Razzball Hittertron – Next 7 Day (Weekly) Hitter Projections

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# Name Team ESPN Y! $ $U G PA AB H R HR RBI SB BB SO AVG OBP SLG OPS OWN%
Miguel Cabrera DET 1B, 3B 1B, 3B 37.1 39.3 7.2 33.3 30.6 9.8 5.3 1.8 6.0 0.1 2.5 5.3 0.322 0.406 0.563 0.968 100
Jose Abreu CHA 1B 1B 28.0 33.4 6.7 28.2 26.4 7.4 4.3 2.1 5.4 0.1 1.7 6.9 0.281 0.349 0.559 0.908 100
Adam Jones BAL OF OF 27.0 27.9 6.7 29.5 28.3 8.2 4.0 1.5 4.9 0.4 1.0 5.6 0.289 0.324 0.501 0.826 100
Nelson Cruz BAL OF OF 26.1 26.9 6.6 28.5 26.7 7.1 4.0 1.8 4.9 0.3 1.6 6.4 0.264 0.326 0.511 0.838 100
Mike Trout LAA OF OF 25.6 27.2 5.7 26.3 23.8 7.1 4.0 1.1 3.6 0.9 2.1 6.2 0.299 0.384 0.506 0.890 100
Giancarlo Stanton MIA OF OF 23.5 24.2 5.7 25.9 23.2 6.4 3.7 1.6 4.0 0.3 2.5 6.8 0.275 0.379 0.544 0.922 100
Buster Posey SF C,1B C, 1B 22.7 18.1 6.3 27.6 25.7 7.9 3.8 1.0 4.1 0.1 1.8 3.8 0.306 0.380 0.494 0.875 100
Joe Mauer MIN C,1B C, 1B 22.0 15.6 6.7 28.8 26.5 8.4 3.8 0.8 3.8 0.2 1.9 4.7 0.315 0.388 0.486 0.874 100
Victor Martinez DET 1B 1B 21.7 24.7 7.6 33.9 31.6 9.9 4.6 1.2 5.1 0.2 2.0 3.2 0.312 0.373 0.486 0.859 100
Jose Bautista TOR 1B, OF 1B, OF 21.6 22.2 4.8 21.3 19.0 5.2 3.3 1.3 3.6 0.2 2.2 3.5 0.275 0.381 0.531 0.912 100
Jose Altuve HOU 2B 2B 21.5 19.8 5.7 25.8 24.5 7.6 3.4 0.4 2.5 1.5 1.0 2.4 0.310 0.357 0.445 0.802 100
Ian Kinsler DET 2B 2B 21.2 18.3 7.6 35.3 33.3 8.9 4.8 1.0 3.9 1.0 1.8 4.1 0.268 0.327 0.418 0.745 100
Chris Davis BAL 1B, 3B 1B, 3B 20.8 22.4 6.0 24.1 22.2 5.8 3.7 1.8 4.5 0.1 1.7 7.4 0.262 0.344 0.552 0.897 98
Ben Zobrist TB 2B, SS, OF 2B, SS, OF 20.1 16.7 6.7 31.4 28.0 7.7 4.3 0.9 3.7 0.6 2.9 4.5 0.275 0.377 0.451 0.828 100
Hunter Pence SF OF OF 19.5 20.6 6.7 30.7 29.0 8.3 4.3 1.0 3.8 0.6 1.7 5.8 0.285 0.342 0.469 0.811 100
Chris Carter HOU 1B, OF 1B, OF 19.1 19.5 5.4 23.9 21.6 5.2 3.3 1.7 4.1 0.1 2.0 7.3 0.242 0.332 0.511 0.844 100
Nick Markakis BAL OF OF 18.0 18.7 6.7 31.5 29.5 8.8 4.4 0.9 3.6 0.3 1.8 3.7 0.299 0.362 0.465 0.827 99
Carlos Santana CLE C, 1B, 3B C, 1B, 3B 17.9 11.6 6.7 28.8 25.3 6.4 3.8 1.1 4.0 0.2 3.0 5.2 0.252 0.369 0.438 0.807 100
Edwin Encarnacion TOR 1B 1B, 3B 17.7 20.4 4.8 20.3 18.7 5.1 3.0 1.3 3.6 0.2 1.6 2.9 0.274 0.358 0.536 0.894 100
Anthony Rizzo CHN 1B 1B 17.0 18.9 7.5 33.9 31.0 8.1 4.2 1.5 4.6 0.4 2.5 6.5 0.262 0.348 0.468 0.816 100
Evan Longoria TB 3B 3B 16.2 17.4 6.7 29.9 27.3 7.0 3.9 1.3 4.4 0.2 2.3 6.2 0.258 0.348 0.465 0.813 100
Justin Upton ATL OF OF 16.2 17.1 6.7 29.2 26.6 7.3 3.7 1.2 4.0 0.5 2.5 7.2 0.275 0.366 0.472 0.838 100
Albert Pujols LAA 1B 1B 15.8 17.8 5.7 25.2 23.4 6.7 3.3 1.1 3.8 0.2 1.4 3.1 0.289 0.348 0.485 0.833 100
J.D. Martinez DET OF OF 15.6 16.5 7.5 31.3 29.8 8.3 3.9 1.2 4.5 0.4 1.4 7.4 0.278 0.325 0.459 0.783 99
Brian Dozier MIN 2B 2B 15.1 12.4 6.6 29.9 27.8 6.8 3.8 0.9 3.4 0.9 1.9 5.2 0.244 0.314 0.405 0.720 100
Jose Reyes TOR SS SS 14.7 14.3 4.8 22.8 21.7 6.3 3.1 0.4 2.3 0.9 1.0 2.4 0.289 0.337 0.435 0.772 100
Adrian Beltre TEX 3B 3B 14.6 16.3 6.7 28.2 26.9 7.9 3.4 1.2 4.2 0.1 1.3 3.5 0.293 0.341 0.482 0.823 100
Josh Donaldson OAK 3B 3B 14.4 15.1 6.5 28.7 26.1 7.0 3.6 1.2 4.0 0.3 2.2 5.4 0.270 0.351 0.459 0.810 100
Rajai Davis DET OF OF 14.4 17.1 6.1 27.1 25.8 7.1 3.5 0.4 2.8 2.3 1.1 5.0 0.275 0.315 0.387 0.702 99
Danny Santana MIN SS, OF SS, OF 14.3 14.0 6.3 31.1 30.2 8.6 3.7 0.5 3.0 1.2 0.7 5.7 0.283 0.305 0.412 0.717 0
Torii Hunter DET OF OF 13.4 14.0 6.2 28.9 27.6 7.9 3.7 0.9 3.8 0.3 1.2 5.2 0.287 0.335 0.447 0.782 94
Andrew McCutchen PIT OF OF 13.1 14.4 5.7 24.1 22.1 6.3 3.1 0.7 3.0 0.8 1.9 4.9 0.283 0.369 0.468 0.837 100
Oswaldo Arcia MIN OF OF 12.9 13.3 5.1 21.5 20.2 5.4 2.9 1.2 3.6 0.2 1.0 5.4 0.269 0.322 0.513 0.835 85
Pablo Sandoval SF 3B 3B 12.8 14.5 6.7 28.7 27.2 8.1 3.6 0.9 4.2 0.0 1.5 4.0 0.297 0.353 0.469 0.822 100
Billy Hamilton CIN OF OF 12.6 16.5 5.5 25.3 24.2 5.9 2.8 0.3 1.7 2.8 1.0 5.3 0.245 0.294 0.339 0.633 100
Salvador Perez KC C C 11.9 8.5 6.3 25.8 24.8 7.2 3.0 0.8 3.5 0.0 0.9 3.0 0.291 0.326 0.456 0.781 100
Corey Dickerson COL OF OF 11.7 12.8 6.5 28.4 27.3 7.3 3.3 0.9 3.6 0.6 1.0 5.5 0.268 0.309 0.458 0.767 100
Robinson Cano SEA 2B 2B 11.6 9.6 5.7 23.0 21.4 6.2 2.8 0.7 3.1 0.3 1.3 2.9 0.289 0.356 0.470 0.826 100
Josh Willingham KC OF OF 11.5 12.0 6.7 28.8 25.4 6.1 3.8 1.3 4.1 0.1 2.9 8.2 0.240 0.353 0.452 0.804 54
Melky Cabrera TOR OF OF 11.1 11.6 4.8 21.8 20.8 6.2 2.9 0.6 2.6 0.2 1.0 2.8 0.297 0.342 0.460 0.802 100
Carlos Beltran NYA OF OF 10.9 11.3 4.7 20.1 18.9 5.2 2.7 0.9 3.1 0.2 1.1 3.5 0.275 0.336 0.485 0.822 70
Kennys Vargas MIN 1B 1B 10.9 14.2 5.8 26.4 24.9 6.7 3.3 1.2 4.1 0.1 1.2 5.7 0.270 0.318 0.463 0.781 95
Freddie Freeman ATL 1B 1B 10.9 13.7 6.7 29.8 27.1 7.6 3.7 1.1 3.8 0.1 2.5 6.2 0.281 0.372 0.459 0.831 100
Jacoby Ellsbury NYA OF OF 10.6 12.3 5.3 23.7 22.3 5.9 2.9 0.5 2.6 1.2 1.2 3.7 0.264 0.328 0.390 0.717 100
Angel Pagan SF OF OF 10.5 11.9 6.0 28.2 26.8 7.8 3.7 0.4 2.6 1.0 1.2 3.7 0.293 0.339 0.405 0.743 82
Matt Joyce TB OF OF 10.5 11.2 6.7 27.1 24.2 6.4 3.6 1.1 3.7 0.3 2.9 5.4 0.263 0.368 0.462 0.829 36
Matt Holliday STL OF OF 10.4 11.0 6.7 29.2 26.1 7.5 3.6 0.9 3.5 0.2 2.6 5.1 0.287 0.381 0.452 0.834 100
Omar Infante KC 2B 2B 10.1 8.5 6.4 27.5 26.5 7.9 3.3 0.5 3.1 0.4 0.9 3.3 0.298 0.330 0.424 0.754 48
Wilin Rosario COL C C 9.9 6.0 5.3 22.1 21.4 5.5 2.3 1.0 3.0 0.2 0.5 5.0 0.256 0.284 0.440 0.724 88
Yasiel Puig LAN OF OF 9.9 10.7 5.3 23.0 21.3 6.0 3.0 0.7 2.6 0.5 1.5 4.8 0.281 0.352 0.461 0.813 100
Evan Gattis ATL C, OF C, OF 9.8 7.1 5.6 24.0 22.6 5.7 2.7 1.1 3.3 0.0 1.4 5.3 0.252 0.309 0.456 0.765 100
David Ortiz BOS DH 1B 9.8 10.7 4.7 20.8 18.8 4.8 2.8 1.1 3.2 0.0 1.9 3.7 0.254 0.352 0.482 0.834 100
Alex Gordon KC OF OF 9.8 10.5 6.7 26.1 24.2 7.0 3.4 0.8 3.4 0.4 1.8 5.1 0.290 0.361 0.469 0.830 100
Jason Heyward ATL OF OF 9.7 10.7 6.2 27.8 25.3 6.8 3.5 0.9 3.0 0.6 2.2 5.3 0.269 0.355 0.431 0.786 100
Derek Norris OAK C C 9.5 4.5 5.3 22.9 20.6 5.2 2.7 0.8 2.8 0.2 1.9 4.8 0.253 0.346 0.413 0.759 45
Mark Trumbo ARI 1B, OF 1B, OF 9.4 9.9 5.7 25.0 23.5 5.8 2.8 1.1 3.4 0.3 1.5 6.7 0.247 0.306 0.449 0.755 100
Brandon Moss OAK 1B, OF 1B, OF 9.3 9.7 6.3 28.3 26.1 6.3 3.3 1.3 3.9 0.1 2.1 8.0 0.240 0.314 0.431 0.745 99
Alexei Ramirez CHA SS SS 9.1 7.7 6.7 27.6 26.6 7.2 3.0 0.6 3.0 0.9 0.8 3.6 0.271 0.305 0.393 0.698 100
Bryce Harper WAS OF OF 9.0 9.7 5.5 22.9 21.2 5.5 2.7 1.0 3.0 0.5 1.4 5.2 0.258 0.330 0.460 0.790 100
Kole Calhoun LAA OF OF 9.0 9.7 5.4 26.0 24.5 6.6 3.3 0.7 2.7 0.4 1.4 4.9 0.270 0.323 0.422 0.745 100
Billy Butler KC 1B 1B 8.9 12.2 6.6 27.3 25.2 7.6 3.5 0.9 3.8 0.0 1.8 4.5 0.303 0.370 0.475 0.845 99
