Batters



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Showing page 161 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
2013 WAS Ryan Zimmerman 633 218 568 264 0.344 0.465 0.809 0.305 0.385 0.690 0.325 0.425 0.749 0.700 12.5 23.5 36.0 1.04 12.0 22.7 34.7
2021 BOS J. D. Martinez 634 221 570 295 0.349 0.518 0.866 0.315 0.425 0.740 0.332 0.471 0.803 0.730 10.6 26.9 37.5 1.06 9.8 24.8 34.6
1914 BRF Solly Hofman 593 211 518 212 0.356 0.409 0.765 0.302 0.337 0.639 0.329 0.373 0.702 0.675 16.1 18.6 34.7 1.02 16.1 18.5 34.6
1970 DET Al Kaline 555 208 467 210 0.375 0.450 0.824 0.313 0.377 0.689 0.344 0.413 0.757 0.697 17.2 17.0 34.2 0.99 17.4 17.2 34.6
2022 HOU Kyle Tucker 609 200 544 260 0.328 0.478 0.806 0.304 0.379 0.683 0.316 0.429 0.745 0.700 7.3 27.4 34.7 1.00 7.3 27.3 34.6
1976 KCA Amos Otis 665 225 592 263 0.338 0.444 0.783 0.312 0.356 0.668 0.325 0.400 0.726 0.677 8.7 25.9 34.4 1.00 8.8 26.0 34.6
2011 KCA Billy Butler 673 243 597 275 0.361 0.461 0.822 0.313 0.403 0.716 0.337 0.432 0.769 0.728 16.1 17.5 33.6 0.99 16.6 18.0 34.6
1999 MIL Jeff Cirillo 697 278 607 280 0.399 0.461 0.860 0.330 0.426 0.755 0.364 0.444 0.808 0.768 24.3 11.1 35.4 1.02 23.8 10.8 34.6
1939 NY1 Frank Demaree 643 240 560 234 0.373 0.418 0.791 0.312 0.369 0.681 0.342 0.394 0.736 0.713 19.7 13.7 33.4 0.99 20.4 14.2 34.6
2022 NYN Brandon Nimmo 673 246 580 251 0.366 0.433 0.798 0.309 0.378 0.687 0.337 0.405 0.742 0.711 19.0 16.3 35.4 0.98 18.6 15.9 34.6
1981 PIT Bill Madlock 320 132 279 138 0.413 0.495 0.907 0.308 0.359 0.667 0.360 0.427 0.787 0.679 16.7 18.9 35.5 1.03 16.3 18.4 34.6
2004 SDN Phil Nevin 623 229 547 269 0.368 0.492 0.859 0.321 0.421 0.742 0.344 0.456 0.801 0.752 14.4 19.2 33.6 0.95 14.8 19.8 34.6
1920 WS1 Braggo Roth 561 218 468 202 0.389 0.432 0.820 0.320 0.364 0.684 0.355 0.398 0.752 0.723 19.2 16.4 35.5 0.98 18.7 16.0 34.6
1915 BOS Duffy Lewis 634 211 556 213 0.333 0.383 0.716 0.291 0.306 0.598 0.312 0.345 0.657 0.640 13.1 21.5 34.7 1.02 13.0 21.4 34.5
2007 BOS Manny Ramirez 569 221 483 238 0.388 0.493 0.881 0.327 0.413 0.740 0.358 0.453 0.810 0.759 17.5 19.4 36.8 1.06 16.4 18.2 34.5
1956 CHA Larry Doby 619 241 504 235 0.389 0.466 0.856 0.344 0.386 0.730 0.367 0.426 0.793 0.731 14.1 20.6 34.7 1.00 14.0 20.5 34.5
1966 CHA Tommie Agee 689 223 629 281 0.324 0.447 0.770 0.299 0.370 0.668 0.311 0.408 0.719 0.671 8.6 24.7 33.3 0.94 8.9 25.6 34.5
2015 CIN Todd Frazier 678 209 619 308 0.308 0.498 0.806 0.301 0.388 0.688 0.305 0.443 0.747 0.710 2.4 34.3 36.7 1.03 2.3 32.2 34.5
2012 CLE Shin-Soo Choo 686 256 598 264 0.373 0.441 0.815 0.318 0.394 0.711 0.345 0.417 0.763 0.729 19.1 14.4 33.5 0.98 19.7 14.8 34.5
1975 LAN Jim Wynn 529 213 412 172 0.403 0.417 0.820 0.315 0.365 0.680 0.359 0.391 0.750 0.691 23.3 10.9 34.2 0.98 23.5 11.0 34.5
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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).