Batters



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Showing page 169 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
2009 NYN Jeff Francoeur 308 104 289 144 0.338 0.498 0.836 0.316 0.401 0.718 0.327 0.450 0.777 0.735 8.1 25.2 33.3 0.99 8.2 25.5 33.7
1928 PHA Bing Miller 565 201 510 238 0.356 0.467 0.822 0.319 0.378 0.698 0.338 0.423 0.760 0.731 10.3 22.5 32.8 0.99 10.6 23.1 33.7
1944 PHA Bobby Estalella 574 212 506 207 0.369 0.409 0.778 0.310 0.345 0.655 0.340 0.377 0.717 0.672 17.1 16.0 33.1 0.99 17.4 16.3 33.7
1921 PHI Ed Konetchy 297 111 268 135 0.374 0.504 0.877 0.313 0.376 0.689 0.343 0.440 0.783 0.726 11.3 23.1 34.4 1.02 11.1 22.6 33.7
1934 PHI Johnny Moore 503 198 458 236 0.394 0.515 0.909 0.349 0.421 0.770 0.372 0.468 0.840 0.722 14.3 26.7 40.9 1.04 11.8 22.0 33.7
2011 PIT Andrew McCutchen 678 246 572 261 0.363 0.456 0.819 0.312 0.398 0.710 0.337 0.427 0.765 0.706 17.4 16.6 34.0 1.01 17.2 16.5 33.7
1979 SEA Bruce Bochte 636 244 554 273 0.384 0.493 0.876 0.341 0.413 0.754 0.362 0.453 0.815 0.739 13.7 22.1 35.8 1.04 12.9 20.8 33.7
2004 BAL Javy Lopez 638 236 579 291 0.370 0.503 0.872 0.324 0.428 0.753 0.347 0.465 0.813 0.769 14.4 21.8 36.2 1.06 13.4 20.2 33.6
1978 BOS Carlton Fisk 658 240 571 271 0.365 0.475 0.839 0.314 0.383 0.696 0.339 0.429 0.768 0.707 16.9 26.8 43.7 1.13 13.0 20.6 33.6
1950 CHN Hank Sauer 603 211 540 280 0.350 0.519 0.868 0.323 0.401 0.724 0.336 0.460 0.796 0.733 8.1 31.9 40.0 1.06 6.8 26.8 33.6
1999 CHN Henry Rodriguez 504 192 447 243 0.381 0.544 0.925 0.345 0.427 0.772 0.363 0.485 0.848 0.768 9.1 25.9 35.0 1.05 8.7 24.9 33.6
2002 CIN Austin Kearns 435 177 372 186 0.407 0.500 0.907 0.318 0.406 0.725 0.363 0.453 0.816 0.738 19.3 17.4 36.7 1.08 17.7 15.9 33.6
2012 CIN Ryan Ludwick 472 163 422 224 0.345 0.531 0.876 0.313 0.408 0.721 0.329 0.469 0.799 0.715 7.7 26.2 33.9 1.01 7.6 26.0 33.6
1937 CLE Hal Trosky 669 244 601 329 0.365 0.547 0.912 0.361 0.429 0.790 0.363 0.488 0.851 0.765 1.2 35.9 37.1 1.03 1.1 32.5 33.6
1980 CLE Joe Charboneau 512 183 453 221 0.357 0.488 0.845 0.316 0.385 0.702 0.337 0.437 0.774 0.727 10.6 22.9 33.5 1.00 10.6 23.0 33.6
1950 DET Johnny Groth 669 269 566 255 0.402 0.451 0.853 0.347 0.399 0.747 0.375 0.425 0.800 0.754 18.4 14.9 33.3 0.99 18.6 15.0 33.6
1982 MIN Kent Hrbek 591 214 532 258 0.362 0.485 0.847 0.328 0.393 0.721 0.345 0.439 0.784 0.727 10.1 24.8 34.9 1.02 9.7 23.9 33.6
2015 NYN Yoenis Cespedes 249 84 230 139 0.337 0.604 0.942 0.314 0.410 0.724 0.326 0.507 0.833 0.710 4.2 30.4 34.6 0.92 4.1 29.5 33.6
2019 OAK Mark Canha 497 197 410 212 0.396 0.517 0.913 0.322 0.451 0.773 0.359 0.484 0.843 0.761 18.3 13.9 32.2 1.00 19.1 14.5 33.6
1951 PHI Willie Jones 647 225 564 265 0.348 0.470 0.818 0.320 0.385 0.705 0.334 0.428 0.761 0.717 9.0 24.3 33.3 0.99 9.1 24.5 33.6
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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).