Game Time Adjustments

Posted on January 2, 2021 by DonH

Trea Turner getting pointers from hitting coach Kevin Long (Photo by Andrew Lang for TalkNats)

Don: This tweet got me thinking about looking at how batters perform as they face a pitcher multiple times in the same game, specifically if their results at the plate are better as the game goes on. Baseball Reference has splits for first, second, third, etc. PA/AB, but not by pitcher. Their split for Times Facing Opponent in Game is similar, but it includes all the PAs even if the batter only faced the pitcher once. So I decided to look at that myself.

Steve: I saw Jomboy’s tweet on Juan Soto as he saw a pitcher for a second time. Mid-game adjustments are so critical but it is also why some teams are pulling pitchers before a batter faces them for a 3rd time.

Don: Well, after figuring out more funky things about how the Game Day AtBats data (which is not actually just ABs) I was finally able to generate some interesting output.

Steve: What can be funky about an AB?

Don: There is a field called Event which supposedly describes the result for each plate appearance. But it has values like Stolen Base 2B, Pickoff . . . ., Passed Ball and so on. What those turned out to be is the plate appearance ended – due to nothing the batter did. For example, “Passed Ball” ended the inning and/or game because a base runner was thrown out trying to advance. So I had to add logic to not count those rows in the Game Day AtBat table as either a Plate Appearance or an At Bat.

What I decided to do was to calculate each Nat player’s slash line including only those PAs where a batter faced a pitcher two or more times in a game. And to summarize the data by how many times the batter had faced that pitcher.

Steve: I can’t wait to see what you came up with! The Nats might hire you to add to their analytics Pentagon!

Don: Thanks for the kinds words; but I am willing to bet the SAS analytics guys are already on top of all these issues. From my perspective they are much better than many of the folks here give them credit for.

So here are the results for the Nats for both 2019 and 2020.  I see a few patterns; but before I list them, I am curious about what jumps out to you. Note that for 2019 I only included those players who had a total of 100 PAs where the player saw the same pitcher at least twice in a game (and if a player saw two different pitchers at least twice each, all four are included but are numbered 1, 2 and 1,2 respectively). Given how many fewer games there were in 2020, the cutoff was 35.

