Batters



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Showing page 144 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 SFN Buster Posey 595 221 520 234 0.371 0.450 0.821 0.308 0.387 0.695 0.340 0.419 0.758 0.700 18.9 16.5 35.4 0.95 19.8 17.3 37.1
2004 TEX Mark Teixeira 625 231 545 305 0.370 0.560 0.929 0.344 0.441 0.785 0.357 0.500 0.857 0.769 8.1 32.5 40.6 1.06 7.4 29.7 37.1
2013 ATL Justin Upton 643 227 558 259 0.353 0.464 0.817 0.306 0.389 0.695 0.330 0.427 0.756 0.700 15.0 22.0 37.0 0.97 15.0 22.0 37.0
1966 BAL Brooks Robinson 686 228 620 275 0.332 0.444 0.776 0.295 0.365 0.661 0.314 0.404 0.718 0.671 12.8 24.6 37.4 1.01 12.7 24.3 37.0
2000 CHA Magglio Ordonez 665 247 588 321 0.371 0.546 0.917 0.339 0.437 0.776 0.355 0.491 0.847 0.790 10.8 32.0 42.9 1.07 9.3 27.6 37.0
1964 CHA Pete Ward 608 210 539 255 0.345 0.473 0.818 0.317 0.375 0.692 0.331 0.424 0.755 0.693 8.8 26.6 35.5 0.96 9.2 27.7 37.0
2019 CHA Yoan Moncada 559 205 511 280 0.367 0.548 0.915 0.328 0.444 0.772 0.347 0.496 0.843 0.761 10.8 26.5 37.3 1.01 10.7 26.3 37.0
2017 CLE Edwin Encarnacion 669 252 554 279 0.377 0.504 0.880 0.327 0.436 0.763 0.352 0.470 0.821 0.752 16.7 20.2 36.8 0.96 16.8 20.3 37.0
1943 CLE Jeff Heath 488 180 424 204 0.369 0.481 0.850 0.329 0.355 0.684 0.349 0.418 0.767 0.657 9.9 26.8 36.7 0.97 10.0 27.0 37.0
1945 DET Hank Greenberg 312 126 270 147 0.404 0.544 0.948 0.311 0.332 0.643 0.358 0.438 0.796 0.665 14.4 28.9 43.4 1.08 12.3 24.6 37.0
2023 LAN J. D. Martinez 479 153 432 247 0.319 0.572 0.891 0.314 0.411 0.725 0.317 0.491 0.808 0.739 1.2 35.1 36.5 0.97 1.2 35.6 37.0
1930 PIT Adam Comorosky 684 242 597 316 0.354 0.529 0.883 0.336 0.424 0.760 0.345 0.477 0.822 0.799 6.2 30.9 37.0 1.00 6.2 30.9 37.0
1919 PIT George Whitted 143 58 131 72 0.406 0.550 0.955 0.299 0.338 0.637 0.352 0.444 0.796 0.639 14.0 23.3 37.3 1.02 13.9 23.1 37.0
1986 PIT Jim Morrison 593 198 537 259 0.334 0.482 0.816 0.304 0.379 0.682 0.319 0.430 0.749 0.698 8.9 27.9 36.7 1.00 9.0 28.1 37.0
1938 BOS Ben Chapman 552 228 479 237 0.413 0.495 0.908 0.349 0.409 0.758 0.381 0.452 0.833 0.768 17.7 20.3 38.0 1.03 17.2 19.7 36.9
1962 CLE John Romano 548 198 459 220 0.361 0.479 0.841 0.308 0.380 0.688 0.335 0.430 0.764 0.716 14.5 22.6 37.2 1.02 14.4 22.4 36.9
1911 CLE Nap Lajoie 353 145 315 143 0.411 0.454 0.865 0.305 0.338 0.643 0.358 0.396 0.754 0.688 19.1 18.4 37.5 1.04 18.8 18.1 36.9
1983 DET Alan Trammell 581 218 505 238 0.375 0.471 0.847 0.314 0.394 0.708 0.345 0.432 0.777 0.726 17.7 19.4 37.2 0.98 17.6 19.2 36.9
1917 DET Harry Heilmann 623 203 557 217 0.326 0.390 0.715 0.288 0.299 0.587 0.307 0.345 0.651 0.626 11.9 25.1 37.0 1.01 11.9 25.0 36.9
1973 DET Willie Horton 443 160 411 206 0.361 0.501 0.862 0.317 0.374 0.691 0.339 0.438 0.777 0.707 9.7 26.0 35.9 0.98 10.0 26.7 36.9
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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).