Batters



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Showing page 70 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
2022 BOS Rafael Devers 614 220 555 289 0.358 0.521 0.879 0.307 0.380 0.688 0.333 0.450 0.783 0.700 15.6 38.8 54.4 1.03 15.0 37.2 52.2
1943 BRO Billy Herman 663 259 585 244 0.391 0.417 0.808 0.306 0.333 0.639 0.348 0.375 0.723 0.666 28.1 24.7 52.8 1.01 27.8 24.4 52.2
1962 DET Al Kaline 452 169 398 236 0.374 0.593 0.967 0.315 0.390 0.706 0.345 0.492 0.836 0.716 13.2 40.4 53.6 1.02 12.9 39.3 52.2
1914 DET Ty Cobb 415 191 345 177 0.460 0.513 0.973 0.337 0.358 0.695 0.399 0.435 0.834 0.633 25.5 26.7 52.2 1.00 25.5 26.7 52.2
1975 NYA Bobby Bonds 626 235 529 271 0.375 0.512 0.888 0.317 0.380 0.697 0.346 0.446 0.793 0.703 18.1 34.5 52.6 0.99 18.0 34.2 52.2
1977 NYA Reggie Jackson 606 227 525 289 0.375 0.550 0.925 0.334 0.396 0.730 0.354 0.473 0.827 0.732 12.1 40.5 52.6 1.02 12.0 40.2 52.2
2002 NYN Mike Piazza 541 194 478 260 0.359 0.544 0.903 0.309 0.391 0.701 0.334 0.468 0.802 0.738 13.4 36.4 49.7 0.93 14.1 38.2 52.2
1984 SDN Tony Gwynn 675 274 606 269 0.406 0.444 0.850 0.324 0.362 0.687 0.365 0.403 0.768 0.685 27.5 24.6 52.2 1.01 27.5 24.6 52.2
2003 SEA Bret Boone 705 258 622 333 0.366 0.535 0.901 0.322 0.425 0.747 0.344 0.480 0.824 0.759 15.4 34.5 50.0 0.96 16.1 36.0 52.2
1980 CAL Jason Thompson 387 169 312 164 0.437 0.526 0.962 0.343 0.407 0.750 0.390 0.466 0.856 0.727 24.4 24.8 49.2 0.97 25.8 26.3 52.1
2006 MIN Justin Morneau 661 248 592 331 0.375 0.559 0.934 0.338 0.430 0.768 0.357 0.494 0.851 0.775 12.3 39.5 51.8 1.00 12.4 39.7 52.1
1960 NYA Bill Skowron 584 206 538 284 0.353 0.528 0.881 0.317 0.386 0.704 0.335 0.457 0.792 0.711 10.4 38.2 48.5 0.96 11.2 41.0 52.1
1985 TOR Jesse Barfield 612 226 539 289 0.369 0.536 0.905 0.320 0.401 0.721 0.345 0.468 0.813 0.730 15.0 36.5 51.6 0.99 15.1 36.9 52.1
2017 WAS Bryce Harper 492 203 420 250 0.413 0.595 1.008 0.333 0.435 0.769 0.373 0.515 0.888 0.746 19.4 33.0 52.4 1.01 19.3 32.8 52.1
1974 ATL Ralph Garr 645 244 606 305 0.378 0.503 0.882 0.335 0.379 0.714 0.357 0.441 0.798 0.688 13.7 37.6 51.3 0.99 13.9 38.1 52.0
2014 BAL Nelson Cruz 678 226 613 322 0.333 0.525 0.859 0.304 0.393 0.697 0.319 0.459 0.778 0.704 10.1 41.1 51.1 0.97 10.3 41.8 52.0
1939 CHN Hank Leiber 431 176 365 203 0.408 0.556 0.965 0.314 0.371 0.685 0.361 0.464 0.825 0.713 20.5 33.8 54.3 1.03 19.6 32.4 52.0
1993 HOU Jeff Bagwell 609 236 535 276 0.388 0.516 0.903 0.314 0.404 0.718 0.351 0.460 0.811 0.722 22.4 30.6 53.0 1.01 22.0 30.0 52.0
1965 PIT Roberto Clemente 642 242 589 273 0.377 0.463 0.840 0.301 0.372 0.673 0.339 0.418 0.757 0.681 24.4 26.4 50.8 0.98 25.0 27.0 52.0
2009 SLN Matt Holliday 270 113 235 142 0.419 0.604 1.023 0.318 0.402 0.720 0.368 0.503 0.871 0.735 17.0 33.7 50.7 0.97 17.4 34.6 52.0
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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).