Ryan Braun MIL OF OF 8.7 9.8 5.7 24.4 22.8 6.0 2.9 0.9 2.8 0.7 1.3 5.1 0.263 0.321 0.448 0.769 100
Hanley Ramirez LAN SS SS 8.4 6.1 5.7 22.4 21.0 5.6 2.6 0.7 2.7 0.5 1.3 3.9 0.267 0.332 0.428 0.760 100
J.J. Hardy BAL SS SS 8.3 4.2 6.2 24.9 23.8 6.4 2.8 0.9 3.1 0.1 0.9 3.8 0.267 0.305 0.426 0.731 98
David Wright NYN 3B 3B 8.2 7.5 6.6 27.0 24.9 6.9 3.1 0.8 3.1 0.5 1.7 5.0 0.278 0.347 0.427 0.773 99
Javier Baez CHN 2B 2B, SS 8.1 5.6 6.4 27.5 26.1 5.7 3.1 1.1 2.8 0.8 1.1 8.5 0.217 0.269 0.398 0.668 100
Desmond Jennings TB OF OF 8.1 9.8 6.0 28.4 25.9 6.4 3.6 0.7 2.7 1.1 2.1 5.6 0.246 0.327 0.384 0.712 99
Dexter Fowler HOU OF OF 8.0 9.1 5.7 25.8 22.9 6.1 3.3 0.6 2.7 0.6 2.5 5.6 0.266 0.371 0.432 0.803 83
Matt Kemp LAN OF OF 7.9 8.7 5.7 23.0 21.6 5.8 2.6 0.8 3.0 0.5 1.3 6.1 0.269 0.330 0.443 0.773 100
Coco Crisp OAK OF OF 7.9 9.2 5.5 24.9 22.9 5.8 3.2 0.7 2.4 1.0 1.7 3.6 0.253 0.334 0.403 0.736 94
Justin Morneau COL 1B 1B 7.9 10.3 6.0 25.8 24.5 7.0 3.0 1.0 3.6 0.1 1.2 3.9 0.288 0.331 0.470 0.801 100
Mike Aviles CLE 2B, SS, 3B, OF 2B, SS, 3B, OF 7.8 6.4 5.4 24.5 23.7 6.0 2.7 0.6 2.7 0.7 0.7 3.2 0.255 0.293 0.385 0.678 15
Jason Kipnis CLE 2B 2B 7.8 6.1 6.1 26.8 24.8 6.2 3.2 0.6 2.8 0.8 1.8 5.5 0.251 0.323 0.376 0.699 100
Carlos Gomez MIL OF OF 7.7 9.7 5.7 25.6 24.1 5.7 2.8 0.7 2.4 1.3 1.3 6.4 0.236 0.291 0.397 0.688 100
Mark Teixeira NYA 1B 1B 7.4 9.8 4.8 21.0 19.0 4.7 2.8 1.1 3.2 0.1 1.8 3.9 0.246 0.338 0.460 0.799 95
Charlie Blackmon COL OF OF 7.4 8.9 6.3 29.9 28.7 7.5 3.4 0.7 2.7 1.0 1.1 4.7 0.262 0.301 0.390 0.690 100
Wil Myers TB OF OF 7.3 8.1 6.3 26.4 24.2 6.0 3.1 1.0 3.3 0.5 2.1 6.0 0.249 0.330 0.419 0.749 99
Chase Headley NYA 3B 1B, 3B 7.3 7.3 5.7 23.6 21.7 5.8 2.9 0.7 3.0 0.3 1.7 4.9 0.268 0.343 0.428 0.771 86
Jay Bruce CIN OF OF 7.1 7.8 5.0 22.5 20.8 5.0 2.6 0.9 2.8 0.4 1.6 6.2 0.239 0.318 0.437 0.754 100
Yoenis Cespedes BOS OF OF 6.9 7.4 5.1 22.4 21.0 5.2 2.6 0.9 3.0 0.3 1.2 4.9 0.248 0.305 0.428 0.732 100
Josh Harrison PIT 2B, 3B, OF 2B, SS, 3B, OF 6.9 5.6 5.4 23.9 23.2 6.2 2.6 0.4 2.2 1.0 0.8 3.8 0.267 0.302 0.388 0.690 100
Neil Walker PIT 2B 2B 6.8 5.0 5.4 22.9 21.4 5.9 2.6 0.7 2.7 0.2 1.4 4.1 0.277 0.339 0.429 0.768 96
Colby Rasmus TOR OF OF 6.4 6.7 4.4 19.0 17.9 4.4 2.3 0.9 2.6 0.1 1.1 5.3 0.244 0.303 0.456 0.759 64
Daniel Murphy NYN 2B 1B, 2B 6.3 4.7 6.7 29.4 28.0 7.9 3.2 0.4 2.7 0.6 1.1 3.9 0.281 0.323 0.397 0.720 100
Brian McCann NYA C,1B C, 1B 6.2 4.1 4.8 19.8 18.6 4.6 2.3 0.8 2.7 0.0 1.1 3.2 0.249 0.311 0.417 0.728 95
Derek Jeter NYA SS SS 6.1 4.4 5.6 24.8 23.6 6.7 3.0 0.4 2.4 0.3 1.1 3.3 0.285 0.336 0.381 0.717 32
Steve Pearce BAL 1B, OF 1B, OF 6.0 6.3 3.9 16.4 15.2 4.2 2.3 0.8 2.3 0.2 1.1 3.4 0.274 0.345 0.477 0.822 39
Christian Yelich MIA OF OF 5.9 7.1 5.7 27.1 25.1 6.7 3.3 0.5 2.2 0.7 1.8 6.5 0.269 0.338 0.387 0.725 100
Arismendy Alcantara CHN 2B, OF 2B, OF 5.8 3.6 7.6 32.7 31.1 7.5 3.2 0.7 2.9 1.4 1.4 8.0 0.242 0.291 0.364 0.655 40
Todd Frazier CIN 1B, 3B 1B, 3B 5.7 4.8 5.4 23.4 21.9 5.3 2.5 0.7 2.5 0.5 1.5 5.3 0.243 0.313 0.404 0.718 100
Jonathan Lucroy MIL C,1B C, 1B 5.6 -1.2 5.7 24.4 23.0 5.8 2.5 0.5 2.4 0.3 1.3 3.3 0.252 0.313 0.389 0.702 100
James Loney TB 1B 1B 5.5 7.2 6.7 29.9 27.9 7.9 3.3 0.7 3.5 0.1 1.6 3.4 0.282 0.346 0.424 0.770 76
Howie Kendrick LAA 2B 2B 5.4 3.9 5.7 24.6 23.3 6.5 2.6 0.4 2.6 0.5 1.0 4.5 0.278 0.324 0.391 0.715 95
Trevor Plouffe MIN 3B 3B 5.3 6.4 5.9 26.0 24.5 6.0 3.0 1.0 3.5 0.1 1.5 5.2 0.247 0.302 0.427 0.729 50
Chase Utley PHI 2B 2B 5.2 3.3 5.5 24.6 23.1 5.8 2.6 0.7 2.7 0.3 1.4 3.6 0.252 0.318 0.401 0.719 100
Alex Avila DET C C 5.2 0.3 6.8 27.5 24.8 5.8 3.2 0.8 3.2 0.1 2.5 7.9 0.235 0.338 0.406 0.744 2
Lucas Duda NYN 1B, OF 1B, OF 5.2 5.6 6.4 27.0 24.5 6.1 3.0 1.1 3.4 0.2 2.3 6.2 0.249 0.341 0.430 0.771 99
Jason Castro HOU C C 5.1 2.6 4.8 21.5 19.7 4.7 2.4 0.7 2.6 0.0 1.5 5.6 0.238 0.324 0.418 0.741 44
Josh Hamilton LAA OF OF 5.1 5.5 5.4 23.9 22.3 5.5 2.7 0.8 3.0 0.2 1.3 6.8 0.246 0.310 0.414 0.724 100
Norichika Aoki KC OF OF 5.0 6.0 5.4 22.3 20.8 6.4 2.9 0.2 2.0 0.8 1.2 2.0 0.306 0.365 0.408 0.773 82
Pedro Alvarez PIT 3B 1B, 3B 5.0 5.4 5.4 20.3 18.8 4.7 2.3 1.0 2.8 0.2 1.4 5.9 0.249 0.319 0.454 0.774 69
Curtis Granderson NYN OF OF 4.8 5.7 6.6 29.6 27.0 6.1 3.5 1.1 3.1 0.5 2.4 7.2 0.226 0.318 0.399 0.717 79
Ian Desmond WAS SS SS 4.8 2.9 5.6 23.3 22.4 5.3 2.3 0.7 2.7 0.7 0.8 6.1 0.235 0.281 0.389 0.670 100
Jordan Schafer MIN OF OF 4.8 6.7 5.0 19.2 17.8 4.4 2.3 0.3 1.8 1.7 1.3 4.0 0.246 0.320 0.374 0.694 26
Michael Brantley CLE OF OF 4.8 5.7 6.1 26.2 24.6 6.7 3.0 0.5 3.0 0.5 1.4 2.8 0.271 0.333 0.392 0.725 100
Adam LaRoche WAS 1B 1B 4.6 6.9 5.5 23.5 21.6 5.3 2.7 1.0 3.2 0.1 1.9 5.3 0.245 0.328 0.441 0.769 100
Matt Adams STL 1B 1B 4.6 6.7 6.7 29.2 27.8 7.5 3.0 0.9 3.7 0.2 1.1 6.8 0.270 0.304 0.413 0.717 100
Kyle Seager SEA 3B 3B 4.6 4.6 5.7 23.0 21.4 5.4 2.6 0.7 2.8 0.3 1.3 4.5 0.253 0.324 0.428 0.752 100
Chris Parmelee MIN 1B, OF 1B, OF 4.6 4.8 4.6 18.7 17.4 4.7 2.3 0.7 2.6 0.1 1.2 3.7 0.267 0.332 0.461 0.793 1
Nolan Arenado COL 3B 3B 4.5 5.4 6.7 29.3 28.4 7.6 3.0 0.8 3.5 0.2 0.8 3.8 0.268 0.299 0.421 0.720 100
Kolten Wong STL 2B 2B 4.3 3.1 5.7 25.9 24.7 6.4 2.6 0.4 2.3 0.9 1.1 3.9 0.258 0.306 0.351 0.657 93
Asdrubal Cabrera WAS 2B, SS 2B, SS 4.1 1.5 5.7 24.1 22.7 5.7 2.7 0.7 2.5 0.3 1.2 4.6 0.251 0.300 0.403 0.703 98
Michael Morse SF 1B, OF 1B, OF 4.0 4.2 6.2 22.8 21.8 5.8 2.8 0.8 3.3 0.0 1.2 5.7 0.268 0.323 0.452 0.775 98
Dioner Navarro TOR C C 3.9 1.6 4.1 16.3 15.4 4.1 1.8 0.5 2.0 0.1 0.7 2.3 0.265 0.313 0.413 0.726 17
Dustin Pedroia BOS 2B 2B 3.9 2.6 5.5 26.0 24.0 6.2 2.8 0.4 2.4 0.5 1.7 3.2 0.260 0.327 0.360 0.687 100
Erick Aybar LAA SS SS 3.9 3.3 5.7 24.6 23.6 6.5 2.5 0.3 2.4 0.7 0.9 2.7 0.274 0.312 0.375 0.688 92
Delmon Young BAL OF OF 3.9 4.1 4.3 17.5 16.8 4.7 2.1 0.7 2.4 0.1 0.5 3.6 0.277 0.313 0.449 0.762 0
Russell Martin PIT C C 3.9 -2.8 5.1 21.6 19.8 4.5 2.1 0.5 2.1 0.3 1.8 4.6 0.227 0.322 0.346 0.668 37
Robbie Grossman HOU OF OF 3.9 5.1 5.4 24.5 22.2 5.6 2.9 0.5 2.3 0.8 2.2 5.0 0.251 0.347 0.390 0.737 10
Martin Prado NYA 2B, 3B, OF 2B, 3B, OF 3.8 2.6 5.0 19.7 18.7 5.3 2.3 0.5 2.4 0.1 0.9 2.3 0.283 0.333 0.425 0.758 98
Jon Singleton HOU 1B 1B 3.8 5.5 5.5 23.8 21.1 4.9 2.8 1.0 3.1 0.2 2.3 6.6 0.231 0.337 0.426 0.763 55
Dayan Viciedo CHA OF OF 3.8 4.0 6.0 23.0 22.0 5.6 2.7 1.0 3.2 0.0 1.0 5.3 0.257 0.300 0.431 0.731 39
Travis d’Arnaud NYN C C 3.8 0.2 5.7 23.0 21.7 5.4 2.3 0.8 2.8 0.1 1.2 3.8 0.248 0.304 0.408 0.711 29
Anthony Rendon WAS 2B, 3B 2B, 3B 3.8 2.0 5.7 25.2 23.8 5.8 2.9 0.6 2.5 0.3 1.4 4.3 0.244 0.304 0.401 0.705 100
Brett Gardner NYA OF OF 3.8 4.9 5.2 23.8 21.9 5.7 3.1 0.4 2.1 0.8 1.7 5.2 0.259 0.329 0.368 0.697 100
