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Showing page 149 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
1973 CHN Rick Monday 651 241 554 260 0.370 0.469 0.840 0.326 0.381 0.706 0.348 0.425 0.773 0.694 14.5 25.9 40.5 1.04 13.0 23.2 36.3
1961 CHN Ron Santo 655 237 578 277 0.362 0.479 0.841 0.314 0.399 0.713 0.338 0.439 0.777 0.728 15.6 23.5 39.1 1.03 14.5 21.8 36.3
1954 CLE Al Smith 583 230 481 209 0.395 0.435 0.829 0.323 0.368 0.691 0.359 0.401 0.760 0.700 20.8 15.4 36.2 1.04 20.9 15.4 36.3
1984 DET Chet Lemon 574 204 509 252 0.355 0.495 0.850 0.317 0.399 0.716 0.336 0.447 0.783 0.722 11.2 24.4 35.7 0.96 11.4 24.8 36.3
2010 LAN Andre Ethier 585 213 517 255 0.364 0.493 0.857 0.322 0.400 0.723 0.343 0.447 0.790 0.720 12.1 23.7 35.8 1.00 12.3 24.0 36.3
1987 MIL Robin Yount 723 275 635 304 0.380 0.479 0.859 0.327 0.419 0.746 0.354 0.449 0.802 0.756 19.3 18.5 37.7 1.01 18.6 17.8 36.3
2014 NYN Lucas Duda 596 208 514 247 0.349 0.481 0.830 0.316 0.380 0.696 0.333 0.430 0.763 0.691 9.7 25.5 35.2 0.95 10.0 26.3 36.3
1954 PHA Jim Finigan 560 211 487 205 0.377 0.421 0.798 0.309 0.352 0.660 0.343 0.386 0.729 0.700 19.0 17.1 36.1 1.00 19.1 17.2 36.3
1978 TEX Al Oliver 568 203 525 257 0.357 0.490 0.847 0.327 0.384 0.711 0.342 0.437 0.779 0.707 8.6 28.1 36.7 0.94 8.5 27.8 36.3
2017 TOR Justin Smoak 637 226 560 296 0.355 0.529 0.883 0.330 0.428 0.758 0.342 0.478 0.821 0.752 7.9 28.2 36.1 1.00 7.9 28.4 36.3
2010 TOR Vernon Wells 646 214 590 304 0.331 0.515 0.847 0.314 0.403 0.717 0.323 0.459 0.782 0.732 5.6 33.1 38.7 1.02 5.3 31.0 36.3
1932 WS1 Heinie Manush 677 254 625 324 0.375 0.518 0.894 0.353 0.424 0.778 0.364 0.471 0.836 0.745 7.5 29.2 36.6 1.01 7.4 29.0 36.3
1976 BAL Bobby Grich 615 227 518 216 0.369 0.417 0.786 0.306 0.351 0.658 0.338 0.384 0.722 0.677 19.2 17.0 36.3 1.01 19.1 17.0 36.2
1980 BAL Ken Singleton 680 270 583 283 0.397 0.485 0.882 0.345 0.422 0.768 0.371 0.454 0.825 0.727 17.6 18.6 36.2 1.00 17.6 18.6 36.2
2023 CHA Luis Robert 595 186 546 296 0.313 0.542 0.855 0.309 0.413 0.722 0.311 0.478 0.788 0.728 1.3 35.4 36.7 1.03 1.3 34.9 36.2
1942 CHN Lou Novikoff 513 171 483 202 0.333 0.418 0.752 0.287 0.316 0.603 0.310 0.367 0.677 0.656 11.7 24.9 36.6 1.02 11.6 24.6 36.2
1999 CIN Greg Vaughn 643 223 550 294 0.347 0.535 0.881 0.329 0.424 0.753 0.338 0.479 0.817 0.768 5.6 30.8 36.3 1.03 5.6 30.7 36.2
1937 DET Gee Walker 683 259 635 317 0.379 0.499 0.878 0.337 0.405 0.742 0.358 0.452 0.810 0.765 14.5 30.1 44.6 1.06 11.8 24.4 36.2
1919 DET Ira Flagstead 342 137 288 138 0.401 0.479 0.880 0.308 0.345 0.654 0.354 0.412 0.767 0.681 15.8 19.5 35.3 0.93 16.2 20.0 36.2
1956 MLN Eddie Mathews 651 242 552 286 0.372 0.518 0.890 0.337 0.430 0.767 0.354 0.474 0.828 0.718 11.3 24.6 35.9 1.00 11.4 24.8 36.2
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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).