# Platoon Hitter Projections

In my previous article, I found that left-handed platoon hitters who start games with the handedness advantage can expect to have that handedness advantage in 84.9 percent of their plate appearances in those games. Right-handed platoon hitters maintain their handedness advantage in 52.6 percent of their plate appearances in those games. Those percentages imply that relying on vs. left-handed pitcher and vs. right-handed pitcher split statistics to project the performance of platoon hitters in their starts with the platoon advantage overestimates their production in those starts. I wanted to see if I could do better based on my research, and so I created a (Marcel-) simple projection system.

Here’s how the system works. From 2004-2014, I identified every platoon hitter each season based on the thresholds of having at least 89 percent of total plate appearances come against opposite-handed pitchers for left-handed hitters and 45 percent for right-handed hitters. Next, I calculated those platoon hitters’ hits per plate appearance, doubles per plate appearance, triples per plate appearance, home runs per plate appearance, strikeouts per plate appearance, walks per plate appearance, and hit by pitches per plate appearance in those seasons and the two preceding seasons. I then weighted those three seasons using the Marcel weights of 5 for the most recent season, 4 for the year prior, and 3 for two years prior. That’s the projection.

I wanted to test whether those projections would beat similar projections based on vs. left-handed pitcher and vs. right-handed pitcher split stats—which does not account for the fact that platoon hitters are sometimes left in the game after the starter is pulled to face a same-handed reliever—and so I ran correlations of both split stat projections and my weighted split stat projections to predict actual hits per plate appearance, doubles per plate appearance, etc., in the following season. For each test, I required a minimum of 50 plate appearances in the predicted season, which created a sample of 495 total batters.

First, here are the correlations of the split stat projections.

Correlations Based on Split Stats
 N H 2B 3B HR K BB HBP LHB 227 0.268 0.215 0.361 0.510 0.746 0.504 0.574 RHB 268 0.279 0.135 0.139 0.403 0.696 0.446 0.486 All 495 0.289 0.164 0.252 0.443 0.715 0.480 0.514

Next, here are the correlations of the weighted split stat projections.

Correlations Based on Weighted Splits
 N H 2B 3B HR K BB HBP LHB 227 0.269 0.223 0.362 0.507 0.749 0.506 0.606 RHB 268 0.286 0.095 0.159 0.471 0.721 0.511 0.527 All 495 0.292 0.146 0.274 0.487 0.734 0.540 0.556

Finally, so you don’t have to scroll back and forth, here is the difference between the two.

 N H 2B 3B HR K BB HBP LHB 227 0.001 0.008 0.001 -0.003 0.003 0.002 0.033 RHB 268 0.007 -0.040 0.020 0.068 0.024 0.065 0.042 All 495 0.003 -0.018 0.022 0.044 0.020 0.060 0.042

The weighted split stat projections were not universally better. They lost on doubles for right-handed hitters and on home runs for left-handed hitters. But, overall, the weightings improved the projections in 12 of 14 tested stats (split by batter handedness).

Now that I’m confident that the weighted split projections are worthwhile, I’ve created 2016 projections for the platoon hitters from 2015. Because per-plate appearance statistics can be sort of hard to interpret, I’ve scaled everyone’s numbers to 600 plate appearances. They won’t actually get 600 plate appearances; it’s just to give you an idea of the quality of the player you can expect to have in his starts against opposite-handed starting pitchers.