Kevin Kiermaier TB OF OF 3.8 5.1 6.3 23.8 22.4 6.0 2.8 0.5 2.7 0.9 1.5 3.9 0.269 0.327 0.421 0.748 15
Devin Mesoraco CIN C C 3.7 0.4 4.6 19.3 18.0 4.3 1.9 0.6 2.2 0.1 1.2 4.1 0.241 0.304 0.402 0.706 98
Zach Walters CLE 3B 3B 3.5 4.1 5.0 19.4 18.7 4.7 2.3 0.8 2.7 0.1 0.6 4.2 0.252 0.285 0.439 0.724 19
Jayson Werth WAS OF OF 3.4 3.9 5.6 23.3 21.5 5.5 2.7 0.7 2.8 0.3 1.4 5.0 0.254 0.329 0.418 0.747 100
Starling Marte PIT OF OF 3.3 4.6 4.5 18.2 17.3 4.4 2.0 0.4 1.7 1.0 0.8 4.8 0.257 0.313 0.398 0.711 100
Elvis Andrus TEX SS SS 3.2 4.8 6.7 31.0 29.3 7.8 3.2 0.2 2.4 1.4 1.5 4.0 0.266 0.320 0.338 0.659 100
Ben Revere PHI OF OF 3.2 5.4 5.7 26.4 25.8 7.2 2.5 0.1 1.7 1.5 0.6 2.5 0.279 0.299 0.331 0.630 100
Matt Carpenter STL 2B, 3B 2B, 3B 3.2 1.7 6.7 30.5 27.8 7.8 3.7 0.4 2.4 0.2 2.3 5.2 0.280 0.360 0.383 0.744 100
Jhonny Peralta STL SS SS 3.2 -0.3 6.7 27.8 25.6 6.8 2.8 0.7 3.1 0.1 1.9 5.3 0.268 0.333 0.402 0.734 100
Drew Stubbs COL OF OF 3.2 4.0 4.3 18.8 17.9 4.3 2.0 0.5 2.0 0.8 0.9 5.8 0.243 0.295 0.384 0.680 85
Alex Rios TEX OF OF 3.1 4.2 5.7 22.4 21.6 5.7 2.3 0.5 2.5 0.9 0.7 3.5 0.263 0.300 0.405 0.706 96
Allen Craig BOS 1B, OF 1B, OF 3.0 3.3 5.4 23.4 21.9 5.9 2.5 0.7 2.8 0.1 1.3 4.8 0.267 0.323 0.406 0.729 62
Jose Ramirez CLE 2B, SS 2B, SS 3.0 3.2 5.7 23.3 22.3 6.0 2.5 0.3 2.3 1.0 0.8 2.3 0.268 0.305 0.354 0.659 2
Josh Rutledge COL 2B, SS 2B, SS, 3B 3.0 2.0 5.1 22.1 21.3 5.6 2.3 0.4 2.2 0.6 0.8 4.5 0.262 0.296 0.391 0.688 36
Adam Lind TOR 1B 1B 2.9 4.5 3.8 14.6 13.8 3.8 1.9 0.6 2.2 0.0 0.8 2.6 0.272 0.333 0.479 0.812 64
Matt Dominguez HOU 3B 3B 2.9 4.2 5.4 22.3 21.1 5.3 2.5 0.9 2.9 0.0 1.1 3.9 0.252 0.300 0.426 0.726 19
Jarrod Dyson KC OF OF 2.8 3.8 3.5 13.2 12.4 3.4 1.6 0.1 1.2 1.3 0.7 2.4 0.272 0.332 0.374 0.706 46
Jimmy Rollins PHI SS SS 2.7 2.0 5.6 25.7 24.0 5.5 2.7 0.6 2.1 0.9 1.4 4.0 0.227 0.289 0.350 0.639 100
Ryan Flaherty BAL 2B, SS, 3B 2B, SS, 3B 2.7 1.4 3.6 13.9 13.2 3.3 1.6 0.5 1.8 0.1 0.7 3.0 0.248 0.304 0.430 0.735 0
Jake Marisnick HOU OF OF 2.6 3.6 4.7 19.0 17.9 4.6 2.0 0.5 2.0 0.7 0.9 3.9 0.254 0.299 0.398 0.696 5
J.P. Arencibia TEX C,1B C, 1B 2.6 -0.2 5.0 19.5 18.7 4.0 2.0 0.9 2.4 0.1 0.7 4.8 0.212 0.257 0.399 0.656 12
Adam Dunn CHA 1B 1B, OF 2.5 5.3 6.4 23.9 21.1 4.7 3.1 1.3 3.4 0.1 2.6 8.3 0.220 0.329 0.438 0.767 38
Mike Moustakas KC 3B 3B 2.5 3.4 6.2 23.5 22.3 5.9 2.7 0.8 3.1 0.1 1.2 4.1 0.264 0.315 0.447 0.763 30
Alejandro De Aza CHA OF OF 2.5 3.9 6.4 25.2 23.6 6.1 3.0 0.5 2.5 0.9 1.3 6.1 0.259 0.322 0.398 0.720 36
Nick Castellanos DET 3B 3B, OF 2.4 2.9 6.9 28.1 26.7 7.3 3.1 0.7 3.4 0.2 1.2 5.9 0.273 0.319 0.410 0.729 73
Ezequiel Carrera DET OF OF 2.4 2.8 2.6 11.9 11.1 3.2 1.4 0.1 1.2 0.9 0.6 1.9 0.285 0.341 0.397 0.738 0
Stephen Vogt OAK C, 1B, OF C, 1B, OF 2.4 1.1 3.5 14.7 13.9 3.6 1.6 0.4 1.8 0.1 0.5 2.1 0.262 0.312 0.430 0.742 88
Mike Napoli BOS 1B 1B 2.4 4.3 5.2 23.6 21.1 4.8 2.7 0.9 2.9 0.1 2.3 7.6 0.226 0.329 0.403 0.732 99
Seth Smith SD OF OF 2.2 2.5 5.5 23.7 21.8 5.4 2.6 0.8 2.6 0.1 1.7 5.0 0.249 0.331 0.450 0.781 56
Kurt Suzuki MIN C C 2.1 -0.7 4.9 20.2 19.2 5.0 2.1 0.4 2.2 0.1 0.9 2.3 0.259 0.309 0.393 0.702 20
Lorenzo Cain KC OF OF 2.0 3.3 6.1 22.6 21.4 6.1 2.6 0.4 2.3 0.9 1.0 4.9 0.283 0.330 0.407 0.737 69
Aaron Hill ARI 2B 2B 1.9 0.3 5.5 23.5 22.2 5.7 2.5 0.6 2.4 0.2 1.1 3.9 0.255 0.309 0.394 0.703 70
Emilio Bonifacio ATL 2B, OF 2B, 3B, OF 1.9 1.6 2.8 13.4 12.6 3.3 1.4 0.1 1.0 0.8 0.7 2.7 0.259 0.318 0.335 0.653 43
Adrian Gonzalez LAN 1B 1B 1.9 3.8 5.0 20.1 19.0 4.9 2.2 0.8 2.6 0.1 1.1 3.7 0.259 0.320 0.454 0.774 100
David Peralta ARI OF OF 1.7 2.2 5.3 23.9 23.0 6.1 2.4 0.5 2.5 0.3 0.8 3.8 0.264 0.301 0.412 0.713 40
Starlin Castro CHN SS SS 1.6 0.1 6.1 27.0 25.8 6.7 2.5 0.5 2.7 0.4 1.0 4.6 0.259 0.305 0.375 0.680 100
Dustin Ackley SEA 2B, OF 1B, 2B, OF 1.6 0.0 5.7 25.2 23.5 5.9 2.7 0.5 2.5 0.4 1.5 4.4 0.251 0.312 0.378 0.690 93
Marwin Gonzalez HOU SS 2B, SS, 3B 1.6 0.8 3.5 14.4 13.7 3.6 1.5 0.3 1.5 0.3 0.6 2.1 0.261 0.306 0.407 0.712 2
Juan Francisco TOR 1B, 3B 1B, 3B 1.5 1.9 3.0 10.7 10.0 2.4 1.3 0.6 1.6 0.1 0.6 3.3 0.244 0.299 0.475 0.774 23
Michael McKenry COL C C 1.5 0.2 2.7 11.1 10.5 2.6 1.1 0.3 1.3 0.1 0.5 2.4 0.251 0.302 0.401 0.703 0
Josh Reddick OAK OF OF 1.4 2.0 6.3 24.9 23.3 5.6 2.7 0.9 3.0 0.3 1.5 5.2 0.238 0.301 0.407 0.708 77
Will Venable SD OF OF 1.4 2.2 4.7 18.7 17.7 4.2 1.9 0.6 2.0 0.6 1.0 4.6 0.235 0.293 0.421 0.714 52
Wilson Ramos WAS C C 1.3 -0.4 4.3 17.1 16.4 4.0 1.6 0.6 2.0 0.0 0.6 3.0 0.246 0.288 0.405 0.693 93
Leonys Martin TEX OF OF 1.3 3.0 6.3 25.9 24.6 6.3 2.5 0.4 2.4 1.2 1.2 5.0 0.255 0.306 0.377 0.684 93
Nick Hundley BAL C C 1.2 -0.2 3.3 11.9 11.3 2.8 1.3 0.4 1.4 0.1 0.6 2.7 0.252 0.296 0.404 0.700 0
Travis Snider PIT OF OF 1.2 1.7 5.4 21.8 20.6 5.4 2.4 0.6 2.4 0.2 1.3 5.0 0.262 0.318 0.408 0.726 43
Jordany Valdespin MIA 2B, OF 2B, OF 1.0 0.7 2.0 8.0 7.6 2.0 0.9 0.2 0.8 0.3 0.5 1.2 0.260 0.321 0.393 0.714 0
Caleb Joseph BAL C C 1.0 -0.7 3.7 13.4 12.8 3.2 1.5 0.4 1.6 0.1 0.6 2.8 0.246 0.292 0.391 0.683 5
Carlos Ruiz PHI C C 0.8 -2.3 4.3 17.8 16.5 4.3 1.6 0.3 1.7 0.2 1.0 2.4 0.259 0.318 0.372 0.690 6
Craig Gentry OAK OF OF 0.8 1.4 3.5 15.5 14.3 3.8 1.6 0.2 1.2 0.8 0.9 2.6 0.262 0.326 0.336 0.661 0
Marc Krauss HOU 1B, OF 1B, OF 0.8 0.8 2.3 8.6 7.9 2.0 1.0 0.4 1.1 0.1 0.7 1.8 0.255 0.336 0.451 0.787 0
Kendrys Morales SEA 1B 1B 0.7 3.0 5.7 23.6 22.1 5.4 2.6 0.8 3.0 0.1 1.1 4.8 0.245 0.304 0.419 0.723 52
Eduardo Nunez MIN SS, 3B, OF SS, 3B, OF 0.7 0.7 1.8 7.8 7.5 2.0 0.8 0.1 0.8 0.4 0.3 1.2 0.270 0.302 0.396 0.699 1
Jonathan Schoop BAL 2B, 3B 2B, 3B 0.7 -0.4 4.8 17.8 17.1 4.2 2.0 0.6 2.1 0.2 0.5 3.8 0.246 0.285 0.388 0.673 29
Chris Johnson ATL 3B 1B, 3B 0.6 1.3 6.5 28.4 27.2 7.3 2.7 0.6 3.1 0.2 1.0 7.0 0.269 0.315 0.398 0.713 75
Mookie Betts BOS OF OF 0.6 0.8 2.3 9.5 8.8 2.3 1.0 0.2 0.9 0.5 0.5 1.3 0.256 0.322 0.362 0.684 8
Avisail Garcia CHA OF OF 0.6 0.6 2.2 8.4 8.1 2.2 0.9 0.3 1.1 0.1 0.2 2.1 0.269 0.305 0.416 0.720 70
Brayan Pena CIN C,1B C, 1B 0.5 -3.3 4.5 17.4 16.7 4.4 1.5 0.3 1.7 0.2 0.6 2.1 0.261 0.293 0.365 0.658 2
Jarrod Saltalamacchia MIA C C 0.5 -2.0 4.5 19.1 17.6 4.0 1.8 0.6 2.0 0.1 1.4 6.1 0.226 0.306 0.369 0.675 25
David Lough BAL OF OF 0.5 0.6 1.8 6.3 6.1 1.8 0.8 0.2 0.8 0.2 0.2 0.9 0.289 0.328 0.449 0.777 0
Zelous Wheeler NYA 3B 3B, OF 0.4 0.5 2.4 8.3 7.9 2.0 1.0 0.3 1.1 0.1 0.4 1.8 0.257 0.317 0.416 0.733 0
Eduardo Escobar MIN SS, 3B 2B, SS, 3B 0.4 -1.4 6.0 24.2 23.3 6.3 2.5 0.4 2.6 0.3 1.0 4.7 0.270 0.303 0.392 0.694 11
Erik Kratz KC C C 0.3 0.1 1.2 4.1 3.8 1.0 0.5 0.2 0.6 0.0 0.2 0.7 0.270 0.315 0.449 0.764 0
Lonnie Chisenhall CLE 1B, 3B 1B, 3B 0.3 0.9 5.3 20.7 19.6 4.9 2.3 0.6 2.5 0.1 1.0 4.0 0.252 0.306 0.402 0.708 52
