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Showing page 175 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
1967 NYN Tommy Davis 621 212 577 254 0.341 0.440 0.782 0.301 0.365 0.666 0.321 0.403 0.724 0.669 12.6 21.4 34.0 1.02 12.2 20.7 32.9
1982 PHI Bo Diaz 572 190 525 236 0.332 0.450 0.782 0.303 0.371 0.674 0.318 0.410 0.728 0.688 8.4 20.6 29.0 0.96 9.5 23.4 32.9
1923 PHI Cotton Tierney 513 178 481 218 0.347 0.453 0.800 0.318 0.369 0.686 0.332 0.411 0.743 0.730 9.7 21.9 31.7 1.10 10.1 22.7 32.9
1969 PHI Larry Hisle 537 181 482 221 0.337 0.459 0.796 0.301 0.360 0.661 0.319 0.409 0.728 0.684 9.5 23.9 33.5 1.02 9.3 23.5 32.9
1982 BAL Gary Roenicke 477 185 393 196 0.388 0.499 0.887 0.324 0.406 0.730 0.356 0.452 0.808 0.727 15.2 18.0 33.2 0.96 15.0 17.8 32.8
1951 BRO Duke Snider 672 230 606 293 0.342 0.483 0.826 0.337 0.399 0.736 0.339 0.441 0.781 0.717 1.9 25.8 27.7 0.97 2.2 30.6 32.8
1992 CHA Robin Ventura 694 260 592 255 0.375 0.431 0.805 0.328 0.375 0.703 0.351 0.403 0.754 0.711 16.2 16.8 32.9 1.01 16.2 16.7 32.8
2012 CHN Alfonso Soriano 615 198 561 280 0.322 0.499 0.821 0.305 0.399 0.704 0.314 0.449 0.763 0.715 5.3 28.3 33.6 1.03 5.2 27.6 32.8
1923 CHN Bernie Friberg 617 222 547 260 0.360 0.475 0.835 0.324 0.378 0.702 0.342 0.427 0.769 0.730 10.9 26.7 37.6 1.08 9.5 23.3 32.8
1976 CIN Tony Perez 586 192 527 238 0.328 0.452 0.779 0.306 0.349 0.655 0.317 0.400 0.717 0.677 6.2 26.8 33.0 1.01 6.2 26.6 32.8
1927 CLE George Burns 607 224 549 239 0.369 0.435 0.804 0.323 0.386 0.709 0.346 0.411 0.757 0.739 13.9 13.8 27.9 0.95 16.4 16.2 32.8
1936 DET Al Simmons 620 237 568 275 0.382 0.484 0.866 0.351 0.410 0.761 0.367 0.447 0.814 0.779 9.5 21.0 30.5 0.99 10.2 22.6 32.8
1959 DET Charlie Maxwell 611 217 518 239 0.355 0.461 0.817 0.320 0.363 0.683 0.338 0.412 0.750 0.703 10.7 25.5 36.2 1.03 9.7 23.1 32.8
2001 HOU Craig Biggio 717 274 617 281 0.382 0.455 0.838 0.320 0.421 0.741 0.351 0.438 0.789 0.753 22.3 11.1 33.4 1.01 21.9 10.9 32.8
1955 KC1 Elmer Valo 338 155 283 137 0.459 0.484 0.943 0.351 0.387 0.738 0.405 0.435 0.840 0.713 18.1 13.5 31.7 0.97 18.7 14.0 32.8
1963 LAA Albie Pearson 684 271 578 230 0.396 0.398 0.794 0.319 0.382 0.701 0.358 0.390 0.748 0.688 26.4 4.8 31.2 0.97 27.8 5.0 32.8
2019 LAN Max Muncy 589 220 487 251 0.374 0.515 0.889 0.325 0.439 0.764 0.349 0.477 0.826 0.752 14.3 18.5 32.7 1.00 14.3 18.6 32.8
2023 MIA Jorge Soler 580 198 504 258 0.341 0.512 0.853 0.317 0.411 0.728 0.329 0.461 0.791 0.739 6.8 25.9 32.7 0.99 6.8 26.0 32.8
1989 MON Tim Raines 618 244 517 216 0.395 0.418 0.813 0.327 0.373 0.700 0.361 0.396 0.757 0.674 20.9 11.7 32.6 0.99 21.0 11.8 32.8
1916 NY1 Dave Robertson 619 195 587 250 0.315 0.426 0.741 0.296 0.335 0.631 0.305 0.380 0.686 0.623 5.9 26.9 32.8 1.00 5.9 26.9 32.8
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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).