Projected Stats in Starts with Platoon Adv. – Scaled to 600 PA
 Hitter Bats H 2B 3B HR K BB HBP BAVG OBP SLG OPS Franklin Gutierrez R 154.2 32.7 0.0 45.2 171.8 36.6 6.4 .277 .329 .579 .908 Kiké Hernandez R 167.9 37.5 8.8 18.3 113.3 43.2 5.2 .304 .361 .504 .864 David Peralta L 167.7 27.2 13.1 18.4 115.1 43.3 3.2 .303 .357 .499 .856 Steve Pearce R 135.6 32.7 1.2 28.6 114.6 54.7 10.1 .253 .334 .479 .813 Danny Valencia R 158.7 37.9 2.0 21.3 120.8 40.3 2.1 .285 .335 .474 .810 Michael Conforto L 143.3 39.8 0.0 25.6 120.6 51.6 2.8 .263 .330 .477 .806 Ben Paulsen L 155.7 32.0 5.7 21.3 158.8 36.3 4.0 .278 .327 .470 .796 Scott Van Slyke R 135.7 32.4 0.8 21.4 154.8 65.3 8.9 .258 .350 .445 .795 Justin Bour L 145.1 26.2 0.0 28.0 135.9 48.3 2.3 .264 .326 .464 .790 Andre Ethier L 146.5 29.7 7.4 13.7 105.1 58.5 7.2 .274 .354 .434 .788 Clint Robinson L 147.1 25.2 1.6 15.9 90.1 63.6 7.9 .278 .364 .422 .787 Wilin Rosario R 161.4 35.8 0.8 23.1 115.8 28.1 0.8 .283 .317 .469 .786 Darin Ruf R 133.1 26.1 0.0 26.1 151.5 53.7 13.9 .250 .335 .446 .781 Matt Adams L 157.5 33.5 2.9 19.9 127.5 32.7 1.7 .278 .320 .454 .773 John Jaso L 139.4 35.0 2.1 12.2 108.9 69.6 7.8 .267 .361 .412 .773 Chris Coghlan L 139.6 32.8 7.3 14.6 113.3 61.0 3.7 .261 .341 .431 .772 Seth Smith L 133.6 38.7 4.8 14.2 121.2 67.7 4.8 .253 .344 .426 .769 Chris Carter R 112.5 22.8 0.7 33.1 198.1 71.9 5.7 .215 .317 .452 .768 Pedro Alvarez L 127.8 20.8 1.0 31.6 163.1 56.1 3.1 .236 .312 .454 .766 Ryan Raburn R 133.1 32.2 1.6 22.2 146.0 53.9 7.3 .247 .324 .436 .760 Derek Dietrich L 126.1 26.5 6.3 20.3 132.2 42.6 26.1 .237 .325 .425 .750 Rajai Davis R 153.7 31.2 9.4 12.3 111.2 33.4 5.2 .274 .321 .429 .749 Jake Smolinski R 130.1 23.9 5.3 20.3 126.5 52.2 10.4 .242 .321 .420 .741 Carl Crawford L 160.0 30.2 4.8 11.0 97.1 30.5 4.9 .283 .326 .412 .738 Joey Butler R 151.2 27.0 0.0 17.1 181.5 37.7 5.1 .271 .323 .412 .735 Brandon Guyer R 138.5 31.9 2.7 10.0 99.2 36.6 31.7 .260 .345 .387 .732 Ryan Howard L 129.7 29.2 1.7 23.6 163.5 47.3 5.0 .237 .303 .426 .729 Chris Young R 123.9 33.0 2.1 20.6 124.3 57.5 5.3 .231 .311 .415 .726 Alejandro De Aza L 140.4 26.7 7.8 12.1 133.8 46.8 6.7 .257 .323 .401 .724 Tyler Collins L 148.5 29.4 7.3 11.7 117.8 33.6 2.4 .263 .308 .404 .711 Kelly Johnson L 131.7 21.6 2.0 21.5 144.5 49.4 2.5 .240 .306 .405 .711 Miguel Montero L 126.7 20.0 0.0 17.0 129.5 66.7 7.4 .241 .335 .376 .711 Shane Victorino R 145.4 23.4 3.9 11.2 88.6 34.8 16.0 .265 .327 .383 .710 Paulo Orlando R 145.3 32.6 13.8 15.7 116.6 13.1 3.8 .249 .270 .434 .704 Scooter Gennett L 155.1 31.2 5.1 10.5 100.8 22.5 3.4 .270 .302 .397 .699 Conor Gillaspie L 141.4 31.1 5.0 11.3 101.2 41.4 2.6 .254 .309 .389 .698 Nolan Reimold R 124.9 19.6 2.0 19.4 172.5 59.7 3.0 .232 .313 .385 .698 A.J. Ellis R 111.5 23.8 0.3 14.5 98.1 89.7 3.9 .220 .342 .354 .696 Travis Snider L 130.4 26.3 3.4 14.3 136.4 55.5 2.9 .241 .315 .381 .696 Tim Beckham R 123.5 18.4 10.7 23.8 181.4 34.0 7.7 .221 .275 .420 .695 David Murphy L 141.3 30.8 1.4 13.9 76.5 40.8 1.8 .253 .306 .389 .695 Jake Lamb L 138.9 21.7 7.0 11.1 153.1 49.3 1.2 .253 .316 .378 .694 JR Murphy R 149.3 30.4 2.2 9.6 153.6 36.8 2.5 .266 .314 .380 .694 Dustin Ackley L 134.6 28.0 4.5 16.2 97.8 41.6 2.3 .242 .298 .396 .693 Josh Phegley R 135.6 33.2 1.2 22.0 121.9 26.4 3.5 .238 .276 .416 .692 Will Venable L 133.6 20.8 5.4 14.5 141.6 46.0 3.6 .243 .306 .379 .685 Alex Avila L 109.6 22.8 0.5 15.2 184.9 88.3 1.7 .215 .333 .351 .684 Jonny Gomes R 118.0 20.9 0.0 15.8 167.2 68.2 9.3 .226 .326 .356 .682 Chris Denorfia R 140.3 24.4 4.0 8.7 117.7 46.2 0.9 .254 .312 .360 .672 Grady Sizemore L 134.1 32.4 3.2 10.0 119.9 46.5 1.9 .243 .304 .368 .672 Ike Davis L 114.0 29.9 0.0 12.4 126.1 78.0 0.6 .219 .321 .347 .668 David DeJesus L 125.4 27.5 3.7 10.6 100.5 48.8 11.2 .232 .309 .356 .665 Matt Joyce L 112.5 26.6 1.5 14.2 135.9 68.0 5.3 .214 .310 .351 .661 Ezequiel Carrera L 143.5 25.6 1.9 6.7 134.4 32.1 9.1 .257 .308 .345 .653 Gordon Beckham R 128.6 28.7 0.3 11.5 96.7 37.9 7.9 .232 .291 .347 .638 Jeff Francoeur R 133.5 26.0 2.0 15.4 138.7 25.2 2.0 .233 .268 .366 .634 Skip Schumaker L 133.8 33.7 0.0 3.3 106.1 46.6 3.3 .243 .306 .322 .628 Jake Marisnick R 127.8 21.7 4.7 13.5 164.7 26.8 7.6 .226 .270 .353 .623 Melvin Upton Jr. R 110.8 23.1 3.3 10.2 176.7 66.5 2.2 .208 .299 .322 .621 Mike Aviles R 133.3 22.4 0.5 10.2 71.2 28.7 2.4 .234 .274 .330 .604 Clint Barmes R 125.1 33.6 0.8 7.9 140.4 29.0 10.0 .223 .274 .328 .601 Shane Robinson R 125.2 15.0 6.7 2.1 79.9 52.8 1.5 .229 .299 .293 .592 Alexi Amarista L 123.6 17.9 5.1 6.3 89.2 37.6 1.9 .221 .272 .304 .576 Hernan Perez R 135.9 30.3 6.5 2.5 130.7 12.0 0.0 .231 .246 .317 .564

