Batters



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Showing page 364 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
2005 HOU Jason Lane 561 177 517 258 0.316 0.499 0.815 0.325 0.419 0.744 0.320 0.459 0.779 0.741 -2.6 20.4 17.7 1.01 -2.6 20.3 17.6
1957 KC1 Gus Zernial 478 138 437 206 0.289 0.471 0.760 0.303 0.369 0.673 0.296 0.420 0.716 0.704 -3.4 22.8 19.3 1.02 -3.6 21.3 17.6
1978 KCA Hal McRae 692 227 623 267 0.328 0.429 0.757 0.318 0.385 0.703 0.323 0.407 0.730 0.707 3.3 13.8 17.2 0.99 3.4 14.1 17.6
1999 MIL Alex Ochoa 329 133 277 129 0.404 0.466 0.870 0.337 0.417 0.753 0.371 0.441 0.812 0.768 11.1 7.0 18.1 1.03 10.8 6.8 17.6
2023 MIN Ryan Jeffers 335 122 286 140 0.364 0.490 0.854 0.317 0.419 0.736 0.341 0.454 0.795 0.728 7.9 9.5 17.3 1.00 8.0 9.7 17.6
2006 NYA Bobby Abreu 248 103 209 106 0.415 0.507 0.922 0.349 0.449 0.799 0.382 0.478 0.861 0.775 7.1 10.8 18.0 0.96 6.9 10.6 17.6
1931 NYA Tony Lazzeri 569 210 484 194 0.369 0.401 0.770 0.330 0.377 0.707 0.349 0.389 0.739 0.735 11.2 5.6 16.7 0.96 11.8 5.9 17.6
2023 NYN Tommy Pham 264 92 231 109 0.348 0.472 0.820 0.315 0.414 0.729 0.332 0.443 0.775 0.739 7.1 9.5 16.5 0.98 7.6 10.1 17.6
2012 OAK Josh Reddick 673 205 611 283 0.305 0.463 0.768 0.315 0.399 0.714 0.310 0.431 0.741 0.729 -3.4 19.5 16.1 0.96 -3.2 20.8 17.6
2022 OAK Seth Brown 555 169 500 222 0.305 0.444 0.749 0.305 0.375 0.680 0.305 0.409 0.714 0.700 0.0 17.5 17.4 1.01 0.0 17.7 17.6
2018 PIT Corey Dickerson 533 176 504 239 0.330 0.474 0.804 0.326 0.405 0.731 0.328 0.439 0.768 0.720 0.9 17.4 18.4 1.04 0.9 16.6 17.6
1939 PIT Paul Waner 507 186 462 202 0.367 0.437 0.804 0.338 0.396 0.734 0.352 0.417 0.769 0.713 7.5 9.5 17.1 0.98 7.7 9.8 17.6
1914 PTF Rebel Oakes 634 221 573 241 0.349 0.421 0.769 0.339 0.372 0.710 0.344 0.396 0.740 0.675 3.1 14.4 17.4 0.99 3.1 14.6 17.6
2015 SDN Matt Kemp 648 202 596 264 0.312 0.443 0.755 0.302 0.402 0.704 0.307 0.422 0.729 0.710 3.1 12.2 15.4 0.98 3.5 14.0 17.6
2016 TBA Brad Miller 601 183 548 264 0.304 0.482 0.786 0.318 0.409 0.726 0.311 0.445 0.756 0.743 -3.9 20.0 16.2 1.00 -3.7 21.2 17.6
2025 TBA Brandon Lowe 553 170 507 242 0.307 0.477 0.785 0.315 0.400 0.715 0.311 0.438 0.750 0.718 -2.1 19.5 17.4 0.99 -2.1 19.7 17.6
1987 TEX Pete O'Brien 638 222 569 260 0.348 0.457 0.805 0.329 0.416 0.745 0.338 0.436 0.775 0.756 6.1 11.7 17.7 1.02 6.1 11.6 17.6
2009 TOR Marco Scutaro 680 256 574 235 0.376 0.409 0.786 0.322 0.414 0.736 0.349 0.412 0.761 0.762 18.6 -0.9 17.6 1.00 18.6 -0.9 17.6
1957 WS1 Eddie Yost 492 182 414 154 0.370 0.372 0.742 0.306 0.364 0.670 0.338 0.368 0.706 0.704 15.7 1.5 17.3 1.01 16.0 1.5 17.6
1935 WS1 John Stone 499 185 455 208 0.371 0.457 0.828 0.352 0.404 0.756 0.361 0.431 0.792 0.747 4.7 12.2 16.9 0.98 4.9 12.7 17.6
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Columns:
--------

Note: The batter's composite OB% and SLG% is obtained by the sum of all individual
plate appearances. For each PA, the OB% and SLG% used is versus pitchers of the same
hand as the one he's facing.

OPP_PIT_OB: the opposing pitcher OB% against, when facing batters of the same hand
OPP_PIT_SLG: the opposing pitcher SLG% against, when facing batters of the same hand
OPP_PIT_OOPS: the opposing pitcher OB% + SLG% against, when facing batters of the same hand

EXPCT_OB_AVG: the average of the opposing pitcher's OPP_PIT_OB and the batter's OB% (vs. L or R)
EXPCT_SLG_AVG: the average of the opposing pitcher's OPP_PIT_SLG and the batter's SLG% (vs. L or R)
EXPCT_OPS: the average of the opposing pitcher's OOPS and the batter's OPS (vs. L or R)

LG_OPS: the average league OPS, with the league of the home park being the league

PME_OB: the cumulative result of the plate appearance minus the EXPCT_OB_AVG
PME_SLG: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG
PME: the cumulative result of the plate appearance minus the EXPCT_OPS

PF: the composite park factor the batter experienced, based on lefty-righty and park

PME_OB_PF: the cumulative result of the plate appearance minus the EXPCT_OB_AVG, with PF
PME_SLG_PF: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG, with PF
PME_PF: the cumulative result of the plate appearance minus the EXPCT_OPS, with PF


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).