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Showing page 176 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
1923 NYA Bob Meusel 503 177 460 220 0.352 0.478 0.830 0.324 0.366 0.691 0.338 0.422 0.760 0.728 6.9 25.8 32.7 0.99 6.9 25.9 32.8
2011 PHI Ryan Howard 644 223 557 272 0.346 0.488 0.835 0.321 0.392 0.713 0.334 0.440 0.774 0.706 8.3 27.5 35.7 1.04 7.6 25.3 32.8
1960 PIT Don Hoak 636 231 553 246 0.363 0.445 0.808 0.309 0.391 0.700 0.336 0.418 0.754 0.704 17.2 14.7 31.9 0.99 17.7 15.1 32.8
1912 PIT Owen Wilson 640 211 582 299 0.330 0.514 0.843 0.343 0.391 0.734 0.336 0.452 0.789 0.700 -4.4 35.8 31.4 0.98 -4.2 37.0 32.8
2006 SEA Raul Ibanez 699 247 626 323 0.353 0.516 0.869 0.337 0.428 0.765 0.345 0.472 0.817 0.775 5.7 27.3 33.0 1.01 5.7 27.1 32.8
2012 SLN David Freese 567 211 501 234 0.372 0.467 0.839 0.313 0.404 0.717 0.343 0.435 0.778 0.715 16.7 16.0 32.7 0.99 16.8 16.0 32.8
1956 SLN Wally Moon 622 242 540 253 0.389 0.469 0.858 0.332 0.407 0.739 0.361 0.438 0.798 0.718 17.9 16.7 34.6 1.04 17.0 15.8 32.8
2008 TBA Evan Longoria 508 174 448 238 0.343 0.531 0.874 0.323 0.409 0.733 0.333 0.470 0.803 0.754 4.9 27.3 32.2 0.99 5.0 27.8 32.8
2022 ANA Taylor Ward 564 203 495 234 0.360 0.473 0.833 0.308 0.397 0.705 0.334 0.435 0.769 0.700 14.5 19.1 33.6 1.02 14.1 18.6 32.7
1978 CAL Don Baylor 677 225 591 279 0.332 0.472 0.804 0.311 0.374 0.684 0.321 0.423 0.744 0.707 7.3 28.7 36.2 1.03 6.6 25.9 32.7
1915 CHF Dutch Zwilling 633 228 546 244 0.360 0.447 0.807 0.329 0.360 0.689 0.344 0.403 0.748 0.651 9.8 24.1 34.0 1.02 9.4 23.2 32.7
2012 COL Carlos Gonzalez 579 215 518 264 0.371 0.510 0.881 0.312 0.385 0.697 0.342 0.447 0.789 0.715 17.1 32.6 49.7 1.14 11.3 21.4 32.7
1993 COL Dante Bichette 581 202 538 283 0.348 0.526 0.874 0.306 0.387 0.693 0.327 0.457 0.783 0.722 12.2 37.0 49.2 1.13 8.1 24.6 32.7
1993 DET Alan Trammell 447 172 401 199 0.385 0.496 0.881 0.327 0.402 0.730 0.356 0.449 0.805 0.742 12.8 19.2 32.0 0.98 13.1 19.6 32.7
2015 KCA Lorenzo Cain 604 218 551 263 0.361 0.477 0.838 0.310 0.412 0.722 0.335 0.445 0.780 0.728 15.4 18.0 33.4 1.01 15.1 17.6 32.7
2024 LAN Mookie Betts 516 192 450 221 0.372 0.491 0.863 0.314 0.413 0.728 0.343 0.452 0.795 0.718 14.9 17.9 32.9 1.01 14.8 17.8 32.7
1961 MIN Earl Battey 522 195 460 216 0.374 0.470 0.843 0.318 0.376 0.693 0.346 0.423 0.768 0.720 14.6 21.7 36.3 1.06 13.2 19.5 32.7
2014 OAK Josh Donaldson 695 238 608 277 0.342 0.456 0.798 0.309 0.392 0.700 0.325 0.424 0.749 0.704 11.9 19.0 30.9 1.01 12.6 20.1 32.7
1929 PIT Pie Traynor 596 224 540 255 0.376 0.472 0.848 0.332 0.395 0.727 0.354 0.434 0.788 0.773 13.1 21.0 34.1 1.03 12.6 20.1 32.7
1977 SDN Gene Tenace 581 240 437 179 0.413 0.410 0.823 0.314 0.389 0.702 0.363 0.399 0.763 0.721 28.8 5.0 33.8 0.96 27.9 4.8 32.7
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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).