2019 2020
PA# PAs ABs BA OBP SLG OPS PAs ABs BA OBP SLG OPS
All The Below Batters Combined
All 4016 3573 0.261 0.329 0.450 0.779 1200 1083 0.277 0.334 0.445 0.779
1 1568 1393 0.251 0.318 0.429 0.747 511 462 0.238 0.305 0.398 0.704
2 1568 1387 0.263 0.334 0.450 0.784 511 459 0.331 0.380 0.516 0.896
3 846 767 0.271 0.333 0.477 0.811 173 157 0.229 0.283 0.357 0.640
4 34 26 0.385 0.529 0.731 1.260 5 5 0.400 0.400 1.000 1.400
Adam Eaton
All 461 408 0.297 0.364 0.456 0.820 101 91 0.231 0.267 0.385 0.652
1 162 142 0.331 0.395 0.563 0.958 41 36 0.167 0.244 0.306 0.549
2 162 142 0.296 0.370 0.415 0.786 41 38 0.289 0.317 0.447 0.764
3 126 115 0.243 0.302 0.365 0.667 19 17 0.235 0.211 0.412 0.622
4 11 9 0.444 0.545 0.556 1.101 . . . . . .
Anthony Rendon
All 454 390 0.321 0.405 0.603 1.008 . . . . . .
1 164 141 0.298 0.378 0.475 0.853 . . . . . .
2 164 140 0.293 0.384 0.629 1.013 . . . . . .
3 122 106 0.387 0.467 0.745 1.212 . . . . . .
4 4 3 0.333 0.500 0.333 0.833 . . . . . .
Asdrubal Cabrera
All 96 82 0.280 0.344 0.488 0.832 113 99 0.232 0.292 0.404 0.696
1 38 32 0.188 0.237 0.281 0.518 47 43 0.209 0.255 0.465 0.720
2 38 31 0.323 0.421 0.645 1.066 47 38 0.263 0.362 0.368 0.730
3 19 18 0.389 0.421 0.611 1.032 19 18 0.222 0.211 0.333 0.544
4 1 1 0.000 0.000 0.000 0.000 . . . . . .
Brian Dozier
All 282 252 0.226 0.305 0.437 0.741 . . . . . .
1 116 109 0.183 0.233 0.312 0.545 . . . . . .
2 116 98 0.245 0.353 0.520 0.874 . . . . . .
3 50 45 0.289 0.360 0.556 0.916 . . . . . .
Brock Holt
All . . . . . . 37 36 0.250 0.270 0.361 0.631
1 . . . . . . 15 15 0.200 0.200 0.333 0.533
2 . . . . . . 15 14 0.357 0.400 0.500 0.900
3 . . . . . . 7 7 0.143 0.143 0.143 0.286
Carter Kieboom
All . . . . . . 65 55 0.218 0.338 0.218 0.557
1 . . . . . . 31 28 0.143 0.226 0.143 0.369
2 . . . . . . 31 25 0.280 0.419 0.280 0.699
3 . . . . . . 3 2 0.500 0.667 0.500 1.167
Eric Thames
All . . . . . . 76 67 0.209 0.303 0.284 0.586
1 . . . . . . 33 28 0.250 0.364 0.250 0.614
2 . . . . . . 33 30 0.200 0.273 0.267 0.539
3 . . . . . . 10 9 0.111 0.200 0.444 0.644
Gerardo Parra
All 92 88 0.284 0.315 0.511 0.827 . . . . . .
1 37 36 0.250 0.270 0.472 0.742 . . . . . .
2 37 35 0.314 0.351 0.514 0.866 . . . . . .
3 18 17 0.294 0.333 0.588 0.922 . . . . . .
Howie Kendrick
All 228 210 0.324 0.364 0.510 0.874 56 53 0.283 0.321 0.434 0.755
1 88 81 0.358 0.409 0.617 1.026 24 23 0.261 0.292 0.435 0.726
2 88 81 0.321 0.352 0.469 0.821 24 23 0.348 0.375 0.522 0.897
3 52 48 0.271 0.308 0.396 0.704 8 7 0.143 0.250 0.143 0.393
Josh Harrison
All . . . . . . 47 42 0.381 0.426 0.548 0.973
1 . . . . . . 21 17 0.353 0.476 0.529 1.006
2 . . . . . . 21 20 0.350 0.333 0.550 0.883
3 . . . . . . 4 4 0.500 0.500 0.500 1.000
4 . . . . . . 1 1 1.000 1.000 1.000 2.000
Juan Soto
All 447 373 0.284 0.398 0.574 0.972 116 90 0.400 0.534 0.744 1.279
1 166 129 0.295 0.446 0.605 1.050 46 38 0.289 0.413 0.526 0.939
2 166 140 0.293 0.398 0.600 0.998 46 34 0.559 0.674 0.971 1.645
3 113 103 0.252 0.319 0.466 0.785 23 17 0.294 0.478 0.588 1.066
4 2 1 1.000 1.000 4.000 5.000 1 1 1.000 1.000 4.000 5.000
Kurt Suzuki
All 204 183 0.268 0.333 0.475 0.809 69 59 0.254 0.333 0.407 0.740
1 84 77 0.234 0.286 0.442 0.727 31 26 0.231 0.323 0.269 0.592
2 84 75 0.320 0.393 0.547 0.940 31 26 0.346 0.419 0.654 1.073
3 36 31 0.226 0.306 0.387 0.693 7 7 0.000 0.000 0.000 0.000
Luis Garcia
All . . . . . . 76 75 0.267 0.276 0.347 0.623
1 . . . . . . 33 33 0.212 0.212 0.394 0.606
2 . . . . . . 33 32 0.313 0.333 0.313 0.646
3 . . . . . . 10 10 0.300 0.300 0.300 0.600
Matt Adams
All 179 162 0.241 0.307 0.494 0.801 . . . . . .
1 71 63 0.254 0.324 0.651 0.975 . . . . . .
2 71 65 0.231 0.296 0.400 0.696 . . . . . .
3 36 33 0.242 0.306 0.394 0.699 . . . . . .
4 1 1 0.000 0.000 0.000 0.000 . . . . . .
Michael A. Taylor
All . . . . . . 39 38 0.132 0.154 0.316 0.470
1 . . . . . . 18 18 0.111 0.111 0.333 0.444
2 . . . . . . 18 18 0.167 0.167 0.333 0.500
3 . . . . . . 3 2 0.000 0.333 0.000 0.333
Ryan Zimmerman
All 129 117 0.188 0.256 0.342 0.598 . . . . . .
1 51 49 0.224 0.255 0.388 0.643 . . . . . .
2 51 44 0.205 0.294 0.341 0.635 . . . . . .
3 27 24 0.083 0.185 0.250 0.435 . . . . . .
Trea Turner
All 417 389 0.290 0.333 0.463 0.796 157 147 0.320 0.363 0.592 0.955
1 145 139 0.281 0.310 0.374 0.684 61 56 0.232 0.295 0.482 0.777
2 145 129 0.279 0.345 0.488 0.833 61 58 0.431 0.459 0.828 1.287
3 115 112 0.304 0.322 0.500 0.822 33 31 0.290 0.333 0.387 0.720
4 12 9 0.444 0.583 1.000 1.583 2 2 0.000 0.000 0.000 0.000
Victor Robles
All 380 340 0.250 0.318 0.403 0.721 94 86 0.244 0.277 0.337 0.614
1 157 143 0.252 0.312 0.378 0.690 44 40 0.200 0.273 0.250 0.523
2 157 142 0.239 0.306 0.380 0.686 44 40 0.300 0.295 0.450 0.745
3 63 53 0.283 0.365 0.547 0.912 6 6 0.167 0.167 0.167 0.333
4 3 2 0.000 0.333 0.000 0.333 . . . . . .
Yan Gomes
All 211 183 0.208 0.308 0.366 0.674 62 57 0.386 0.403 0.596 1.000
1 90 81 0.148 0.233 0.296 0.530 27 25 0.440 0.481 0.800 1.281
2 90 77 0.260 0.356 0.390 0.745 27 25 0.360 0.333 0.480 0.813
3 31 25 0.240 0.387 0.520 0.907 8 7 0.286 0.375 0.286 0.661

