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Showing page 158 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
2022 SFN Joc Pederson 433 153 380 198 0.353 0.521 0.874 0.313 0.385 0.698 0.333 0.453 0.786 0.711 8.8 26.1 35.0 1.00 8.8 26.2 35.1
1972 BOS Reggie Smith 543 198 467 222 0.365 0.475 0.840 0.320 0.362 0.682 0.342 0.419 0.761 0.645 12.2 26.4 38.6 1.04 11.1 23.9 35.0
1963 CHA Pete Ward 667 234 600 289 0.351 0.482 0.832 0.321 0.386 0.707 0.336 0.434 0.770 0.688 10.2 28.5 38.7 1.04 9.2 25.8 35.0
2013 CIN Jay Bruce 697 229 626 299 0.329 0.478 0.806 0.319 0.379 0.698 0.324 0.428 0.752 0.700 3.2 31.9 35.0 0.99 3.2 31.9 35.0
2013 MIL Carlos Gomez 590 199 536 271 0.337 0.506 0.843 0.307 0.391 0.698 0.322 0.448 0.770 0.700 9.0 31.0 39.9 1.06 7.9 27.2 35.0
1928 NY1 Shanty Hogan 470 187 411 196 0.398 0.477 0.875 0.321 0.380 0.701 0.360 0.428 0.788 0.730 18.1 20.1 38.1 1.03 16.6 18.5 35.0
2024 NYN Francisco Lindor 689 237 618 309 0.344 0.500 0.844 0.322 0.416 0.738 0.333 0.458 0.791 0.718 7.6 26.6 34.2 0.94 7.8 27.2 35.0
1991 OAK Harold Baines 566 217 488 231 0.383 0.473 0.857 0.331 0.391 0.722 0.357 0.432 0.789 0.721 15.0 19.6 34.5 0.95 15.2 19.9 35.0
1981 OAK Rickey Henderson 493 201 423 185 0.408 0.437 0.845 0.314 0.381 0.695 0.361 0.409 0.770 0.690 23.0 12.1 35.2 1.01 22.9 12.0 35.0
1918 PHA Tillie Walker 466 164 414 175 0.352 0.423 0.775 0.292 0.306 0.597 0.322 0.364 0.686 0.635 13.9 24.5 38.4 1.06 12.7 22.3 35.0
2009 PHI Raul Ibanez 565 196 500 276 0.347 0.552 0.899 0.338 0.426 0.764 0.343 0.489 0.831 0.735 2.4 32.4 34.8 0.99 2.4 32.6 35.0
1979 PIT Bill Madlock 353 137 311 146 0.388 0.469 0.858 0.316 0.389 0.705 0.352 0.429 0.781 0.705 18.8 17.2 36.0 1.04 18.3 16.7 35.0
1958 PIT Bob Skinner 596 230 529 260 0.386 0.491 0.877 0.344 0.404 0.747 0.365 0.448 0.812 0.729 12.7 23.2 35.9 1.02 12.4 22.6 35.0
1990 SEA Edgar Martinez 572 226 487 211 0.395 0.433 0.828 0.317 0.383 0.700 0.356 0.408 0.764 0.712 22.4 12.5 34.8 0.99 22.5 12.6 35.0
2011 TEX Michael Young 689 262 631 299 0.380 0.474 0.854 0.318 0.409 0.727 0.349 0.441 0.790 0.728 21.5 20.8 42.3 1.08 17.8 17.2 35.0
1996 TEX Rusty Greer 617 245 542 287 0.397 0.530 0.927 0.354 0.438 0.792 0.375 0.484 0.859 0.793 13.2 24.5 37.7 1.05 12.3 22.7 35.0
2007 TOR Frank Thomas 624 235 531 255 0.377 0.480 0.857 0.327 0.417 0.744 0.352 0.449 0.800 0.759 15.6 17.5 33.0 0.99 16.5 18.6 35.0
2000 CLE Jim Thome 684 272 557 296 0.398 0.531 0.929 0.352 0.447 0.798 0.375 0.489 0.864 0.790 15.9 22.7 38.6 1.04 14.4 20.5 34.9
1932 CLE Joe Vosmik 690 257 621 287 0.372 0.462 0.835 0.330 0.389 0.719 0.351 0.426 0.777 0.745 14.5 23.9 38.4 1.04 13.2 21.7 34.9
2012 LAN Andre Ethier 618 217 556 256 0.351 0.460 0.812 0.311 0.385 0.696 0.331 0.423 0.754 0.715 12.5 20.9 33.4 0.99 13.1 21.8 34.9
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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).