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Showing page 154 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
1926 CIN Bubbles Hargrave 366 145 326 171 0.396 0.525 0.921 0.326 0.378 0.704 0.361 0.451 0.812 0.713 12.8 23.5 36.3 0.95 12.6 23.0 35.6
1993 MIL Greg Vaughn 667 246 569 274 0.369 0.482 0.850 0.326 0.406 0.732 0.347 0.444 0.791 0.742 14.3 21.5 35.9 1.01 14.2 21.3 35.6
1933 NYA Ben Chapman 654 253 567 247 0.387 0.436 0.822 0.329 0.378 0.707 0.358 0.407 0.765 0.727 18.9 16.5 35.4 0.92 19.0 16.6 35.6
1978 PHI Mike Schmidt 616 224 513 223 0.364 0.435 0.798 0.305 0.364 0.669 0.334 0.399 0.733 0.688 18.2 18.5 36.8 1.03 17.6 17.9 35.6
1959 SLN Joe Cunningham 557 252 458 219 0.452 0.478 0.931 0.339 0.412 0.752 0.396 0.445 0.841 0.721 31.5 13.8 45.4 1.09 24.7 10.8 35.6
2000 SLN Will Clark 197 84 171 112 0.426 0.655 1.081 0.357 0.436 0.793 0.391 0.546 0.937 0.770 7.3 27.3 34.6 1.01 7.5 28.1 35.6
2016 TBA Evan Longoria 685 218 633 330 0.318 0.521 0.840 0.311 0.416 0.727 0.315 0.469 0.783 0.743 2.3 33.9 36.3 0.99 2.3 33.2 35.6
2010 TEX Vladimir Guerrero 643 222 593 294 0.345 0.496 0.841 0.316 0.405 0.721 0.330 0.451 0.781 0.732 9.4 27.2 36.6 1.01 9.1 26.5 35.6
2007 TOR Alex Rios 711 252 643 320 0.354 0.498 0.852 0.327 0.418 0.746 0.341 0.458 0.799 0.759 9.5 25.8 35.3 0.99 9.6 26.0 35.6
1911 WS1 Germany Schaefer 526 209 442 174 0.397 0.394 0.791 0.312 0.337 0.649 0.355 0.366 0.720 0.688 22.5 12.6 35.1 0.99 22.8 12.8 35.6
2025 ATH Shea Langeliers 523 170 481 258 0.325 0.536 0.861 0.309 0.403 0.712 0.317 0.470 0.787 0.718 4.2 31.7 35.9 1.04 4.2 31.3 35.5
1988 BAL Eddie Murray 681 246 603 286 0.361 0.474 0.836 0.328 0.395 0.723 0.345 0.435 0.779 0.712 11.2 23.9 35.2 0.99 11.3 24.1 35.5
1945 BRO Eddie Stanky 725 295 554 185 0.407 0.334 0.741 0.308 0.340 0.648 0.358 0.337 0.695 0.691 35.7 -1.2 34.6 0.98 36.6 -1.2 35.5
2021 COL C. J. Cron 547 205 470 249 0.375 0.530 0.905 0.309 0.407 0.716 0.342 0.468 0.810 0.723 18.1 28.9 47.0 1.11 13.7 21.8 35.5
2005 KCA Mike Sweeney 514 178 470 243 0.346 0.517 0.863 0.310 0.407 0.717 0.328 0.462 0.790 0.753 9.3 26.2 35.4 1.00 9.3 26.3 35.5
1970 MIN Cesar Tovar 726 255 650 287 0.351 0.442 0.793 0.311 0.379 0.690 0.331 0.410 0.742 0.697 14.5 20.5 34.9 0.97 14.7 20.9 35.5
1984 MON Tim Raines 718 281 622 272 0.391 0.437 0.829 0.332 0.385 0.717 0.362 0.411 0.773 0.685 21.3 16.1 37.4 0.96 20.2 15.3 35.5
1944 NY1 Phil Weintraub 423 173 361 189 0.409 0.524 0.933 0.349 0.412 0.762 0.379 0.468 0.847 0.683 12.6 22.4 35.1 0.99 12.7 22.7 35.5
1979 SFN Jack Clark 598 208 527 251 0.348 0.476 0.824 0.312 0.387 0.699 0.330 0.432 0.762 0.705 10.8 23.7 34.5 0.93 11.1 24.4 35.5
2010 SFN Pat Burrell 341 124 289 147 0.364 0.509 0.872 0.309 0.386 0.695 0.336 0.447 0.784 0.720 12.0 23.8 35.7 1.01 11.9 23.7 35.5
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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).