Steve: Well we can see why the Nats were so successful in 2019 and the ability to never be out of a game, and then we see why 2020 was a disaster for the Nats. I wonder why Soto drops back some in his third at-bat. Still a very strong OPS but the BA fell a lot.

Don: There were a number of players who were better (some much better) at the plate the 2nd, 3rd, and 4th time they saw the same pitcher in 2019. Rendon (sigh), Cabrera, Parra, Turner among them. I was surprised that Soto did not follow that pattern in 2019; likewise, the older players (e.g., Howie and Zim) did not – perhaps the issue of rest played into that?????

Soto really turned it around in 2020 however. Harrison also showed he was learning and getting better each time they faced the same pitcher. I also found it interesting that both Kieboom and Garcia showed that same pattern in 2020 – that is somewhat encouraging.

There was more of a drop-off once a batter got to the third time. Almost like they lost interest or weren’t having as much fun as they did in 2019: the Parra effect perhaps???

Steve: I’m glad to see that Kieboom and Luis Garcia both improved after their first plate appearances.

Can you run a similar report for these players who have been discussed here as perhaps options: Zim, Droobs, Tyler Flowers, Curt Casali, Jason Castro, Kyle Schwarber, Justin Turner, Eugenio Suarez, Yasiel Puig, Marcell Ozuna, Adam Duvall, DJ LeMahieu, Joc Pederson, Michael Brantley, Tommy La Stella and Didi Gregorius?

Don: I assume you included Zim and Droobs so their numbers can be compared with these other players without having to scroll too much.

I am also going to add Kris Bryant to the list as he has been discussed as an option and given what the Cubs are doing, maybe he is a possibility. And I will also include Kieboom and Garcia so they can be compared with these guys.