Eric Fryer MIN C C 0.3 -0.7 2.1 8.5 7.9 1.8 0.8 0.1 0.8 0.1 0.4 1.7 0.223 0.289 0.344 0.633 0
Don Kelly DET 1B, 3B, OF 1B, 3B, OF 0.3 0.2 1.8 7.9 7.4 1.9 0.9 0.2 0.8 0.2 0.5 1.1 0.258 0.331 0.381 0.712 0
Miguel Montero ARI C C 0.3 -2.0 5.1 21.9 20.0 4.9 2.1 0.6 2.3 0.0 1.6 5.0 0.243 0.325 0.389 0.714 88
Carlos Corporan HOU C C 0.2 -0.2 1.9 7.4 7.0 1.7 0.8 0.2 0.9 0.0 0.4 1.6 0.240 0.303 0.404 0.707 0
Marcell Ozuna MIA OF OF 0.2 0.6 5.7 24.2 22.8 5.7 2.4 0.7 2.7 0.2 1.2 5.9 0.250 0.300 0.406 0.706 98
Austin Jackson SEA OF OF 0.2 1.2 5.7 25.2 23.7 6.1 2.9 0.4 2.2 0.6 1.3 6.3 0.257 0.315 0.376 0.691 96
Dee Gordon LAN 2B, SS 2B, SS 0.2 3.2 5.4 24.0 23.0 5.4 2.4 0.1 1.5 1.8 1.0 4.3 0.234 0.285 0.325 0.610 100
Matt Duffy SF 2B 2B 0.2 0.1 1.3 5.3 5.0 1.3 0.6 0.1 0.5 0.2 0.3 1.0 0.263 0.326 0.364 0.690 0
Denard Span WAS OF OF 0.2 1.6 5.7 25.7 24.6 6.6 2.9 0.2 1.8 0.9 1.0 3.1 0.266 0.309 0.368 0.677 99
Paul Konerko CHA 1B 1B 0.1 0.2 1.3 5.3 5.0 1.3 0.6 0.2 0.7 0.0 0.3 0.8 0.268 0.329 0.432 0.760 1
Christian Colon KC 2B 2B 0.1 0.1 1.2 4.2 4.0 1.1 0.5 0.1 0.4 0.1 0.2 0.4 0.284 0.334 0.396 0.730 0
Danny Valencia TOR 1B, 3B 1B, 2B, 3B 0.1 0.3 2.7 10.0 9.6 2.5 1.1 0.3 1.3 0.1 0.3 2.1 0.263 0.296 0.428 0.724 0
Andrew Romine DET 2B, SS, 3B 2B, SS, 3B 0.1 0.2 2.8 13.7 13.0 3.2 1.4 0.1 1.2 0.5 0.7 2.8 0.243 0.302 0.320 0.622 0
Matt McBride COL OF OF 0.0 0.0 0.8 2.7 2.7 0.7 0.3 0.1 0.3 0.0 0.1 0.5 0.270 0.294 0.434 0.728 0
Ryan Hanigan TB C C 0.0 0.0 0.4 1.4 1.3 0.3 0.1 0.0 0.1 0.0 0.1 0.2 0.231 0.326 0.323 0.649 0
Anthony Recker NYN C C 0.0 -0.4 1.4 5.4 5.1 1.1 0.5 0.2 0.5 0.1 0.3 1.7 0.210 0.271 0.348 0.620 0
Gaby Sanchez PIT 1B 1B 0.0 0.0 0.3 1.0 0.9 0.2 0.1 0.0 0.1 0.0 0.1 0.2 0.243 0.308 0.359 0.666 0
Michael Choice TEX OF OF 0.0 0.0 0.4 1.7 1.6 0.4 0.2 0.1 0.2 0.0 0.1 0.3 0.246 0.312 0.386 0.699 0
Sean Rodriguez TB 1B, 2B, OF 1B, 2B, SS, 3B, OF 0.0 0.0 1.0 3.8 3.5 0.8 0.4 0.1 0.4 0.1 0.2 1.0 0.229 0.295 0.382 0.677 1
Kevin Pillar TOR OF OF 0.0 0.0 0.3 1.1 1.1 0.3 0.1 0.0 0.1 0.1 0.0 0.2 0.279 0.304 0.418 0.722 0
Leury Garcia CHA 2B, 3B, OF 2B, SS, 3B, OF 0.0 -0.1 0.9 3.2 3.1 0.7 0.3 0.1 0.3 0.2 0.1 0.7 0.231 0.261 0.308 0.569 0
Carl Crawford LAN OF OF 0.0 1.0 4.8 19.0 18.3 4.6 1.9 0.4 1.8 0.8 0.7 3.4 0.252 0.291 0.393 0.684 96
Mike Carp TEX 1B, OF 1B, OF 0.0 0.0 0.6 2.0 1.8 0.5 0.2 0.1 0.2 0.0 0.1 0.5 0.252 0.316 0.421 0.736 0
Clint Barmes PIT 2B, SS 2B, SS 0.0 0.0 0.3 1.2 1.1 0.2 0.1 0.0 0.1 0.0 0.1 0.3 0.216 0.261 0.301 0.562 0
Tomas Telis TEX C C 0.0 0.0 0.4 1.7 1.7 0.4 0.1 0.0 0.1 0.0 0.0 0.3 0.254 0.280 0.349 0.629 0
Jesus Guzman HOU 1B, OF 1B, OF 0.0 0.0 0.9 3.1 2.8 0.7 0.4 0.1 0.4 0.0 0.2 0.6 0.258 0.336 0.415 0.751 0
Brandon Guyer TB OF OF 0.0 0.0 1.0 3.8 3.6 0.9 0.4 0.1 0.4 0.1 0.2 0.7 0.256 0.321 0.380 0.701 0
Josh Thole TOR C C 0.0 -0.4 1.3 4.7 4.4 1.1 0.5 0.1 0.5 0.1 0.3 0.8 0.256 0.320 0.371 0.692 0
Joaquin Arias SF 1B, 2B, SS, 3B 1B, 2B, SS, 3B 0.0 0.0 0.8 2.7 2.6 0.7 0.3 0.0 0.3 0.0 0.1 0.4 0.272 0.306 0.366 0.673 0
John McDonald LAA SS, 3B 2B, SS, 3B 0.0 0.0 0.3 1.0 1.0 0.2 0.1 0.0 0.1 0.0 0.0 0.2 0.215 0.267 0.296 0.563 0
Chris Heisey CIN OF OF 0.0 0.1 1.9 8.4 8.0 1.9 0.8 0.2 0.8 0.2 0.3 2.1 0.233 0.284 0.367 0.650 1
Nate Schierholtz WAS OF OF 0.0 0.4 4.5 19.9 19.1 4.4 1.9 0.6 2.3 0.3 0.8 4.3 0.227 0.271 0.387 0.657 8
Justin Ruggiano CHN OF OF 0.0 0.2 3.0 12.0 11.2 2.7 1.2 0.3 1.2 0.3 0.7 3.3 0.237 0.305 0.384 0.689 10
Scott Hairston WAS OF OF -0.1 -0.1 0.5 1.6 1.5 0.3 0.1 0.1 0.2 0.0 0.1 0.5 0.208 0.247 0.361 0.608 0
Jose Tabata PIT OF OF -0.1 0.0 0.9 4.1 3.9 1.0 0.4 0.0 0.4 0.1 0.2 0.6 0.267 0.318 0.355 0.672 0
Xavier Paul ARI OF OF -0.1 -0.1 0.6 2.1 2.0 0.5 0.2 0.1 0.2 0.0 0.1 0.4 0.235 0.293 0.397 0.690 0
Ryan Doumit ATL C, OF C, OF -0.1 -0.2 0.9 3.2 3.0 0.7 0.3 0.1 0.3 0.0 0.2 0.6 0.238 0.293 0.361 0.654 0
Daniel Descalso STL 2B, SS, 3B 2B, SS, 3B -0.1 -0.1 0.5 2.1 2.0 0.5 0.2 0.0 0.2 0.0 0.1 0.3 0.241 0.304 0.320 0.624 0
Reed Johnson MIA OF OF -0.1 -0.1 0.9 3.7 3.5 0.9 0.3 0.1 0.3 0.1 0.1 0.7 0.252 0.297 0.364 0.661 0
Randal Grichuk STL OF OF -0.1 -0.1 1.1 4.3 4.1 1.0 0.4 0.1 0.4 0.1 0.2 1.1 0.235 0.278 0.375 0.652 0
A.J. Pierzynski STL C C -0.1 -0.3 1.6 6.2 5.9 1.5 0.6 0.1 0.6 0.0 0.2 0.9 0.260 0.296 0.362 0.658 14
Kirk Nieuwenhuis NYN OF OF -0.1 -0.1 0.7 2.3 2.2 0.5 0.2 0.1 0.2 0.0 0.1 0.7 0.228 0.291 0.373 0.664 0
Freddy Galvis PHI 2B, 3B 2B, SS, 3B, OF -0.1 -0.1 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.0 0.2 0.232 0.255 0.334 0.588 0
Adam Eaton CHA OF OF -0.1 1.1 6.0 22.9 21.3 5.8 2.9 0.3 1.9 0.8 1.4 4.1 0.271 0.340 0.385 0.725 87
Kelly Johnson BOS 1B, 2B, 3B, OF 1B, 2B, 3B, OF -0.1 -0.2 1.1 4.1 3.7 0.8 0.4 0.1 0.4 0.1 0.3 1.1 0.214 0.294 0.368 0.662 10
Steve Tolleson TOR 2B, 3B 2B, 3B, OF -0.1 -0.2 1.0 3.5 3.3 0.9 0.4 0.1 0.3 0.1 0.2 0.8 0.267 0.319 0.393 0.712 0
Bryan Holaday DET C C -0.1 -0.4 1.7 6.8 6.4 1.5 0.7 0.1 0.7 0.1 0.3 1.3 0.238 0.287 0.338 0.624 0
Chris Gimenez CLE C C, 1B -0.1 -0.2 1.2 4.7 4.3 1.1 0.5 0.1 0.5 0.0 0.3 1.1 0.249 0.324 0.362 0.686 0
David Ross BOS C C -0.2 -0.3 0.9 3.1 2.9 0.6 0.3 0.1 0.3 0.0 0.2 1.1 0.203 0.267 0.332 0.599 0
Chris Denorfia SEA OF OF -0.2 -0.2 1.3 4.8 4.6 1.2 0.5 0.1 0.4 0.1 0.2 1.0 0.257 0.299 0.362 0.660 1
Matthew Szczur CHN OF OF -0.2 -0.1 0.8 2.8 2.7 0.6 0.2 0.0 0.2 0.1 0.1 0.5 0.236 0.290 0.311 0.601 0
Chris Stewart PIT C C -0.2 -0.4 0.9 3.4 3.2 0.7 0.3 0.0 0.3 0.0 0.2 0.7 0.226 0.288 0.292 0.581 0
Jon Jay STL OF OF -0.2 -0.1 1.9 7.5 6.9 2.0 0.7 0.1 0.7 0.1 0.5 1.2 0.293 0.352 0.371 0.723 54
Charlie Culberson COL 2B, SS, 3B, OF 2B, SS, 3B, OF -0.2 -0.5 3.2 13.2 12.9 3.2 1.1 0.3 1.3 0.3 0.3 2.9 0.248 0.268 0.362 0.630 4
Darin Ruf PHI 1B, OF 1B, OF -0.2 -0.2 1.2 4.2 3.9 0.9 0.4 0.1 0.5 0.0 0.2 1.1 0.235 0.302 0.398 0.699 0
Domonic Brown PHI OF OF -0.2 0.2 5.1 20.1 19.0 4.8 1.9 0.7 2.3 0.3 0.9 3.7 0.254 0.298 0.405 0.703 40
Ryan Howard PHI 1B 1B -0.2 2.4 5.5 23.5 21.8 4.9 2.4 1.0 3.0 0.1 1.5 6.8 0.225 0.300 0.411 0.711 73
Scott Van Slyke LAN 1B, OF 1B, OF -0.2 0.0 2.9 10.1 9.4 2.2 1.2 0.4 1.2 0.1 0.9 2.3 0.231 0.325 0.411 0.736 1
Ed Lucas MIA 1B, 2B, SS, 3B 1B, 2B, SS, 3B, OF -0.2 -0.2 0.9 4.0 3.8 0.9 0.4 0.1 0.3 0.0 0.2 0.9 0.246 0.294 0.329 0.623 0
Adrian Nieto CHA C C -0.2 -0.8 1.8 6.3 5.8 1.3 0.6 0.1 0.6 0.1 0.4 1.7 0.219 0.282 0.330 0.613 0
Ramiro Pena ATL 2B, SS, 3B 2B, SS, 3B -0.2 -0.2 1.0 4.0 3.7 0.9 0.4 0.0 0.4 0.0 0.2 0.9 0.240 0.303 0.331 0.634 0
Andrew Susac SF C C -0.3 -0.9 2.5 9.7 9.0 2.1 1.0 0.2 1.1 0.0 0.7 2.2 0.236 0.311 0.363 0.674 0
Raul Ibanez KC OF 1B, OF -0.3 -0.3 2.3 7.1 6.6 1.7 0.9 0.3 0.9 0.1 0.5 1.5 0.249 0.321 0.452 0.773 2
Andres Blanco PHI 3B 3B -0.3 -0.3 0.9 3.3 3.2 0.7 0.2 0.0 0.3 0.1 0.1 0.5 0.229 0.264 0.314 0.578 0
