Batters



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Showing page 153 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
1972 MIN Bobby Darwin 562 183 513 227 0.326 0.442 0.768 0.293 0.333 0.626 0.309 0.388 0.697 0.645 9.4 28.3 37.7 1.04 8.9 26.8 35.7
1973 MON Ron Fairly 505 212 413 189 0.420 0.458 0.877 0.330 0.385 0.715 0.375 0.421 0.796 0.694 22.5 15.4 37.9 1.03 21.2 14.5 35.7
1977 MON Tony Perez 633 223 559 259 0.352 0.463 0.816 0.308 0.385 0.693 0.330 0.424 0.754 0.721 14.2 21.9 36.1 1.01 14.0 21.7 35.7
1952 NY1 Al Dark 650 229 589 254 0.352 0.431 0.784 0.305 0.360 0.665 0.329 0.396 0.724 0.693 15.5 21.5 36.9 1.02 15.0 20.8 35.7
1921 NY1 Ross Youngs 594 237 504 232 0.399 0.460 0.859 0.332 0.398 0.730 0.366 0.429 0.795 0.726 20.0 16.2 36.2 1.03 19.7 16.0 35.7
1978 NYA Reggie Jackson 581 207 510 244 0.356 0.478 0.835 0.321 0.373 0.694 0.339 0.426 0.764 0.707 10.2 27.1 37.3 1.02 9.8 25.9 35.7
2019 OAK Matt Olson 547 192 483 263 0.351 0.545 0.896 0.319 0.438 0.757 0.335 0.491 0.826 0.761 8.7 26.0 34.6 1.00 9.0 26.8 35.7
1913 PHI Beals Becker 336 122 307 165 0.363 0.537 0.901 0.338 0.380 0.717 0.350 0.459 0.809 0.671 5.4 28.4 33.8 1.09 5.7 30.0 35.7
1926 PHI Cy Williams 384 158 336 191 0.411 0.568 0.980 0.337 0.412 0.749 0.374 0.490 0.865 0.713 14.3 26.6 40.9 1.05 12.5 23.2 35.7
1963 PIT Donn Clendenon 614 199 563 242 0.324 0.430 0.754 0.289 0.345 0.634 0.307 0.387 0.694 0.666 10.8 24.0 34.8 0.99 11.1 24.6 35.7
1914 PTF Steve Yerkes 159 60 142 70 0.377 0.493 0.870 0.300 0.329 0.628 0.339 0.411 0.749 0.675 13.3 21.3 34.6 0.99 13.7 22.0 35.7
1987 SEA Ken Phelps 424 174 332 182 0.410 0.548 0.959 0.335 0.428 0.763 0.373 0.488 0.861 0.756 16.0 20.0 36.0 1.01 15.9 19.8 35.7
1984 SFN Dan Gladden 384 155 342 153 0.404 0.447 0.851 0.301 0.357 0.657 0.352 0.402 0.754 0.685 19.7 15.6 35.3 1.00 19.9 15.8 35.7
1941 SLA Roy Cullenbine 629 282 501 233 0.448 0.465 0.913 0.367 0.422 0.789 0.408 0.444 0.851 0.726 25.5 10.3 35.8 1.03 25.4 10.3 35.7
1972 SLN Joe Torre 613 219 544 228 0.357 0.419 0.776 0.298 0.353 0.652 0.328 0.386 0.714 0.676 18.1 17.9 36.1 1.01 17.9 17.7 35.7
1978 ATL Gary Matthews 542 198 474 219 0.365 0.462 0.827 0.303 0.362 0.665 0.334 0.412 0.746 0.688 16.9 23.4 40.3 1.08 14.9 20.7 35.6
2017 BAL Jonathan Schoop 675 228 622 313 0.338 0.503 0.841 0.311 0.420 0.732 0.325 0.462 0.786 0.752 8.9 25.6 34.6 0.99 9.2 26.3 35.6
1915 BSN Sherry Magee 655 221 571 223 0.337 0.391 0.728 0.291 0.318 0.609 0.314 0.354 0.669 0.630 15.1 20.7 35.9 1.01 15.0 20.5 35.6
1983 CAL Bobby Grich 477 196 387 178 0.411 0.460 0.871 0.318 0.399 0.716 0.364 0.429 0.794 0.726 22.2 12.2 34.4 0.98 23.0 12.6 35.6
1992 CHN Andre Dawson 582 184 542 247 0.316 0.456 0.772 0.293 0.355 0.648 0.304 0.405 0.710 0.679 6.7 27.2 34.0 0.99 7.0 28.5 35.6
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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).