To make sure all of these players are included, I am going to remove the constraint about at least 35 such PAs in 2020 and 100 such PAs in 2019 that I used to limit the Nats players to guys who played somewhat regularly.

2019 2020
PA PA AB BA OBP SLG OPS PA AB BA OBP SLG OPS
All The Below Batters Combined
All 4964 4447 0.277 0.345 0.512 0.857 1842 1638 0.260 0.335 0.477 0.812
1 1886 1688 0.257 0.327 0.481 0.808 754 681 0.247 0.314 0.488 0.802
2 1886 1683 0.285 0.355 0.510 0.865 754 660 0.277 0.357 0.461 0.817
3 1151 1040 0.295 0.358 0.564 0.922 323 287 0.261 0.341 0.509 0.849
4 41 36 0.250 0.341 0.528 0.869 11 10 0.000 0.091 0.000 0.091
Adam Duvall
All 78 74 0.270 0.295 0.541 0.835 125 114 0.228 0.288 0.526 0.814
1 30 28 0.143 0.200 0.393 0.593 53 50 0.220 0.264 0.440 0.704
2 30 30 0.367 0.367 0.567 0.933 53 46 0.261 0.340 0.565 0.905
3 18 16 0.313 0.333 0.750 1.083 19 18 0.167 0.211 0.667 0.877
Andrew Stevenson
All 7 6 0.167 0.286 0.167 0.452 30 27 0.407 0.467 0.815 1.281
1 3 2 0.000 0.333 0.000 0.333 12 10 0.500 0.583 0.800 1.383
2 3 3 0.000 0.000 0.000 0.000 12 11 0.455 0.500 0.909 1.409
3 1 1 1.000 1.000 1.000 2.000 5 5 0.200 0.200 0.800 1.000
4 . . . . . . 1 1 0.000 0.000 0.000 0.000
Asdrubal Cabrera
All 311 271 0.277 0.344 0.487 0.831 113 99 0.232 0.292 0.404 0.696
1 129 109 0.275 0.349 0.459 0.808 47 43 0.209 0.255 0.465 0.720
2 129 110 0.255 0.341 0.491 0.832 47 38 0.263 0.362 0.368 0.730
3 52 51 0.333 0.346 0.549 0.895 19 18 0.222 0.211 0.333 0.544
4 1 1 0.000 0.000 0.000 0.000 . . . . . .
Carter Kieboom
All 23 22 0.045 0.087 0.045 0.132 65 55 0.218 0.338 0.218 0.557
1 10 10 0.000 0.000 0.000 0.000 31 28 0.143 0.226 0.143 0.369
2 10 9 0.111 0.200 0.111 0.311 31 25 0.280 0.419 0.280 0.699
3 3 3 0.000 0.000 0.000 0.000 3 2 0.500 0.667 0.500 1.167
Curt Casali
All 132 119 0.218 0.280 0.319 0.600 44 39 0.205 0.295 0.308 0.603
1 57 51 0.157 0.211 0.255 0.465 22 19 0.316 0.409 0.368 0.778
2 57 52 0.212 0.281 0.288 0.569 22 20 0.100 0.182 0.250 0.432
3 18 16 0.438 0.500 0.625 1.125 . . . . . .
DJ LeMahieu
All 430 388 0.330 0.388 0.552 0.940 147 132 0.364 0.415 0.614 1.029
1 156 137 0.299 0.378 0.540 0.918 59 54 0.352 0.407 0.667 1.073
2 156 143 0.392 0.436 0.587 1.023 59 51 0.373 0.424 0.608 1.032
3 113 103 0.282 0.336 0.515 0.851 28 26 0.385 0.429 0.538 0.967
4 5 5 0.400 0.400 0.600 1.000 1 1 0.000 0.000 0.000 0.000
Didi Gregorius
All 212 205 0.273 0.297 0.507 0.804 130 122 0.295 0.323 0.607 0.930
1 84 81 0.333 0.357 0.519 0.876 55 52 0.308 0.327 0.731 1.058
2 84 82 0.220 0.238 0.451 0.689 55 51 0.314 0.345 0.529 0.875
3 44 42 0.262 0.295 0.595 0.891 20 19 0.211 0.250 0.474 0.724
Eugenio Suarez
All 440 383 0.277 0.361 0.590 0.951 150 129 0.202 0.307 0.473 0.780
1 157 141 0.348 0.414 0.766 1.180 59 51 0.176 0.288 0.431 0.720
2 157 134 0.224 0.318 0.500 0.818 59 49 0.245 0.356 0.510 0.866
3 118 102 0.255 0.347 0.480 0.828 32 29 0.172 0.250 0.483 0.733
4 8 6 0.167 0.375 0.333 0.708 . . . . . .
Jason Castro
All 155 138 0.254 0.329 0.507 0.836 45 42 0.190 0.244 0.381 0.625
1 68 60 0.283 0.353 0.533 0.886 22 20 0.100 0.182 0.200 0.382