Robinson Chirinos TEX C C -0.3 -2.0 3.9 15.4 14.5 3.4 1.5 0.4 1.6 0.1 0.8 3.1 0.234 0.289 0.368 0.657 2
Jesus Sucre SEA C C -0.3 -0.5 1.3 5.0 4.8 1.1 0.4 0.1 0.4 0.1 0.1 0.9 0.228 0.256 0.309 0.565 0
Jeff Baker MIA 1B, 2B, OF 1B, 2B, 3B, OF -0.3 -0.7 3.1 12.6 11.8 3.0 1.2 0.3 1.3 0.1 0.7 3.1 0.257 0.310 0.393 0.703 0
B.J. Upton ATL OF OF -0.3 0.8 5.3 22.6 20.8 4.6 2.5 0.6 2.0 0.8 1.7 7.0 0.222 0.300 0.359 0.659 48
Michael Bourn CLE OF OF -0.4 1.2 6.7 30.8 29.0 7.3 3.6 0.2 2.3 1.0 1.5 7.6 0.252 0.306 0.338 0.644 44
Eric Campbell NYN 1B, 3B, OF 1B, 3B, OF -0.4 -0.5 1.5 5.8 5.3 1.2 0.5 0.1 0.5 0.1 0.3 1.2 0.233 0.311 0.356 0.666 0
Tuffy Gosewisch ARI C C -0.4 -1.2 2.2 8.9 8.5 1.9 0.7 0.2 0.8 0.1 0.4 1.9 0.223 0.275 0.336 0.611 0
Chris Dickerson CLE OF OF -0.4 -0.3 1.8 5.1 4.7 1.2 0.6 0.1 0.6 0.2 0.3 1.4 0.258 0.321 0.383 0.704 1
Kevin Frandsen WAS 1B, 2B, 3B, OF 1B, 2B, 3B, OF -0.4 -0.4 1.0 3.8 3.7 0.9 0.3 0.0 0.3 0.0 0.1 0.5 0.241 0.277 0.329 0.605 0
Hank Conger LAA C C -0.4 -0.8 1.8 6.1 5.7 1.4 0.6 0.1 0.6 0.0 0.4 1.3 0.241 0.299 0.353 0.652 0
Jonny Gomes OAK OF OF -0.4 -0.3 2.2 7.6 6.7 1.7 0.9 0.3 0.8 0.0 0.7 1.9 0.246 0.355 0.402 0.757 2
Cliff Pennington ARI 2B, SS 2B, SS -0.4 -0.6 2.7 12.4 11.6 2.8 1.2 0.2 1.1 0.3 0.7 2.3 0.242 0.308 0.352 0.660 23
Brandon Barnes COL OF OF -0.4 -0.3 2.3 9.2 8.9 2.1 0.9 0.2 0.9 0.2 0.3 2.6 0.232 0.271 0.369 0.640 0
Darwin Barney LAN 2B 2B -0.4 -0.4 1.0 3.7 3.6 0.9 0.3 0.0 0.3 0.0 0.1 0.4 0.243 0.275 0.312 0.588 0
Martin Maldonado MIL C C, 1B -0.5 -0.6 1.1 4.2 3.9 0.8 0.3 0.1 0.3 0.0 0.2 1.2 0.198 0.252 0.303 0.554 0
Jack Hannahan CIN 3B 1B, 3B -0.5 -0.5 1.2 4.2 4.0 0.9 0.4 0.1 0.4 0.0 0.3 0.9 0.234 0.301 0.334 0.635 0
Rougned Odor TEX 2B 2B -0.5 -1.9 6.2 24.9 24.0 5.8 2.4 0.4 2.4 0.7 0.8 4.1 0.243 0.279 0.369 0.649 20
Lyle Overbay MIL 1B 1B -0.5 -0.5 1.3 4.7 4.4 1.0 0.4 0.1 0.4 0.0 0.3 1.1 0.231 0.304 0.358 0.662 0
Yasmani Grandal SD C,1B C, 1B -0.5 -2.7 4.2 16.5 15.1 3.4 1.6 0.5 1.7 0.1 1.3 3.9 0.223 0.317 0.409 0.726 1
Francisco Cervelli NYA C C -0.6 -2.9 3.7 12.7 11.9 3.0 1.4 0.3 1.3 0.1 0.8 2.8 0.253 0.314 0.359 0.674 0
Logan Forsythe TB 2B 2B, SS, 3B, OF -0.6 -0.8 2.3 8.9 8.3 1.9 0.9 0.2 0.9 0.1 0.6 1.8 0.234 0.312 0.353 0.665 4
Gerald Laird ATL C C -0.6 -1.0 1.8 7.4 6.9 1.6 0.6 0.1 0.6 0.1 0.5 1.5 0.234 0.306 0.318 0.624 0
Alcides Escobar KC SS SS -0.7 -0.2 6.7 24.1 23.4 6.4 2.6 0.2 2.4 1.0 0.8 3.6 0.272 0.307 0.374 0.681 99
Tyler Flowers CHA C C -0.7 -3.7 5.2 19.0 17.6 3.8 2.0 0.7 2.1 0.1 1.1 6.9 0.216 0.277 0.363 0.640 6
Chris Valaika CHN 2B 2B, SS, 3B -0.7 -0.8 1.8 7.4 7.1 1.6 0.6 0.1 0.6 0.1 0.3 1.6 0.232 0.278 0.328 0.606 0
Carlos Sanchez CHA SS SS -0.7 -0.8 3.5 15.6 14.9 3.8 1.6 0.1 1.3 0.4 0.7 3.3 0.255 0.304 0.342 0.646 0
Will Middlebrooks BOS 3B 3B -0.7 -0.5 3.6 14.8 14.0 3.2 1.5 0.5 1.7 0.1 0.7 4.0 0.226 0.274 0.375 0.649 20
Phil Gosselin ATL 2B 2B -0.8 -1.0 2.7 10.9 10.5 2.7 1.0 0.1 1.0 0.1 0.4 1.7 0.262 0.301 0.349 0.649 1
Jose Lobaton WAS C C -0.8 -1.0 1.7 6.6 6.2 1.4 0.5 0.1 0.6 0.0 0.4 1.7 0.219 0.283 0.334 0.618 0
Eric Young NYN OF OF -0.8 0.0 3.5 12.6 11.8 2.7 1.3 0.1 0.9 1.1 0.8 2.4 0.226 0.301 0.309 0.609 32
Eric Sogard OAK 2B, SS 2B, SS -0.8 -0.9 3.1 11.6 10.9 2.8 1.2 0.2 1.0 0.4 0.7 1.6 0.254 0.313 0.356 0.669 1
Wil Nieves PHI C C -0.9 -1.4 1.8 6.8 6.7 1.5 0.5 0.1 0.6 0.1 0.2 1.4 0.226 0.256 0.314 0.570 0
Ike Davis PIT 1B 1B -0.9 -0.4 4.2 14.2 12.7 3.3 1.6 0.5 1.8 0.1 1.3 3.2 0.260 0.353 0.430 0.783 11
Daniel Robertson TEX OF OF -0.9 -0.8 2.1 7.6 7.2 1.8 0.7 0.1 0.6 0.2 0.4 0.9 0.255 0.314 0.341 0.655 0
Travis Ishikawa SF 1B 1B -0.9 -0.8 2.6 9.7 9.2 2.3 1.0 0.2 1.1 0.1 0.6 2.4 0.253 0.314 0.380 0.694 0
Danny Espinosa WAS 2B 2B, SS -0.9 -1.0 1.6 5.7 5.5 1.1 0.5 0.1 0.5 0.1 0.2 1.9 0.202 0.249 0.325 0.574 6
Andrelton Simmons ATL SS SS -0.9 -2.8 6.5 25.7 24.5 6.5 2.5 0.5 2.5 0.4 1.1 2.2 0.264 0.314 0.381 0.695 73
Efren Navarro LAA 1B, OF 1B, OF -0.9 -0.8 2.1 8.1 7.5 1.9 0.8 0.1 0.7 0.1 0.5 1.3 0.257 0.321 0.355 0.676 0
Jedd Gyorko SD 2B 2B, 3B -0.9 -2.3 5.0 20.5 19.6 4.5 1.9 0.6 2.2 0.2 1.0 4.7 0.230 0.283 0.381 0.664 86
Drew Butera LAN C C -0.9 -1.0 1.3 5.1 4.8 0.9 0.3 0.1 0.4 0.0 0.2 1.1 0.186 0.234 0.267 0.501 0
Sam Fuld OAK OF OF -1.0 -0.4 3.5 15.2 14.0 3.3 1.5 0.1 1.2 0.8 1.1 1.5 0.233 0.308 0.320 0.628 5
Ichiro Suzuki NYA OF OF -1.0 -0.8 2.8 9.7 9.4 2.6 1.0 0.1 0.9 0.3 0.3 1.4 0.280 0.310 0.347 0.657 0
Chris Iannetta LAA C C -1.0 -4.5 4.9 18.6 16.5 3.8 2.0 0.4 1.9 0.1 2.0 5.1 0.229 0.335 0.353 0.687 1
Chris Nelson SD 3B 3B -1.1 -1.0 1.7 6.3 6.0 1.4 0.5 0.1 0.5 0.1 0.3 1.5 0.236 0.279 0.324 0.603 0
Marlon Byrd PHI OF OF -1.1 -0.9 5.2 21.4 20.4 4.8 2.0 0.7 2.5 0.1 0.8 5.7 0.236 0.279 0.392 0.671 100
Gregorio Petit HOU 2B SS -1.1 -1.4 3.0 10.9 10.3 2.6 1.1 0.2 1.1 0.1 0.5 1.8 0.257 0.310 0.386 0.697 0
Gregor Blanco SF OF OF -1.2 -0.8 3.5 13.6 12.5 3.2 1.5 0.1 1.2 0.5 0.9 2.8 0.254 0.328 0.337 0.665 0
Garrett Jones MIA 1B, OF 1B, OF -1.2 -0.9 5.1 20.6 19.3 4.5 2.1 0.6 2.4 0.1 1.2 5.0 0.235 0.305 0.393 0.697 40
Brock Holt BOS SS, 3B, OF 1B,3B,OF -1.2 -1.3 5.4 26.6 25.0 6.4 2.8 0.2 1.9 0.6 1.4 4.5 0.255 0.312 0.351 0.662 75
Rickie Weeks MIL 2B 2B -1.2 -1.4 2.2 8.0 7.3 1.6 0.7 0.2 0.7 0.1 0.5 2.3 0.212 0.286 0.340 0.625 5
Chris Coghlan CHN OF 3B, OF -1.3 -0.1 7.0 30.7 28.6 7.3 3.2 0.5 2.6 0.8 2.0 5.5 0.257 0.322 0.368 0.690 32
Abraham Almonte SD OF OF -1.3 -1.1 2.5 10.5 10.0 2.4 0.9 0.2 0.8 0.3 0.5 2.3 0.238 0.286 0.359 0.646 2
Aramis Ramirez MIL 3B 3B -1.3 -0.5 5.5 22.2 21.0 5.3 2.2 0.7 2.5 0.1 1.2 3.8 0.251 0.308 0.408 0.716 100
Tommy Medica SD 1B, OF 1B, OF -1.3 -1.2 2.6 10.5 9.9 2.2 0.9 0.3 1.0 0.1 0.5 2.8 0.218 0.279 0.362 0.641 17
Elian Herrera MIL SS, 3B, OF SS, 3B, OF -1.4 -1.4 1.9 6.8 6.4 1.5 0.6 0.0 0.5 0.2 0.4 1.4 0.234 0.286 0.294 0.579 0
Rymer Liriano SD OF OF -1.4 -1.2 2.5 9.9 9.4 2.1 0.8 0.2 0.9 0.3 0.5 2.7 0.224 0.277 0.331 0.609 20
Jordan Pacheco ARI C,1B C, 1B -1.4 -2.4 2.6 9.9 9.5 2.4 0.8 0.1 0.8 0.1 0.5 1.5 0.248 0.295 0.332 0.627 0
Matt den Dekker NYN OF OF -1.4 -1.2 3.2 11.8 11.1 2.7 1.0 0.2 1.1 0.4 0.6 2.5 0.246 0.299 0.360 0.658 0
Collin Cowgill LAA OF OF -1.4 -1.4 2.3 8.3 7.9 1.9 0.8 0.1 0.8 0.1 0.4 2.3 0.237 0.297 0.346 0.643 0
Jake Goebbert SD 1B 1B -1.4 -1.3 2.6 8.8 8.2 1.9 0.8 0.2 0.9 0.1 0.6 1.7 0.236 0.307 0.414 0.721 0
Logan Morrison SEA 1B 1B, OF -1.4 -0.8 5.4 20.6 19.1 4.6 2.2 0.6 2.4 0.2 1.4 3.5 0.241 0.320 0.411 0.731 13
Brandon Phillips CIN 2B 2B -1.5 -2.9 5.7 23.6 22.3 5.7 2.2 0.5 2.3 0.2 1.0 3.8 0.255 0.305 0.368 0.672 88
Miguel Rojas LAN SS, 3B SS, 3B -1.5 -1.5 1.7 5.9 5.7 1.2 0.4 0.0 0.4 0.1 0.2 0.9 0.208 0.253 0.265 0.518 0
