Batters



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Showing page 170 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
1927 PIT Pie Traynor 633 221 573 261 0.349 0.455 0.805 0.319 0.374 0.693 0.334 0.415 0.749 0.714 9.6 23.5 33.1 1.02 9.7 23.9 33.6
2008 SDN Brian Giles 653 260 559 255 0.398 0.456 0.854 0.333 0.413 0.746 0.366 0.435 0.800 0.740 21.3 11.6 32.9 0.95 21.8 11.8 33.6
1995 SEA Jay Buhner 539 184 470 266 0.341 0.566 0.907 0.334 0.430 0.763 0.338 0.498 0.835 0.769 2.0 32.1 34.1 1.01 2.0 31.6 33.6
1987 SFN Candy Maldonado 489 169 442 225 0.346 0.509 0.855 0.307 0.395 0.702 0.326 0.452 0.778 0.728 9.6 25.1 34.6 0.96 9.3 24.4 33.6
1995 SLN Bernard Gilkey 531 190 480 235 0.358 0.490 0.847 0.310 0.402 0.713 0.334 0.446 0.780 0.735 12.6 20.9 33.5 0.98 12.6 21.0 33.6
1986 CAL Brian Downing 631 244 513 232 0.387 0.452 0.839 0.322 0.408 0.730 0.354 0.430 0.784 0.735 20.4 11.2 31.6 0.98 21.6 11.9 33.5
1984 CAL Fred Lynn 600 219 517 245 0.365 0.474 0.839 0.326 0.385 0.711 0.345 0.429 0.775 0.722 11.8 22.1 33.9 1.02 11.7 21.8 33.5
1943 NYA Bill Dickey 284 126 242 119 0.444 0.492 0.935 0.327 0.364 0.691 0.385 0.428 0.813 0.657 16.6 15.6 32.1 0.95 17.3 16.3 33.5
1952 NYA Gene Woodling 471 186 408 193 0.395 0.473 0.868 0.332 0.379 0.711 0.364 0.426 0.789 0.690 14.7 19.0 33.8 1.01 14.6 18.8 33.5
1962 PHI Johnny Callison 672 241 603 296 0.359 0.491 0.850 0.341 0.397 0.738 0.350 0.444 0.794 0.716 6.1 27.9 33.9 1.00 6.0 27.6 33.5
2018 SEA Nelson Cruz 591 202 519 264 0.342 0.509 0.850 0.314 0.413 0.727 0.328 0.461 0.789 0.733 8.3 25.5 33.7 0.98 8.3 25.3 33.5
1910 SLN Mike Mowrey 581 211 489 182 0.363 0.372 0.735 0.311 0.327 0.638 0.337 0.349 0.687 0.657 19.1 14.2 33.4 0.96 19.2 14.2 33.5
2016 BAL Mark Trumbo 667 211 613 327 0.316 0.533 0.850 0.315 0.421 0.736 0.316 0.477 0.793 0.743 0.6 34.0 34.6 1.04 0.6 32.8 33.4
1921 BOS Del Pratt 577 215 521 240 0.373 0.461 0.833 0.327 0.385 0.711 0.350 0.423 0.772 0.754 13.3 19.9 33.2 1.00 13.4 20.0 33.4
1916 CHN Cy Williams 485 173 406 186 0.357 0.458 0.815 0.299 0.339 0.638 0.328 0.399 0.727 0.623 14.1 24.3 38.3 1.07 12.3 21.2 33.4
1910 CHN Frank Schulte 628 210 558 257 0.334 0.461 0.795 0.329 0.353 0.682 0.331 0.407 0.738 0.657 2.9 31.9 34.8 0.94 2.8 30.6 33.4
2022 CIN Brandon Drury 385 129 350 182 0.335 0.520 0.855 0.307 0.390 0.697 0.321 0.455 0.776 0.711 8.3 27.7 36.0 1.05 7.7 25.7 33.4
2003 COL Larry Walker 564 238 454 216 0.422 0.476 0.898 0.339 0.414 0.753 0.380 0.445 0.826 0.745 23.4 13.9 37.4 1.04 20.9 12.4 33.4
1986 HOU Kevin Bass 640 228 591 287 0.356 0.486 0.842 0.335 0.395 0.730 0.346 0.440 0.786 0.698 6.8 26.8 33.6 1.02 6.8 26.6 33.4
2021 MIN Byron Buxton 254 91 235 152 0.358 0.647 1.005 0.318 0.412 0.730 0.338 0.529 0.868 0.730 5.1 27.5 32.6 0.98 5.2 28.2 33.4
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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).