Franklin Gutierrez has had insane home run per flyball luck over the last two seasons, but regression would not change the fact that he has been an incredible source of power in his limited plate appearances. Among the platoon options, he is the best projected power source in his platoon starts in 2015. Chris Carter, Pedro Alvarez, Justin Bour, and Steve Pearce are the other platoon hitters projected to hit 28 or more home runs per 600 plate appearances in their platoon starts.

David Peralta has likely graduated from his platoon role. Ender Inciarte is no longer in Arizona, and Peralta was nearly a 4-win player last season. That eliminates one of the premiere batting average platoon options, but there are several others. Kike Hernandez is projected to hit .304 in his starts versus lefties, assuming he can get on the field behind Corey Seager and the rest of that loaded Dodgers lineup. Ditto for Carl Crawford and his projected .283 average. Some of the good options who should regularly see the field this year include Danny Valencia (.285)—who looks like the primary Athletics starter at third base following the Brett Lawrie trade—Ben Paulsen—who should share first base duties with Mark Reynolds in Colorado—Clint Robinson (.278), Matt Adams (.278), and Scooter Gennett (.270).

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Scott Spratt is a fantasy sports writer for FanGraphs and Pro Football Focus. He is a Sloan Sports Conference Research Paper Competition and FSWA award winner. Feel free to ask him questions on Twitter – @Scott_Spratt

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Atreyu Jones

Thanks. This is a great article.

However, I do find that my shallowish mixed league with daily lineups and bench spots, guys that I use as a platoon are often not actually platooned in real life. E.G. Gerardo Parra, who is not in the table above and will probably be a full-time starter this year, is someone I will target to start against righties, especially at home.