2 68 60 0.233 0.324 0.467 0.790 22 21 0.286 0.318 0.571 0.890
3 19 18 0.222 0.263 0.556 0.819 1 1 0.000 0.000 0.000 0.000
Joc Pederson
All 343 303 0.261 0.347 0.581 0.928 99 88 0.193 0.283 0.398 0.681
1 120 109 0.275 0.342 0.615 0.956 41 38 0.158 0.220 0.579 0.798
2 120 102 0.245 0.358 0.529 0.888 41 37 0.297 0.366 0.351 0.717
3 93 84 0.274 0.344 0.619 0.963 17 13 0.000 0.235 0.000 0.235
4 10 8 0.125 0.300 0.375 0.675 . . . . . .
Justin Turner
All 357 316 0.307 0.375 0.544 0.920 143 126 0.325 0.399 0.563 0.962
1 129 112 0.241 0.318 0.375 0.693 56 52 0.365 0.393 0.654 1.047
2 129 113 0.389 0.465 0.743 1.208 56 50 0.280 0.357 0.400 0.757
3 99 91 0.286 0.333 0.505 0.839 30 24 0.333 0.467 0.708 1.175
4 . . . . . . 1 0 . 1.000 . 1.000
Kris Bryant
All 416 362 0.290 0.377 0.536 0.913 90 81 0.160 0.244 0.222 0.467
1 153 136 0.221 0.301 0.368 0.668 34 31 0.161 0.235 0.226 0.461
2 153 130 0.300 0.399 0.538 0.937 34 31 0.161 0.235 0.258 0.493
3 108 94 0.372 0.454 0.777 1.230 22 19 0.158 0.273 0.158 0.431
4 2 2 0.500 0.500 0.500 1.000 . . . . . .
Kyle Schwarber
All 374 322 0.270 0.364 0.559 0.923 135 111 0.135 0.289 0.297 0.586
1 142 124 0.266 0.352 0.548 0.900 55 43 0.186 0.364 0.442 0.805
2 142 117 0.265 0.380 0.547 0.927 55 47 0.106 0.236 0.191 0.428
3 84 75 0.267 0.345 0.560 0.905 25 21 0.095 0.240 0.238 0.478
4 6 6 0.500 0.500 1.000 1.500 . . . . . .
Marcell Ozuna
All 370 335 0.239 0.308 0.469 0.777 189 169 0.337 0.407 0.675 1.082
1 140 127 0.220 0.286 0.480 0.766 74 69 0.304 0.351 0.594 0.946
2 140 129 0.240 0.300 0.434 0.734 74 65 0.354 0.432 0.723 1.156
3 89 78 0.269 0.360 0.513 0.872 40 34 0.382 0.475 0.765 1.240
4 1 1 0.000 0.000 0.000 0.000 1 1 0.000 0.000 0.000 0.000
Michael Brantley
All 433 396 0.308 0.363 0.495 0.858 136 127 0.307 0.353 0.488 0.841
1 161 140 0.236 0.329 0.393 0.722 54 51 0.275 0.315 0.392 0.707
2 161 152 0.368 0.398 0.572 0.970 54 49 0.367 0.426 0.592 1.018
3 107 101 0.327 0.364 0.535 0.899 24 23 0.304 0.333 0.565 0.899
4 4 3 0.000 0.250 0.000 0.250 4 4 0.000 0.000 0.000 0.000
Ryan Zimmerman
All 129 117 0.188 0.256 0.342 0.598 . . . . . .
1 51 49 0.224 0.255 0.388 0.643 . . . . . .
2 51 44 0.205 0.294 0.341 0.635 . . . . . .
3 27 24 0.083 0.185 0.250 0.435 . . . . . .
Tommy La Stella
All 200 187 0.299 0.345 0.513 0.858 152 133 0.278 0.355 0.421 0.776
1 76 75 0.253 0.263 0.480 0.743 57 49 0.245 0.333 0.449 0.782
2 76 70 0.343 0.395 0.571 0.966 57 49 0.265 0.351 0.306 0.657
3 46 40 0.300 0.391 0.400 0.791 35 32 0.375 0.429 0.594 1.022
4 2 2 0.500 0.500 2.000 2.500 3 3 0.000 0.000 0.000 0.000
Tyler Flowers
All 171 155 0.213 0.275 0.413 0.688 49 44 0.205 0.286 0.341 0.627
1 75 70 0.171 0.227 0.300 0.527 23 21 0.095 0.174 0.286 0.460
2 75 68 0.265 0.320 0.529 0.849 23 20 0.250 0.348 0.300 0.648
3 21 17 0.176 0.286 0.412 0.697 3 3 0.667 0.667 1.000 1.667
Yasiel Puig
All 383 348 0.290 0.352 0.503 0.855 . . . . . .
1 145 127 0.276 0.366 0.496 0.862 . . . . . .
2 145 135 0.252 0.303 0.363 0.666 . . . . . .
3 91 84 0.381 0.418 0.750 1.168 . . . . . .
4 2 2 0.000 0.000 0.000 0.000 . . . . . .

