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Showing page 148 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
1983 KCA Willie Aikens 458 171 410 221 0.373 0.539 0.912 0.335 0.402 0.737 0.354 0.470 0.825 0.726 8.9 28.1 37.0 1.01 8.8 27.7 36.5
1989 MIL Paul Molitor 696 262 615 270 0.376 0.439 0.815 0.319 0.385 0.704 0.348 0.412 0.760 0.707 19.8 16.9 36.8 1.01 19.6 16.8 36.5
1920 NY1 George Burns 714 260 631 253 0.364 0.401 0.765 0.306 0.351 0.657 0.335 0.376 0.711 0.670 20.6 16.1 36.7 1.01 20.5 16.0 36.5
1910 NY1 Larry Doyle 670 240 574 236 0.358 0.411 0.769 0.322 0.338 0.660 0.340 0.374 0.715 0.657 12.7 24.1 36.9 0.99 12.6 23.8 36.5
1919 NY1 Ross Youngs 561 210 490 203 0.374 0.414 0.789 0.314 0.350 0.664 0.344 0.382 0.726 0.639 16.9 15.8 32.6 0.96 18.9 17.7 36.5
2018 NYA Giancarlo Stanton 705 242 617 314 0.343 0.509 0.852 0.317 0.423 0.740 0.330 0.466 0.796 0.733 9.2 26.8 36.0 1.05 9.3 27.2 36.5
2017 PIT Andrew McCutchen 650 236 570 277 0.363 0.486 0.849 0.314 0.417 0.731 0.338 0.452 0.790 0.746 16.1 20.0 36.1 1.00 16.3 20.2 36.5
2000 PIT Jason Kendall 678 279 579 272 0.412 0.470 0.881 0.332 0.437 0.769 0.372 0.453 0.825 0.770 26.8 9.7 36.6 0.99 26.7 9.7 36.5
1997 SDN Ken Caminiti 576 224 486 247 0.389 0.508 0.897 0.343 0.424 0.767 0.366 0.466 0.832 0.740 13.2 20.4 33.6 0.96 14.3 22.2 36.5
1916 SLA Del Pratt 671 217 597 233 0.323 0.390 0.714 0.293 0.310 0.603 0.308 0.350 0.659 0.635 10.0 24.0 33.9 0.98 10.8 25.8 36.5
2010 BAL Luke Scott 517 190 447 239 0.368 0.535 0.902 0.327 0.401 0.728 0.347 0.468 0.815 0.732 10.4 30.2 40.6 1.04 9.3 27.1 36.4
1961 KC1 Norm Siebern 648 248 560 265 0.383 0.473 0.856 0.330 0.405 0.736 0.357 0.439 0.796 0.720 16.9 19.8 36.6 1.04 16.8 19.7 36.4
1975 OAK Joe Rudi 515 173 468 231 0.336 0.494 0.830 0.313 0.371 0.685 0.325 0.432 0.757 0.703 5.8 28.6 34.4 0.95 6.1 30.3 36.4
1914 PHI Beals Becker 563 206 515 230 0.366 0.447 0.812 0.325 0.350 0.675 0.345 0.398 0.744 0.641 11.5 25.2 36.7 1.01 11.4 25.0 36.4
1933 PIT Freddie Lindstrom 596 200 538 241 0.336 0.448 0.784 0.303 0.346 0.649 0.320 0.397 0.717 0.673 9.5 27.2 36.6 1.01 9.4 27.1 36.4
2025 SDN Fernando Tatis 691 254 594 265 0.368 0.446 0.814 0.308 0.398 0.706 0.338 0.422 0.760 0.718 20.3 13.7 34.0 0.98 21.7 14.7 36.4
2025 WAS James Wood 689 241 598 284 0.350 0.475 0.825 0.314 0.396 0.710 0.332 0.435 0.767 0.718 12.4 23.6 36.0 0.97 12.5 23.9 36.4
1936 WS1 John Stone 500 209 437 238 0.418 0.545 0.963 0.368 0.445 0.813 0.393 0.495 0.888 0.779 12.5 21.4 33.9 0.98 13.4 23.0 36.4
1957 BRO Duke Snider 592 217 508 298 0.367 0.587 0.953 0.346 0.434 0.781 0.356 0.510 0.867 0.719 6.0 38.7 44.8 1.09 4.9 31.3 36.3
1934 CHN Gabby Hartnett 487 171 438 220 0.351 0.502 0.853 0.315 0.377 0.692 0.333 0.440 0.773 0.722 8.9 27.7 36.6 1.00 8.8 27.5 36.3
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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).