John Baker CHN C C -1.6 -2.1 2.3 9.0 8.4 1.8 0.6 0.1 0.6 0.1 0.6 2.3 0.215 0.285 0.280 0.565 0
Nate Freiman OAK 1B 1B -1.6 -1.2 3.0 9.4 8.9 2.3 1.0 0.3 1.1 0.0 0.5 1.5 0.255 0.332 0.416 0.748 0
Eugenio Suarez DET SS SS -1.7 -2.3 5.2 18.9 17.8 4.3 2.0 0.3 1.9 0.5 1.0 4.3 0.243 0.301 0.356 0.657 4
Andy Parrino OAK SS 2B, SS -1.7 -1.9 2.5 8.8 8.2 1.9 0.8 0.1 0.8 0.1 0.5 1.8 0.234 0.304 0.323 0.627 0
Tommy La Stella ATL 2B 2B -1.7 -2.3 4.9 20.1 18.5 5.1 2.0 0.2 1.7 0.2 1.3 2.0 0.276 0.352 0.362 0.714 31
Mike Zunino SEA C C -1.9 -4.7 4.7 17.1 16.2 3.5 1.7 0.6 1.9 0.1 0.9 5.8 0.215 0.257 0.371 0.628 29
Jim Adduci TEX OF OF -1.9 -1.1 4.7 17.1 16.1 4.0 1.7 0.3 1.6 0.7 0.9 3.6 0.249 0.311 0.361 0.672 1
Chris Taylor SEA SS SS -1.9 -1.7 4.1 15.6 14.7 3.8 1.5 0.1 1.4 0.6 0.7 3.7 0.259 0.304 0.344 0.648 12
Justin Turner LAN 2B, SS, 3B 1B, 2B, SS, 3B -2.0 -2.5 3.6 14.4 13.6 3.5 1.3 0.2 1.3 0.2 0.8 2.5 0.259 0.311 0.352 0.663 33
Christian Vazquez BOS C C -2.0 -6.3 4.5 18.7 17.5 4.0 1.7 0.2 1.5 0.2 1.1 3.4 0.228 0.287 0.307 0.593 1
Grady Sizemore PHI OF OF -2.2 -2.0 3.4 13.0 12.4 2.9 1.2 0.3 1.2 0.2 0.6 2.6 0.238 0.287 0.373 0.660 10
David Freese LAA 3B 3B -2.2 -1.6 5.7 22.2 20.8 5.5 2.3 0.5 2.4 0.1 1.3 5.6 0.265 0.331 0.384 0.715 21
Conor Gillaspie CHA 3B 1B, 3B -2.4 -1.5 6.5 24.8 23.3 6.2 2.7 0.6 2.8 0.1 1.3 4.2 0.266 0.321 0.406 0.727 35
Khris Davis MIL OF OF -2.4 -2.1 4.5 17.2 16.1 3.6 1.7 0.6 1.9 0.2 1.0 4.1 0.223 0.288 0.397 0.686 100
Didi Gregorius ARI 2B, SS 2B, SS -2.4 -3.3 3.8 15.7 14.8 3.6 1.4 0.3 1.4 0.2 0.9 2.6 0.243 0.295 0.368 0.663 4
Skip Schumaker CIN 2B, OF 2B, OF -2.5 -2.7 3.0 12.0 11.2 2.9 1.0 0.1 1.0 0.1 0.6 2.1 0.258 0.313 0.334 0.647 0
Welington Castillo CHN C C -2.5 -4.9 5.7 22.7 21.2 5.0 2.0 0.6 2.2 0.1 1.3 5.9 0.235 0.298 0.361 0.659 1
Kristopher Negron CIN 2B 2B, 3B -2.5 -2.9 3.4 14.5 13.7 2.9 1.2 0.2 1.2 0.4 0.7 3.7 0.214 0.266 0.307 0.572 0
Brad Miller SEA SS 2B, SS -2.5 -3.8 5.1 17.4 16.5 4.1 1.9 0.4 1.8 0.3 0.9 3.7 0.247 0.303 0.386 0.689 18
Brendan Ryan NYA 2B, SS 2B, SS -2.5 -2.6 2.6 8.0 7.5 1.7 0.8 0.1 0.7 0.1 0.4 1.4 0.224 0.287 0.316 0.604 0
Scooter Gennett MIL 2B 2B -2.6 -3.3 4.5 16.6 16.0 4.2 1.6 0.2 1.5 0.3 0.5 2.7 0.263 0.302 0.365 0.666 57
Alfredo Marte ARI OF OF -2.6 -2.4 3.7 14.2 13.5 3.3 1.3 0.3 1.5 0.2 0.7 2.9 0.241 0.299 0.370 0.669 0
Donovan Solano MIA 2B 2B -2.6 -3.0 3.6 16.2 15.3 3.9 1.4 0.1 1.3 0.2 0.8 3.0 0.252 0.302 0.329 0.631 0
Ruben Tejada NYN SS SS -2.7 -2.9 2.9 10.8 10.1 2.4 0.9 0.1 0.8 0.1 0.6 1.7 0.242 0.310 0.309 0.619 0
Joe Panik SF 2B 2B -2.7 -3.4 5.5 23.0 21.8 5.9 2.2 0.2 2.2 0.2 1.0 2.4 0.271 0.318 0.348 0.666 15
Rene Rivera SD C C -2.7 -3.6 3.1 11.9 11.3 2.5 0.9 0.2 1.0 0.0 0.5 2.7 0.220 0.270 0.335 0.605 0
Yangervis Solarte SD 2B, 3B SS -2.7 -4.0 5.4 23.8 22.6 5.6 2.2 0.5 2.1 0.1 1.2 3.0 0.247 0.303 0.394 0.697 56
Ryan Sweeney CHN OF OF -2.7 -2.6 4.3 15.9 15.0 3.9 1.5 0.3 1.5 0.1 0.9 2.5 0.258 0.323 0.378 0.701 0
Oscar Taveras STL OF OF -2.7 -2.5 4.3 16.6 15.8 4.2 1.5 0.2 1.6 0.2 0.7 2.4 0.266 0.313 0.367 0.679 69
Ender Inciarte ARI OF OF -2.8 -1.5 5.4 24.8 23.7 5.8 2.3 0.3 1.8 0.9 0.9 4.0 0.243 0.286 0.337 0.623 45
Ramon Santiago CIN 2B, SS, 3B 2B, SS, 3B -2.8 -3.0 2.6 9.8 9.1 2.0 0.8 0.1 0.7 0.1 0.6 2.0 0.225 0.299 0.296 0.596 0
Luis Valbuena CHN 2B, 3B 2B, 3B -2.9 -4.7 6.9 27.4 25.1 6.0 2.8 0.7 2.8 0.1 2.1 5.4 0.240 0.322 0.380 0.701 20
Daniel Nava BOS OF 1B, OF -2.9 -2.6 4.7 18.9 17.2 4.2 2.0 0.4 1.8 0.2 1.6 4.0 0.244 0.331 0.370 0.701 1
Ryan Ludwick CIN OF OF -2.9 -2.7 4.7 19.9 18.6 4.5 1.8 0.5 2.0 0.1 1.2 5.2 0.239 0.298 0.369 0.667 2
Jeff Mathis MIA C C -2.9 -3.3 2.5 8.8 8.3 1.6 0.6 0.2 0.7 0.0 0.5 2.6 0.199 0.257 0.299 0.556 0
Jose Molina TB C C -2.9 -4.4 3.4 11.3 10.6 2.3 1.0 0.1 1.0 0.1 0.7 2.4 0.217 0.276 0.295 0.570 0
Tyler Holt CLE OF OF -3.0 -2.4 4.3 16.2 15.1 4.0 1.6 0.0 1.3 0.6 1.0 3.2 0.263 0.325 0.339 0.664 0
Wilmer Flores NYN SS, 3B 2B, SS, 3B -3.0 -4.3 4.6 16.5 16.0 3.9 1.4 0.4 1.7 0.1 0.5 2.5 0.244 0.279 0.365 0.644 1
Roberto Perez CLE C C -3.1 -4.8 4.0 14.7 13.4 2.9 1.5 0.2 1.3 0.1 1.2 3.4 0.216 0.300 0.319 0.620 0
Gerardo Parra MIL OF OF -3.2 -2.7 4.5 18.4 17.3 4.4 1.8 0.3 1.5 0.4 0.9 3.5 0.251 0.306 0.354 0.660 14
DJ LeMahieu COL 2B 2B, 3B -3.4 -4.2 5.7 21.2 20.6 5.6 1.8 0.2 1.9 0.6 0.6 3.3 0.274 0.300 0.355 0.655 13
Yunel Escobar TB SS SS -3.5 -5.7 6.7 27.8 25.8 6.5 2.7 0.4 2.6 0.2 1.7 3.5 0.250 0.326 0.346 0.672 0
Gordon Beckham LAA 2B 2B -3.5 -4.5 5.1 19.9 19.0 4.6 2.0 0.3 1.9 0.2 0.9 3.3 0.242 0.292 0.351 0.643 15
Stephen Drew NYA 2B, SS 2B, SS -3.6 -5.5 5.1 20.6 19.0 4.1 2.1 0.5 2.1 0.2 1.4 5.5 0.214 0.285 0.336 0.620 7
Mark Reynolds MIL 1B, 3B 1B, 3B -3.7 -3.3 4.3 15.4 14.1 2.8 1.5 0.6 1.7 0.1 1.3 5.2 0.200 0.289 0.391 0.680 42
Andre Ethier LAN OF OF -3.7 -3.5 4.5 16.8 15.8 3.8 1.6 0.4 1.8 0.1 1.1 3.5 0.243 0.304 0.393 0.697 11
Alberto Callaspo OAK 1B, 2B, 3B 1B, 2B, 3B -3.8 -4.7 5.6 22.4 20.8 5.4 2.2 0.3 2.1 0.1 1.5 2.7 0.259 0.329 0.355 0.684 1
Casey McGehee MIA 3B 3B -4.1 -3.3 5.7 24.8 22.9 6.0 2.3 0.4 2.5 0.1 1.6 4.1 0.261 0.330 0.370 0.700 83
Cody Asche PHI 3B 3B -4.2 -3.7 5.5 20.1 19.2 4.8 1.8 0.6 2.2 0.2 0.7 4.2 0.250 0.293 0.395 0.688 10
Jordy Mercer PIT 2B, SS 2B, SS -4.3 -5.4 4.8 18.9 18.2 4.6 1.6 0.3 1.7 0.2 0.8 3.7 0.251 0.291 0.353 0.644 43
Jean Segura MIL SS SS -4.3 -3.9 4.6 16.4 15.9 3.8 1.4 0.2 1.3 0.8 0.6 2.4 0.236 0.279 0.336 0.616 81
Endy Chavez SEA OF OF -4.8 -4.4 4.1 14.7 14.3 3.7 1.4 0.2 1.2 0.2 0.5 2.1 0.257 0.287 0.338 0.625 0
Brandon Crawford SF SS SS -5.1 -7.1 6.3 23.9 22.3 5.5 2.3 0.4 2.5 0.2 1.7 4.9 0.246 0.315 0.360 0.675 12
Alexi Amarista SD 2B, SS, 3B, OF 2B, SS, 3B, OF -5.2 -5.8 4.9 19.2 18.3 4.3 1.6 0.3 1.6 0.4 0.7 3.0 0.236 0.280 0.365 0.645 6
Jacob Lamb ARI 3B 3B -5.3 -4.9 4.2 16.7 15.8 3.6 1.4 0.4 1.6 0.0 0.9 4.6 0.231 0.292 0.366 0.658 0
Munenori Kawasaki TOR 2B, SS, 3B 2B, SS, 3B -5.3 -5.6 3.8 13.3 12.5 3.1 1.3 0.1 1.1 0.2 0.7 2.2 0.251 0.312 0.344 0.655 0
Cameron Maybin SD OF OF -6.1 -5.5 4.5 15.5 14.6 3.6 1.3 0.2 1.3 0.4 0.7 3.4 0.246 0.290 0.347 0.636 0
A.J. Ellis LAN C C -6.7 -8.9 4.7 17.6 16.1 3.6 1.5 0.2 1.5 0.1 1.5 3.8 0.224 0.321 0.319 0.639 0
Tony Cruz STL C C -7.0 -9.7 5.2 18.5 17.3 4.0 1.5 0.2 1.6 0.1 1.0 3.6 0.231 0.290 0.322 0.612 0
Juan Lagares NYN OF OF -8.1 -7.4 6.3 24.0 23.2 5.9 2.0 0.3 2.1 0.4 0.7 5.0 0.256 0.288 0.351 0.639 2
Peter Bourjos STL OF OF -8.4 -7.7 6.4 21.9 20.5 4.8 2.1 0.4 2.1 0.5 1.3 5.2 0.235 0.299 0.363 0.662 5
Zack Cozart CIN SS SS -8.9 -10.5 5.5 20.5 19.7 4.7 1.7 0.3 1.8 0.1 0.8 3.7 0.237 0.279 0.340 0.619 13
Adeiny Hechavarria MIA SS SS -9.8 -10.3 5.7 23.1 22.0 5.5 1.8 0.1 1.8 0.4 0.8 3.9 0.250 0.287 0.328 0.615 11
Geovany Soto OAK C C 0.6 2.1 1

