413 0.421 0.834 0.318 0.401 0.719 0.366 0.411 0.776 0.726 29.7 5.2 34.9 0.97 29.9 5.2 35.1
1952 PHA Eddie Joost 669 259 540 224 0.387 0.415 0.802 0.312 0.348 0.660 0.350 0.381 0.731 0.690 25.1 18.5 43.4 1.06 20.3 15.0 35.1
2004 SEA Ichiro Suzuki 762 315 704 320 0.413 0.455 0.868 0.341 0.425 0.766 0.377 0.440 0.817 0.769 27.6 9.9 37.6 0.98 25.8 9.2 35.1
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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).