Batters



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Showing page 167 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
1945 NY1 Mel Ott 534 220 451 225 0.412 0.499 0.911 0.357 0.402 0.759 0.384 0.451 0.835 0.691 14.7 22.2 36.9 1.02 13.5 20.4 33.9
1989 NYN Kevin McReynolds 599 195 545 245 0.326 0.450 0.775 0.293 0.361 0.654 0.309 0.405 0.714 0.674 9.6 24.3 33.8 1.00 9.6 24.4 33.9
1962 PHI Roy Sievers 543 188 477 217 0.346 0.455 0.801 0.310 0.378 0.688 0.328 0.416 0.744 0.716 9.8 18.8 28.6 0.97 11.6 22.3 33.9
1965 PIT Willie Stargell 582 191 533 267 0.328 0.501 0.829 0.314 0.388 0.703 0.321 0.445 0.766 0.681 4.0 30.2 34.1 1.01 4.0 30.0 33.9
1995 SDN Ken Caminiti 602 229 526 270 0.380 0.513 0.894 0.347 0.426 0.773 0.364 0.470 0.834 0.735 10.1 22.5 32.6 0.96 10.5 23.4 33.9
1986 SEA Ken Phelps 441 179 344 181 0.406 0.526 0.932 0.333 0.402 0.735 0.370 0.464 0.834 0.735 16.0 21.0 37.0 1.04 14.7 19.2 33.9
1948 SLA Jerry Priddy 657 253 560 248 0.385 0.443 0.828 0.333 0.376 0.709 0.359 0.409 0.768 0.726 16.9 18.5 35.4 1.03 16.2 17.7 33.9
2008 SLN Felipe Lopez 169 72 156 84 0.426 0.538 0.964 0.352 0.429 0.781 0.389 0.484 0.873 0.740 13.3 20.4 33.8 1.00 13.3 20.5 33.9
2018 TEX Shin-Soo Choo 665 250 560 243 0.376 0.434 0.810 0.309 0.395 0.704 0.343 0.414 0.757 0.733 22.0 11.8 33.8 1.08 22.1 11.8 33.9
2000 TOR Shannon Stewart 631 229 583 302 0.363 0.518 0.881 0.332 0.434 0.766 0.347 0.476 0.823 0.790 9.8 24.6 34.4 1.03 9.7 24.2 33.9
2021 ARI Ketel Marte 374 141 340 181 0.377 0.532 0.909 0.311 0.397 0.708 0.344 0.465 0.809 0.723 12.4 22.6 35.1 1.04 11.9 21.8 33.8
1959 BAL Bob Nieman 409 149 360 190 0.364 0.528 0.892 0.318 0.393 0.711 0.341 0.460 0.802 0.703 9.4 24.3 33.8 1.00 9.4 24.3 33.8
2008 BOS J. D. Drew 456 186 368 191 0.408 0.519 0.927 0.337 0.415 0.752 0.372 0.467 0.839 0.754 16.1 19.4 35.6 1.07 15.3 18.4 33.8
1924 BOS Joe Harris 591 233 491 211 0.394 0.430 0.824 0.327 0.361 0.688 0.361 0.395 0.756 0.743 19.8 17.2 36.9 1.04 18.1 15.8 33.8
1935 CHN Augie Galan 748 294 646 302 0.393 0.467 0.861 0.347 0.422 0.770 0.370 0.445 0.815 0.717 17.3 14.1 31.4 0.97 18.6 15.2 33.8
1923 CLE Joe Sewell 685 301 554 265 0.439 0.478 0.918 0.372 0.431 0.803 0.406 0.455 0.860 0.728 23.0 13.7 36.6 1.03 21.2 12.7 33.8
1963 CLE Max Alvis 660 211 602 277 0.320 0.460 0.780 0.303 0.378 0.680 0.311 0.419 0.730 0.688 5.7 25.2 30.8 0.96 6.3 27.6 33.8
1958 CLE Vic Power 405 136 385 194 0.336 0.504 0.840 0.306 0.384 0.690 0.321 0.444 0.765 0.701 7.2 26.0 33.2 0.99 7.3 26.5 33.8
2019 HOU Jose Altuve 548 193 500 275 0.352 0.550 0.902 0.320 0.440 0.761 0.336 0.495 0.831 0.761 8.7 27.6 36.4 1.05 8.1 25.6 33.8
1996 MIL Dave Nilsson 516 210 453 238 0.407 0.525 0.932 0.352 0.437 0.790 0.380 0.481 0.861 0.793 14.2 20.3 34.4 1.02 14.0 19.9 33.8
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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).