Hittertron Info

Razzball Hittertron (aka Hitter-Tron):  This tool is designed to identify attractive short-term hitter pickups and to determine when to start/sit hitters on your roster.  (For Daily Fantasy Baseball games, check out DFSBot)  The higher the $ value, the more attractive the start.  Hittertron projections rely on Steamer Rest of Season projections as a foundation and then adjust based on several game-specific factors that include quality of opposing starting pitcher, hitter’s performance vs. the handedness of opposing starting pitcher, park factors, whether the game is home or away, and predicted batting order spot (for Runs, RBIs, and Plate Appearances).

What Is The Expected Accuracy Of Hittertron Projections?:  Please see the Razzball Ombotsman for correlations between the Hittertron projections and actual stats.

Filtering Results:  You can filter multiple fields at the same time.  The text fields below the column headers enable several methods for filtering the data.  Here are some examples:

Function Symbol Example Explanation
ANY MATCH ‘B’ in Pos Typing B in Pos will filter to any player with 1B, 2B, or 3B eligibility.  Type in more details to filter further – e.g., “1B’, “1B, 3B”, etc.
OR | 2B|SS Requires exact match on both sides – so 2B|SS returns anyone who has 2B or SS eligibility but not anyone with 2B/SS, 2B/3B, etc.
NOT ! !OF All players who do not have OF eligibility.
NOR ! | !1B|OF All players who do not have 1B eligibility NOR OF eligibility.  Just use the ! once.
GREATER THAN > >30 in $ All players whose $ is greater than 30.  Does not work for Date.
LESS THAN < <30 in $ All players whose $ is less than 30.  Does not work for Date.
GREATER THAN OR EQUAL TO >= >=30 in $ All players whose $ is greater than or equal to 30.  Does not work for date.
LESS THAN OR EQUAL TO <= <=30 in $ All players whose $ is less than or equal to 30.  Does not work for Date.

Position Eligibility - 20 Games in last season for ESPN and ’2 Catcher’ formats.   5 Games for Yahoo.

$ Values - A hitter’s projected stats for the game are multiplied by 150 and then valued for a 12-team MLB league using the ESPN/CBSSports roster format (13 hitters, 9 pitchers) and a $260 team budget.  While the $ values would vary for any other league format, we would expect the rankings to remain relatively the same.   For 12-team leagues, $8 is about the ‘average’ hitter start. The ‘$’ estimate takes position value into account.  The ‘$U’ estimate is position-neutral and is the value to use when looking at players for Utility slots.

Own%  Based on ownership within the Razzball Commenter Leagues which consists of 84 12-team MLB leagues using the standard ESPN roster format.

 

  1. franky2times says:
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    This looks like the wrong place to post comments but first of all, everything on this site is the tits, and is there or will there ever be some sort of tool where i can input my whole time and rank by $ value for the week ahead?

    • @franky2times: thanks. i’ll start valuing readers’ next 7 days when grey starts covering that in the roundup. “franky2times – Didn’t even leave the house on Sunday. Add him if your team needs help in counting stats like ‘Eating Cheetos’ and ‘Leisurely defecation’.

  2. Note: Removed SLG from this table as it was causing display issues on iPads (the table went behind the ad on the right side). The SLG data can be found on the player pages as well as the weekly hitter data.

  3. Note: Removed SLG from this table as it was causing display issues on iPads (the table went behind the ad on the right side). The SLG data can be found on the player pages as well as the weekly hitter data.

  4. Richard Kenno says:
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    It seems today for some reason, I can’t access the Razzball tools spreadsheet lists, e.g. Hitter-Tron, Stream-o-Nator, etc. but am seeing the comments. Are the lists down temporarily? Thanks for the hard work, these tools are amazing!

  5. ZACJAMESBITCH says:
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    the hittertron is the best tool in fantasy but what the hell is with its obsession with David freaking Murphy?

    • @ZACJAMESBITCH: Thanks. It likes Murphy this week b/c they have seven home games (in a great hitter park) and they face only 2 lefties.

      • Kevin S says:
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        @Rudy Gamble:

        I bet it also has to do with how well he played last year. This year he has been terrible but all the back data shows him as a better player.

  6. goodfold2 says:
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    it appears avila is expected to get less at bats and less total stats for the next 7 days than Ruiz, but yet rates higher at his position. The only way this makes sense is to say that the more time a shitty stats gathering catcher gets, the worse he is for the team. I’m trying to find a catcher better than Zunino. I used to have Ruiz (sidenote, why does Hitter T project a better slugging for Ruiz than Avila/Ellis/Zunino? He seems to have the worst slugging in real life this season)
    best catchers out there are
    ruiz (probably best avg, plays almost every day, but worst power, probably most likely to be injured)
    avila (more likely to get homers, in good lineup, also likeliest to have worst avg,biggest upside)
    ellis (playing the best right now, lineup’s hot,but sits out the most too)
    zunino (gets off 2nd most days of these guys, has terrible avg, on pretty weak lineup too, he’s the dropped one, but for which of the above?)
    This is a 16 team h2h league with hits/avg/total bases all as categories. That probably helps Ruiz. Grey pretty much says they’re all crap but just play the hottest hitting currently. Which would be Ellis.

    • goodfold2 says:
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      @goodfold2: another factor i just noticed. in my league next “week” is actually both the next 7 day week plus the 3 days after all star break, so fri-sun 19-21. The only reason this affects anything is that we still only have 5 weekly pickups in this the 10 day week. Further complicating matters is i’m using probably nearly all 5 of these (one of them the catcher one) late tonight. I have needs, such as
      1. Aramis injury (who knows how much time he misses over next few weeks.) have to pickup Beckham (moved up to 2nd in order today, got his usual 2 hits) and move Dj lemehieu to 3rd (or wallace at 3rd) dropping Rutledge (which will leave me without backup at SS for Aybar, but he pretty much never sits anyway) since Rutledge pretty much doesn’t perform anymore and only plays like 60% of the time, usually in the worst part of the order.
      2. Dropping Kendrick’s ugly ass lately for Kluber yet again
      3.dropping either Robertson/Smyly/Fausto Carmona (after his Monday start)/Wallace for Porcello after his great start recently.
      4. Span getting hits, and more importantly the next 3 guys also are, upping his run scoring value again, so dropping Quentin (at home, where he sucks, for next 7 days) for Span.
      5. Catcher flip in question

  7. hankp101 says:
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    It looks like some players are missing from the hittertron. I’m looking at the weekly version and both Pujols and Bautista are missing from my roster.

  8. malamoney says:
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    I feel like HitterTron should include projected strikeouts.

  9. malamoney says:
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    Also, where can I find past results? I’d like to see how accurate HitterTron and Stream-o-nator were last week and the week before, etc.

    • @malamoney: Still on the long-term to-do list. Not easy to display that type of data in a way that’s going to be relevant to our readers.

      • malamoney says:
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        @Rudy Gamble: My suggestion would be just to list the actual results of each column in a column next to the projection column.

        So you have Games, PA, AB, H, R, HR, RBI, SB, BB, SO

        Why not add one extra column for each of the above the contains the actual values and let the users (consumers) of your tools do what they want with it.

        I can do this manually, but it is a nightmare. I am guessing you have better access to daily stats in which you could put together a few functions to populate these columns.

        Without this data, I have no way of know how accurate these tools are. What I typically do is cut and paste your data into a spreadsheet and plug the projections into a formula based on my league’s scoring system to figure out how many fantasy points each will have for the week. I use this to help me decide on my lineup for the week.

        I do this each week (just started this season) so I can manually look at what HitterTron and Streamonator projected vs. what each of my players actually did. This helps me get an idea of their accuracies, but it’s only a small sample size of players (just the guys on my roster).

        I love these tools, or at least the concept of them, but think they a few tweaks away from being awesome.

