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Showing page 143 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
1920 CIN Heinie Groh 633 232 550 216 0.367 0.393 0.759 0.302 0.346 0.648 0.334 0.369 0.704 0.670 20.4 12.6 33.1 0.96 22.9 14.2 37.2
1967 DET Willie Horton 445 150 401 193 0.337 0.481 0.818 0.294 0.347 0.641 0.316 0.414 0.730 0.651 9.6 27.2 36.7 1.00 9.7 27.6 37.2
1975 HOU Cesar Cedeno 576 213 500 220 0.370 0.440 0.810 0.310 0.364 0.674 0.340 0.402 0.742 0.691 17.4 19.3 36.8 0.98 17.6 19.5 37.2
2009 LAN Matt Kemp 667 235 606 297 0.352 0.490 0.842 0.316 0.407 0.723 0.334 0.448 0.783 0.735 12.0 25.1 37.1 0.93 12.0 25.2 37.2
2013 MIN Joe Mauer 508 205 445 212 0.404 0.476 0.880 0.321 0.404 0.725 0.362 0.440 0.802 0.723 21.0 17.0 37.9 1.02 20.6 16.7 37.2
1947 NY1 Willard Marshall 655 239 587 310 0.365 0.528 0.893 0.356 0.412 0.768 0.361 0.470 0.831 0.724 2.7 34.3 36.9 1.00 2.7 34.6 37.2
1939 NYA Joe Gordon 648 238 567 287 0.367 0.506 0.873 0.340 0.401 0.742 0.354 0.454 0.808 0.752 8.8 29.8 38.7 0.93 8.5 28.6 37.2
1963 NYA Tom Tresh 614 227 520 253 0.370 0.487 0.856 0.320 0.402 0.722 0.345 0.444 0.789 0.688 15.3 21.9 37.2 1.01 15.3 21.9 37.2
2015 TEX Shin-Soo Choo 653 244 555 257 0.374 0.463 0.837 0.313 0.394 0.707 0.343 0.429 0.772 0.728 19.6 19.8 39.4 1.02 18.5 18.7 37.2
2005 ATL Chipper Jones 432 178 358 199 0.412 0.556 0.968 0.342 0.430 0.772 0.377 0.493 0.870 0.741 15.2 23.6 38.7 1.02 14.6 22.6 37.1
2012 BAL Adam Jones 697 233 648 327 0.334 0.505 0.839 0.308 0.410 0.719 0.321 0.458 0.779 0.729 9.0 30.5 39.5 1.04 8.5 28.6 37.1
1956 BRO Carl Furillo 587 209 523 244 0.356 0.467 0.823 0.305 0.383 0.688 0.331 0.425 0.755 0.718 14.9 21.6 36.6 1.00 15.1 21.9 37.1
1983 CHA Carlton Fisk 545 193 488 253 0.354 0.518 0.873 0.318 0.397 0.715 0.336 0.458 0.794 0.726 9.8 29.5 39.3 1.04 9.3 27.8 37.1
1975 CIN Pete Rose 764 310 662 286 0.406 0.432 0.838 0.345 0.394 0.739 0.375 0.413 0.788 0.691 23.4 13.5 36.9 1.00 23.5 13.6 37.1
2014 COL Corey Dickerson 478 174 436 247 0.364 0.567 0.931 0.312 0.381 0.693 0.338 0.474 0.812 0.691 12.3 40.8 53.1 1.14 8.6 28.5 37.1
1927 NY1 Doc Farrell 159 69 142 74 0.434 0.521 0.955 0.319 0.369 0.688 0.377 0.445 0.822 0.714 16.6 20.1 36.8 0.98 16.7 20.3 37.1
1922 NY1 George Kelly 642 229 592 292 0.357 0.493 0.850 0.329 0.402 0.731 0.343 0.447 0.791 0.743 8.9 27.3 36.0 0.99 9.2 28.1 37.1
1920 PHI Cy Williams 643 227 590 292 0.353 0.495 0.848 0.322 0.370 0.692 0.337 0.433 0.770 0.670 10.0 36.7 46.7 1.10 7.9 29.2 37.1
2011 PHI Hunter Pence 236 93 207 116 0.394 0.560 0.954 0.308 0.393 0.701 0.351 0.477 0.828 0.706 13.1 23.9 37.1 0.98 13.1 23.9 37.1
1913 PHI Sherry Magee 538 190 471 225 0.353 0.478 0.831 0.307 0.347 0.654 0.330 0.413 0.743 0.671 12.6 30.6 43.2 1.06 10.8 26.3 37.1
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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).