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Showing page 174 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
1954 CLE Larry Doby 673 243 577 279 0.361 0.484 0.845 0.342 0.391 0.734 0.352 0.438 0.789 0.700 6.3 26.7 32.9 0.99 6.3 26.8 33.0
1933 DET Charlie Gehringer 705 275 628 294 0.390 0.468 0.858 0.348 0.409 0.757 0.369 0.439 0.808 0.727 14.9 18.7 33.5 1.02 14.7 18.4 33.0
1934 DET Marv Owen 637 242 565 255 0.380 0.451 0.831 0.333 0.386 0.718 0.356 0.418 0.775 0.744 15.2 18.3 33.6 1.02 14.9 18.0 33.0
1994 MIN Kirby Puckett 482 174 439 237 0.361 0.540 0.901 0.331 0.423 0.754 0.346 0.482 0.827 0.776 7.4 25.6 32.9 1.01 7.4 25.7 33.0
1925 PHA Sammy Hale 426 154 391 211 0.362 0.540 0.901 0.340 0.390 0.730 0.351 0.465 0.816 0.757 4.4 29.1 33.6 1.03 4.3 28.6 33.0
1914 PHA Stuffy McInnis 628 205 576 214 0.326 0.372 0.698 0.287 0.304 0.592 0.307 0.338 0.645 0.633 12.3 19.7 32.0 0.95 12.7 20.3 33.0
2010 PHI Ryan Howard 620 219 550 278 0.353 0.505 0.859 0.331 0.407 0.738 0.342 0.456 0.799 0.720 6.8 26.6 33.5 1.02 6.7 26.2 33.0
1929 PIT Paul Waner 704 293 596 318 0.416 0.534 0.950 0.371 0.475 0.847 0.394 0.504 0.898 0.773 15.8 17.4 33.2 1.01 15.7 17.3 33.0
1990 SLN Willie McGee 542 207 501 219 0.382 0.437 0.819 0.325 0.391 0.715 0.353 0.414 0.767 0.700 18.1 16.1 34.3 1.03 17.4 15.5 33.0
1983 TOR Lloyd Moseby 604 226 539 269 0.374 0.499 0.873 0.334 0.400 0.733 0.354 0.449 0.803 0.726 12.2 26.7 38.8 1.07 10.4 22.7 33.0
2007 BAL Nick Markakis 710 257 637 309 0.362 0.485 0.847 0.330 0.416 0.745 0.346 0.450 0.796 0.759 11.4 22.5 33.9 1.02 11.1 21.8 32.9
1971 CHA Mike Andrews 408 161 330 145 0.395 0.439 0.834 0.305 0.356 0.661 0.350 0.398 0.748 0.678 18.3 14.2 32.4 0.97 18.6 14.4 32.9
1994 CHN Sammy Sosa 458 155 426 232 0.338 0.545 0.883 0.318 0.418 0.735 0.328 0.481 0.809 0.743 4.8 26.6 31.4 0.93 5.0 27.9 32.9
2018 CIN Joey Votto 623 260 503 211 0.417 0.419 0.837 0.325 0.403 0.729 0.371 0.411 0.783 0.720 28.7 5.6 34.3 1.07 27.5 5.4 32.9
1924 CIN Rube Bressler 415 159 383 185 0.383 0.483 0.866 0.318 0.383 0.701 0.351 0.433 0.783 0.721 13.5 19.6 33.0 0.99 13.5 19.5 32.9
2007 HOU Lance Berkman 668 258 561 286 0.386 0.510 0.896 0.345 0.439 0.783 0.366 0.474 0.840 0.753 13.8 20.0 33.8 0.98 13.4 19.5 32.9
1914 IND Frank LaPorte 565 199 504 220 0.352 0.437 0.789 0.305 0.343 0.648 0.328 0.390 0.718 0.675 13.3 23.9 37.2 1.06 11.8 21.1 32.9
1974 MIN Larry Hisle 576 202 510 237 0.351 0.465 0.815 0.315 0.369 0.684 0.333 0.417 0.750 0.691 10.0 24.4 34.4 1.01 9.6 23.3 32.9
1925 NYA Earle Combs 674 273 593 274 0.405 0.462 0.867 0.354 0.415 0.769 0.379 0.439 0.818 0.757 17.3 14.6 31.9 0.98 17.8 15.1 32.9
1987 NYN Tim Teufel 351 138 299 163 0.393 0.545 0.938 0.326 0.417 0.743 0.359 0.481 0.841 0.728 11.8 19.2 31.0 0.96 12.5 20.4 32.9
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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).