        • The reality is that the vast majority of our users do not know how to gauge the accuracy of projections vs actual results. I would rather present the results of such tests than encourage false conclusions.

          I can already tell you that daily data is only slightly predictive (at best) once you account for the underlying hitter/pitcher talent (e.g., predicting Miggy will be better than Don Kelly isn’t an accomplishment). My goal is to leverage the various daily factors like park, opposing pitcher/lineup to improve upon each player’s baseline which should net out – in the long run – to a slight advantage (in less time and more effectively) than most people can do by following their gut.

          • malamoney says:
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            @Rudy Gamble: Weekly prediction results would be fine. If HitterTron says Ryan Braun is going to put up the following line this week:

            23AB, 9H, 3HR, 7RBI, 1SB, 5R, 3BB, 6K

            At the end of the week, I’d like to be able to easily compare that line to his actual line. And when I say easily, I am asking if it is possible for you put the data next to each other.

            On the player details pages under Projections, you have “Next 7 Days (HitterTron”), why not have a section called “Projection Results” that shows “Last 7 Days (HitterTron”) and under that “Last 7 Days (Actual)”?

            • @malamoney: I will work on a way that provides a statistically sound way to compare the accuracy of the projections. I am not going to clutter the player pages to do so. The ‘test’ you want to do – “how far off were the projections last day/week” – are going to fail miserably for every baseball projection source forever. The sample sizes are too small. Look at Miggy Cabrera’s last 7 days. I can guarantee he was probably a top 5 projected hitter. Massively off! There is no value to be gained here.

              If you disagree, you are more than welcome to copy the ‘Next 7 Day’ projections on Mondays and the ‘Last 7 Day’ projections the following Monday to compare. It’ll take you 5 minutes a week to cut/paste.

              • malamoney says:
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                @Rudy Gamble: Where do I find “Last 7 Days (Actual)” for a given player?

                I agree that there is no value in comparing daily stat projections. However, weekly stats are another story, especially since most of us play in weekly leagues.

                Why do we use tools such as HitterTron and Stream-o-nator? Because they help us. Or at least we think or hope they help us. Perhaps they do, but it also just as possible that they do not. How would I know?

                I use the tools to help push me one way or another when I am on the fence about who to start, but I’d really like to know if it makes sense for me to continue using the tools.

                I am not trying to be insulting. I think what you have here is potentially awesome and I respect the effort you put into it, but I think it’s possible to make these tools even more powerful and back them up with proof that validate their awesomeness. At the end of the day fancy numbers don’t impress me, results do.

                If I am trying to decide whether to start Gyorko or Moss I’d like to turn to HitterTron to tell me what to do. So let’s say it says to start Moss. At the end of the week I’d like see if HitterTron did me a solid or not. That I can do easily.

                I think weekly is the best unit of stat projection measurement. Daily is way too small and monthly becomes an aggregate that will mask accurate and inaccurate weeks.

                Think of players like stocks. Each week you project the players performance for that week. At the end of the week you evaluate your projections. Perhaps define a threshold for defining successful accuracy. Define a formula for determining a player’s performance. In my case I use our league’s scoring system. I plug in your projections and get a projected fantasy points value. Then at the end of the week compare. If HitterTron was within let’s say 20%, then it was a success. If not, a failure. Perhaps success and failure are too strong of words, but you get the point. You could also indicate that whether HitterTron’s projections were too high or too low.

                So for a player you might see:

                Paul Goldschmidt’s HitterTron Results:

                Week 1: Projected: 29 points, Actual 33, -13% (green because within 20% threshold)
                Week 2: Projected: 35, Actual 25, +28% (red)
                Week 3: Projected: 42, Actual 42, The Price Is Right!

                Season: Projected: 106, Actual 100, +5% (green)

                I think something like this would be extremely valuable.

                Anyway, just the ramblings of a fantasy baseball loving computer programmer.

                • @malamoney: Last 7 Day and Last 30 Day Stats are accessible via ‘Stats’ in the main menu (7 day is http://razzball.com/mlbhittingstats-last7days/). You can also access it by clicking the ‘Last 7 Days’ hyperlink from the Player Page.

                  I agree that a weekly accuracy comparison will have less sample-related noise than daily (though not that much less) and I already have a solid metric to compare against (my 5×5 $). I also want to compare for each hitting category to communicate which are more reliable than others.

                  But we disagree on your proposed method above. You cannot really make the comparison at the player level and pretend it has any relevance. Miguel Cabrera’s last week is the perfect example. The ‘accuracy’ test has to be done on the aggregate of players (perhaps removing players who didn’t start). If you look at the weekly projections, you can see that it’s a game of decimals – e.g, 1.6 HR vs. 1.8 HR. If you focus on one player, all you’ll see is statistical noise. If I can present it in the aggregate, the noise will be neutralized enough that you can make some statistical assumptions.

                  Just to give you some perspective, my rankings tests that compare pre-season rankings against end of year rankings (http://razzball.com/fantasy-baseball-rankings-review-2013/) show that a rankings system is LUCKY to even correlate at 20% for hitting. Only 3 of 17 ranking systems hit that mark! While daily/weekly has the advantage of factoring in the opposing pitcher and doesn’t get penalized by injured players (e.g., 2013 Kemp), the smaller samples are debilitating. My point here is that if you looked at the pre-season ranking systems (or projections) and compared against each player, you’d never be able to tell which system was more accurate. Too much noise.

                  So to do this test right – and to have it update every day/week – takes some thinking. Once I figure it out, I can guarantee I’ll have the most transparently tested system out there. Whether that will inspire more/less confidence in you given the abstractness of the necessary testing (e.g., correlation testing), will be another matter.

                  • malamoney says:
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                    @Rudy Gamble: I find very little value in aggregate stats as I feel they have too strong a potential to mask issues. An aggregate is like an average. I’m not looking for averages. Here’s an example. Let’s say we are looking at home run projections for 10 players:

                    Projected (these are made up projections):
                    Braun 25, Goldschmidt 25, Jones 20, Chris Davis 44, Posey 15, Fielder 48, Cespedes 23, Cabrera 40, Moss 15 and Gyorko 15

                    Actual:
                    Braun 35, Goldschmidt 35, Jones 30, Chris Davis 20, Posey 25, Fielder 20, Cespedes 33, Cabrera 25, Moss 25 and Gyorko 25

                    If you take the aggregate of the above the projections say 270HR and the actual is 273HR. That would point to some excellent, and accurate, projections. But if you look at each player, the projections are actually horrible and misleading and most importantly, useless.

                    When a fantasy owner is evaluating a player they are doing it at a per player level. They are trying to decide whether to draft one player over another, whether to start one player over another and/or whether to trade one player for another. The emphasis and importance needs to be on the per player level. Not daily stats, but last 7, last 30, season-to-date, next 7, next 30, rest of season.

                    I just don’t see there be any value in aggregate stat projections, especially across different players. I think the best way to evaluate a projection system is to say something like “this system was very accurate on 150 of the 350 (43%) players it provided projections for”. Perhaps even have a breakdown of accuracy levels: very accurate, accurate, not very accurate, way off.

                    Very accurate: 70 of 350 players (20%)
                    Accurate: 120 of 350 players (34%)
                    Not very accurate: 100 of 350 players (28%)
                    Way off: 60 of 350 players (17%)

                    Perhaps you can click on each of the above and expand to show a list of the players that fell into that category.

                    What is your formula for calculating your 5×5 $ value?

                    Would you consider adding “Last 7 days (HitterTron)” to the projections section of the player details page? It’s just one row, but it puts all the player data on the same page.

                    • When I say testing in aggregate, it is not adding up all the stats and comparing. A correlation test (which is what I’m thinking of doing) looks at each player comparison and determines how closely the two data sets compare. The test ranges from 100% to -100% with 100% meaning the values are perfectly aligned and -100% meaning they are completely negative aligned. Assuming that the sums are right (and I’m not projected, like, 4x the SB), this test should provide solid guidance for the accuracy of the projections. Bucketing them as you’re suggesting will not be effective. As I’ve mentioned before, with a little cutting/pasting, you can evaluate my projections how you see fit. But I’m not going to clutter the player pages or build extra reports for site visitors to facilitate incorrect judgments.

                      Sorry if that comes off dickish. I think fantasy baseball is an awesome mix of statistical rigor, common sense, and ‘gut’. Based on this thread, there’s no statistical rigor that is likely to satisfy your gut and your gut is not able to measure accuracy with any statistical rigor. So either you can believe in the science or just have faith that it works. Or use your gut more in making the final decisions. All fine by me.

    • I have archived them but we do not display on the interface.

  10. Charles says:
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    What time are these updated each morning? I use them for weekly Baseball Challenge but have to enter my lineup early Monday mornings.

    • malamoney says:
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      @Charles: Agreed. I find that on Monday mornings, it still includes Sunday’s games until about noon EST. Any chance of having the cutover be around midnight so that Monday morning it’s fresh for the coming week?

      • @malamoney: The updates happen usually by 10:30AM EST. I’m looking into a way that I can project ‘Next Calendar Week’ starting on Friday or Saturdays (requires some guesswork/extrapolation on probable starters).

  11. steve says:
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    Hey Rudy

    Alcides Escobar is on fire, but Hitter Tron views his next 7 very poorly. How do you utilize Hitter Tron when you see a (possible) discrepancy like this one? Is there a way to include some measure/value to see how well hitter tron has been doing with regard to it’s prognosis?

    It predicted the Neil Walker breakout last week!

    Thanks man.
    Steve

  12. Eddy says:
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    Hey Rudy,

    Was the feature of comparing multiple players by putting a | in between removed? Can’t seem to get it to work.

    • @Eddy: It’s still there but you now have to type in each player’s full name on each side of the ‘|’

  13. Simply Fred

    simply fred says:
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    Rudy, i have a simple mind. for next 7, in a 5X5 league, i add up the $values for R,HR,RBI,SB (sans avg since a $ isn’t provided)…expecting to get a total number relative to the ‘$’column.

    for Goldschmidt i get:
    R=4.2
    HR=.8
    RBI=2.6
    SB=.3
    tot=7.9
    avg=.286

    for J.D.Martinez i get:
    R=5.7
    HR=.8
    RBI=3.1
    SB=.2
    tot=8.8
    avg=.289

    so, sum for Martinez at 8.8 is greater than Goldy at 7.9.

    yet, $ for Goldy = 26.3, for Martinez = 22.8

    with avg nearly identical, doesn’t make sense to me that the sum doesn’t equal the parts…?

    bottom line, it makes more sense to me to rely on the sum of the identifiable parts, rather than the $ value (which isn’t quite as clear to me from the formula)…?

    • @simply fred: The R/HR/RBI/SB/AVG data are the projections not $ but you are right that the $ estimation is sub-optimal. Long story short, was doing an average $ for the week vs doing a complete $ calculation like I do with Player Rater and ‘Next Calendar Week’. Looks like my adjustment to handle extra games (Detroit has a doubleheader) is insufficient. When I have a chance, will need to create a ‘next 7 day’ player rater to get the $ right

  14. Simply Fred

    simply fred says:
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    still trying to figure streaming hitters…
    jaso ranks #22 for the next 7 days (i am set to add him).
    why don’t you have him rostered over guys you currently have:
    arcia #139
    adduci #170
    valdespin #176
    ? (he would fit your utility slot)
    (btw: did get francisco, #127 and odor, #108 today for a nice return!)

    • @simply fred: for RCL, I’m all about daily value. I’ve got Rosario right now at C and Rutledge at UT so no room to consider another catcher. I used Navarro yesterday as fill-in catcher because i knew he was starting and hitting cleanup (lineup posted). When in doubt, grab the sure things (Ruggiano ended up with an 0 for 0 b/c he didn’t end up starting).

      Nice on Juan-Fran! My streaming sucked yesterday aside from Navarro.

  15. Simply Fred

    simply fred says:
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    thanks! daily answers it.

    streaming daily has it’s downside. when those matchups look tasty, but there is an early start and the manager sits our guy, left with nuttin’ for that slot. (most of the time can juggle, but occasionally on a fabulous kayaking junket :-) and can’t get at the lineup :-(

    having fun!!

  16. Chuckles Tiddlesworth says:
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    Hey Rudy! Not sure if this is simply a quirk of me using the “Next 7 days” sort … but seems your “H” and “R” column headers are switched. Everyone is scoring more runs than they have hits. Probably the opposite, eh? That said, Hittertron rules. Thanks so much for this. You guys are the best.

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