Batters



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Showing page 152 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
2017 CHN Anthony Rizzo 691 271 572 290 0.392 0.507 0.899 0.333 0.428 0.761 0.362 0.468 0.830 0.746 20.7 21.3 42.0 1.06 17.7 18.2 35.9
2025 COL Hunter Goodman 579 187 540 281 0.323 0.520 0.843 0.304 0.389 0.692 0.313 0.455 0.768 0.718 5.6 35.8 41.3 1.10 4.9 31.1 35.9
1982 MIN Gary Ward 616 203 570 296 0.330 0.519 0.849 0.318 0.400 0.718 0.324 0.460 0.783 0.727 3.6 33.7 37.3 1.02 3.5 32.4 35.9
2005 NYA Hideki Matsui 704 258 629 312 0.366 0.496 0.863 0.331 0.419 0.750 0.349 0.458 0.806 0.753 12.6 24.1 36.7 1.00 12.3 23.6 35.9
2003 PIT Brian Giles 481 207 388 202 0.430 0.521 0.951 0.344 0.433 0.777 0.387 0.477 0.864 0.745 21.6 17.3 38.8 1.07 20.0 16.0 35.9
1940 PIT Elbie Fletcher 641 267 510 223 0.417 0.437 0.854 0.341 0.401 0.741 0.379 0.419 0.798 0.697 24.3 10.1 34.4 1.00 25.4 10.5 35.9
1942 WS1 George Case 563 211 513 209 0.375 0.407 0.782 0.311 0.334 0.645 0.343 0.371 0.714 0.681 18.1 18.7 36.9 1.02 17.6 18.2 35.9
1942 WS1 Stan Spence 696 266 629 272 0.382 0.432 0.815 0.332 0.368 0.700 0.357 0.400 0.757 0.681 17.6 20.0 37.5 1.04 16.9 19.1 35.9
1978 DET Jason Thompson 669 243 589 278 0.363 0.472 0.835 0.325 0.379 0.703 0.344 0.425 0.769 0.707 13.0 27.9 40.8 1.06 11.4 24.5 35.8
1915 NY1 Larry Doyle 638 222 588 261 0.348 0.444 0.792 0.316 0.357 0.673 0.332 0.401 0.733 0.630 10.1 25.3 35.5 0.93 10.2 25.5 35.8
2011 NYN Jose Reyes 586 224 537 265 0.382 0.493 0.876 0.332 0.415 0.747 0.357 0.454 0.811 0.706 14.8 21.1 35.9 1.02 14.8 21.0 35.8
1946 SLN Enos Slaughter 685 254 609 283 0.371 0.465 0.835 0.335 0.380 0.715 0.353 0.422 0.775 0.678 12.4 25.5 37.9 1.04 11.7 24.1 35.8
2012 ATL Jason Heyward 651 218 587 281 0.335 0.479 0.814 0.313 0.385 0.698 0.324 0.432 0.756 0.715 7.1 28.5 35.7 1.02 7.1 28.5 35.7
1991 BOS Wade Boggs 641 270 546 251 0.421 0.460 0.881 0.330 0.392 0.723 0.376 0.426 0.802 0.721 29.3 18.6 47.8 1.10 21.9 13.9 35.7
1926 BRO Babe Herman 554 203 496 248 0.366 0.500 0.866 0.328 0.403 0.731 0.347 0.452 0.799 0.713 10.6 24.1 34.8 0.98 10.9 24.7 35.7
1938 BRO Ernie Koy 575 198 521 244 0.344 0.468 0.813 0.314 0.363 0.677 0.329 0.416 0.745 0.701 8.8 27.3 36.0 1.01 8.7 27.1 35.7
1930 BRO Glenn Wright 581 203 532 289 0.349 0.543 0.893 0.342 0.433 0.776 0.346 0.488 0.834 0.799 1.9 29.3 31.2 0.96 2.2 33.5 35.7
1990 CIN Eric Davis 518 180 453 220 0.347 0.486 0.833 0.303 0.380 0.682 0.325 0.433 0.758 0.700 11.5 24.2 35.9 1.01 11.4 24.1 35.7
1931 CIN Tony Cuccinello 639 235 575 248 0.368 0.431 0.799 0.315 0.365 0.680 0.341 0.398 0.739 0.716 16.8 19.0 35.8 1.01 16.8 18.9 35.7
1991 CLE Albert Belle 496 160 461 249 0.323 0.540 0.863 0.320 0.396 0.715 0.321 0.468 0.789 0.721 0.7 33.1 33.9 1.02 0.7 34.9 35.7
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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).