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Showing page 166 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
1939 CIN Ival Goodman 553 213 470 242 0.385 0.515 0.900 0.347 0.413 0.760 0.366 0.464 0.830 0.713 10.4 24.0 34.4 1.01 10.3 23.7 34.0
2013 LAN Adrian Gonzalez 641 219 583 269 0.342 0.461 0.803 0.310 0.385 0.695 0.326 0.423 0.749 0.700 10.1 21.9 32.0 0.97 10.7 23.3 34.0
1964 NYA Roger Maris 584 212 513 238 0.363 0.464 0.827 0.319 0.385 0.703 0.341 0.424 0.765 0.693 12.9 21.0 33.9 1.04 12.9 21.1 34.0
1999 NYN Rickey Henderson 526 222 438 204 0.422 0.466 0.888 0.328 0.420 0.748 0.375 0.443 0.818 0.768 24.8 10.6 35.4 0.98 23.8 10.2 34.0
1984 OAK Dave Kingman 613 197 549 277 0.321 0.505 0.826 0.314 0.394 0.708 0.318 0.449 0.767 0.722 2.2 30.5 32.6 0.97 2.3 31.8 34.0
1968 OAK Rick Monday 563 208 482 194 0.369 0.402 0.772 0.303 0.343 0.646 0.336 0.373 0.709 0.633 18.5 14.1 32.6 0.98 19.3 14.7 34.0
1990 PIT Bobby Bonilla 686 221 625 324 0.322 0.518 0.841 0.332 0.398 0.731 0.327 0.458 0.786 0.700 -3.4 37.6 34.2 0.99 -3.4 37.4 34.0
1936 PIT Gus Suhr 686 280 583 272 0.408 0.467 0.875 0.355 0.414 0.769 0.382 0.440 0.822 0.717 18.1 15.1 33.3 0.99 18.5 15.4 34.0
1988 SFN Brett Butler 679 264 568 226 0.389 0.398 0.787 0.319 0.366 0.685 0.354 0.382 0.736 0.669 23.8 8.9 32.8 0.94 24.7 9.2 34.0
2024 ARI Joc Pederson 449 174 367 189 0.388 0.515 0.903 0.323 0.405 0.728 0.355 0.460 0.815 0.718 14.4 19.3 33.7 0.99 14.5 19.4 33.9
2003 ATL Andruw Jones 659 223 595 305 0.338 0.513 0.851 0.321 0.419 0.740 0.330 0.466 0.795 0.745 5.6 27.8 33.5 0.99 5.7 28.1 33.9
2022 ATL Michael Harris 441 149 414 213 0.338 0.514 0.852 0.304 0.385 0.689 0.321 0.450 0.771 0.711 7.5 26.6 34.2 0.99 7.4 26.4 33.9
1985 BOS Jim Rice 608 212 546 266 0.349 0.487 0.836 0.316 0.396 0.712 0.332 0.441 0.774 0.730 9.8 24.8 34.6 1.01 9.6 24.3 33.9
1934 BRO Danny Taylor 476 186 405 178 0.391 0.440 0.830 0.311 0.368 0.679 0.351 0.404 0.755 0.722 18.9 14.8 33.8 1.00 19.0 14.8 33.9
1917 CHA Shoeless Joe Jackson 621 226 537 232 0.364 0.432 0.796 0.332 0.346 0.678 0.348 0.389 0.737 0.626 10.0 23.3 33.3 0.98 10.2 23.7 33.9
1938 CHN Stan Hack 707 289 609 263 0.409 0.432 0.841 0.340 0.399 0.740 0.374 0.416 0.790 0.701 24.2 9.7 33.9 1.00 24.2 9.7 33.9
1919 CIN Edd Roush 572 210 504 219 0.367 0.435 0.802 0.316 0.355 0.671 0.342 0.395 0.736 0.639 14.6 20.2 34.8 1.03 14.2 19.7 33.9
2012 CIN Jay Bruce 633 207 560 288 0.327 0.514 0.841 0.316 0.389 0.705 0.321 0.452 0.773 0.715 3.6 35.3 39.0 1.04 3.1 30.7 33.9
1991 DET Lou Whitaker 572 223 470 230 0.390 0.489 0.879 0.329 0.385 0.715 0.360 0.437 0.797 0.721 17.3 24.1 41.5 1.08 14.1 19.7 33.9
1983 KCA Hal McRae 654 243 589 272 0.372 0.462 0.833 0.317 0.400 0.717 0.344 0.431 0.775 0.726 17.8 18.1 36.0 1.02 16.8 17.0 33.9
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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).