In terms of getting better as the games go on, it appears that DJ LeMahieu, Turner, Bryant (in 2019), Ozuna, Brantley, La Stella, Flowers and Puig (in 2019 since he did not play in 2020) seems to do the best. La Stella and Flowers are somewhat of a surprise in that group.

It also seems like a number of players (e.g., Didi, ) do better the second time, but drop off later. I wonder if that could be because teams are pulling all but the elite pitchers before they go thru the complete lineup three times???? If that is the case, then they players who did not drop off the third time around, are all the more valuable.

These results are not encouraging for anyone advocating for Suarez or Schwarber.

Steve: The Schwarber numbers are mind-boggling, and not in a good way. I just love the LeMahieu numbers. One thing you need is to start with a good overall slash and analyze what you have to see if there is a set pattern. LeMahieu is just a darn good player.

Don: Well I hope you are a bit less interested in Schwarber now. And I agree on LeMahieu, but I seriously doubt he will be a Nat next year.

Based on these results I am a bit more interested in Brantley, La Stella, Flowers, Puig (and yes, I do have the standard disclaimer on Puig) and perhaps Duvall as FAs. Other than Brantley who will expect and get a multi-year deal with an AAV in the teens, the other four could be very affordable.

And, of course, if the Cubs decide to dump Bryant’s salary, I would be for that. But I am not giving them Kieboom – especially after seeing these results which could be an indication that he is learning – albeit more slowly than I’d like.

Steve: We got spoiled by players like Bryce Harper and Juan Soto who were teenage stars and Kieboom deserves the time to develop.  I would love Brantley if he was signed alongside Duvall or Kevin Pillar to take some platoon at-bats. Yes, LeMahieu is a long-shot based on the dollars. Justin Turner is another who could allow the Nats to take their time with Kieboom.

Don: And the good news is that I think I am closing in on having better understanding of the good and the bad about the Game Day data. If anyone wants to see this report for another player, it should not take me long at all to generate it.

Steve: You’ve done some great analysis. It really is great stuff in evaluating a potential lineup. Now with these numbers we can finally call 2020 as last year.

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