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%
Jose Bautista TOR 1B, OF 1B, OF 33.7 34.5 6.7 28.9 25.4 7.1 4.7 2.0 5.3 0.3 3.2 5.1 0.281 0.400 0.553 0.953 100
Miguel Cabrera DET 1B, 3B 1B, 3B 28.4 30.2 6.3 26.8 24.6 8.1 4.3 1.4 5.0 0.1 1.9 4.7 0.330 0.408 0.582 0.991 100
Edwin Encarnacion TOR 1B 1B, 3B 27.8 33.0 6.7 28.2 25.6 7.1 4.4 2.0 5.4 0.1 2.3 4.3 0.279 0.370 0.546 0.917 100
Josh Donaldson OAK 3B 3B 26.2 26.9 6.6 29.8 27.3 7.6 4.3 1.5 4.8 0.4 2.3 4.6 0.277 0.362 0.504 0.866 100
Andrew McCutchen PIT OF OF 23.1 24.5 6.7 28.1 25.8 7.5 4.0 1.2 4.2 0.8 2.0 4.8 0.290 0.375 0.499 0.874 100
Jose Abreu CHA 1B 1B 22.9 27.6 6.4 27.4 25.6 7.5 4.0 1.8 5.0 0.1 1.7 6.0 0.291 0.359 0.555 0.914 100
Adrian Beltre TEX 3B 3B 21.8 24.0 6.7 27.4 25.9 8.3 3.9 1.3 4.7 0.0 1.3 3.7 0.319 0.371 0.527 0.898 100
Nelson Cruz BAL OF OF 21.7 22.5 6.7 28.3 26.8 6.8 3.8 1.7 4.6 0.3 1.5 6.4 0.255 0.309 0.474 0.783 100
Bryce Harper WAS OF OF 20.3 21.3 6.7 28.2 26.0 7.9 3.8 1.1 4.0 0.5 2.0 6.2 0.303 0.379 0.516 0.894 100
Victor Martinez DET 1B 1B 20.2 23.6 6.7 28.3 26.4 8.4 4.0 1.2 4.5 0.1 1.7 3.2 0.317 0.381 0.504 0.885 100
Jose Altuve HOU 2B 2B 20.2 18.7 5.7 25.7 24.7 7.6 3.4 0.3 2.6 1.4 0.9 2.7 0.308 0.346 0.427 0.772 100
Jose Reyes TOR SS SS 19.4 18.6 6.7 30.2 28.7 8.4 4.2 0.6 3.0 1.1 1.2 3.8 0.291 0.347 0.437 0.784 100
Ian Kinsler DET 2B 2B 18.7 16.1 6.7 30.3 28.6 7.9 4.3 0.9 3.5 0.6 1.6 3.8 0.276 0.327 0.437 0.764 100
Anthony Rendon WAS 2B, 3B 2B, 3B 18.7 16.5 6.7 30.3 28.4 8.4 4.1 0.7 3.4 0.7 1.5 4.5 0.294 0.356 0.455 0.811 100
Yoenis Cespedes BOS OF OF 18.3 18.9 5.3 22.9 22.1 6.4 3.1 1.0 3.9 0.3 0.9 5.4 0.290 0.332 0.526 0.858 99
Ryan Braun MIL OF OF 17.7 18.7 5.2 22.4 20.7 6.1 3.1 1.0 3.3 0.6 1.4 4.8 0.295 0.363 0.517 0.880 100
Jacoby Ellsbury NYA OF OF 17.6 19.8 5.9 25.3 23.7 6.9 3.6 0.7 3.1 1.6 1.3 3.6 0.289 0.350 0.444 0.794 90
Mike Trout LAA OF OF 17.1 18.5 5.7 24.8 22.5 6.3 3.7 0.9 3.2 0.8 2.1 6.2 0.278 0.362 0.474 0.836 100
Brian McCann NYA C,1B C, 1B 17.1 13.8 6.5 27.1 25.1 6.7 3.6 1.3 4.1 0.0 1.9 4.2 0.267 0.341 0.477 0.818 99
Jonathan Lucroy MIL C,1B C, 1B 16.6 9.9 5.7 25.0 23.3 6.7 3.1 0.7 3.0 0.2 1.5 3.6 0.286 0.356 0.450 0.806 100
Coco Crisp OAK OF OF 16.5 17.9 5.8 27.2 25.3 7.0 4.2 0.8 3.2 0.9 1.8 3.4 0.275 0.347 0.438 0.785 93
Ian Desmond WAS SS SS 16.1 14.5 6.3 27.4 26.1 7.3 3.1 0.7 3.5 1.0 1.3 6.2 0.279 0.324 0.432 0.756 100
Dustin Pedroia BOS 2B 2B 15.9 14.2 5.7 25.5 23.9 7.1 3.6 0.5 3.1 0.6 1.4 3.2 0.298 0.359 0.446 0.805 63
Mookie Betts BOS OF 2B, OF 15.9 17.6 5.4 23.6 22.3 6.8 3.2 0.6 2.7 1.2 1.1 3.2 0.302 0.358 0.456 0.815 94
Carlos Beltran NYA OF OF 15.7 16.2 6.3 26.2 24.3 6.9 3.6 1.1 3.9 0.2 1.7 4.3 0.284 0.352 0.494 0.846 55
Derek Norris OAK C C 15.7 10.4 6.3 25.5 23.2 5.9 3.4 1.1 3.6 0.1 2.2 5.1 0.255 0.352 0.445 0.797 40
Jayson Werth WAS OF OF 15.6 16.4 6.3 27.4 24.9 7.5 3.6 0.9 3.6 0.5 2.1 5.7 0.300 0.385 0.468 0.854 100
Denard Span WAS OF OF 15.6 17.8 6.7 31.6 30.1 9.3 4.2 0.3 2.5 1.4 1.4 3.1 0.309 0.356 0.415 0.771 99
J.D. Martinez DET OF OF 15.4 16.0 6.7 28.3 26.9 7.6 3.6 1.1 4.2 0.3 1.3 6.8 0.283 0.330 0.484 0.814 100
Leonys Martin TEX OF OF 15.4 17.5 6.3 27.5 26.2 7.6 3.4 0.5 3.3 1.4 1.2 5.1 0.290 0.338 0.425 0.763 100
Matt Holliday STL OF OF 15.4 15.8 5.7 24.3 22.2 6.5 3.5 1.0 3.6 0.1 1.9 4.2 0.291 0.374 0.485 0.859 99
Robinson Cano SEA 2B 2B 15.2 12.7 6.7 26.8 24.9 7.5 3.4 0.8 3.7 0.3 1.5 3.2 0.302 0.361 0.471 0.832 100
Torii Hunter DET OF OF 14.2 14.6 6.2 27.5 26.3 7.7 3.7 0.9 3.7 0.2 0.9 5.3 0.294 0.338 0.463 0.801 100
Carlos Gomez MIL OF OF 13.9 15.4 4.7 21.3 20.0 5.4 2.9 0.8 2.7 1.1 1.3 4.9 0.268 0.331 0.459 0.791 100
David Ortiz BOS DH 1B 13.8 14.7 4.7 19.6 18.0 5.3 3.0 1.0 3.4 0.0 1.6 3.8 0.295 0.385 0.562 0.946 100
Chris Carter HOU 1B, OF 1B, OF 13.7 14.2 5.4 23.2 21.1 4.9 3.1 1.4 3.7 0.2 1.7 8.1 0.231 0.322 0.480 0.802 95
Adam Jones BAL OF OF 13.0 13.6 6.1 25.2 24.4 6.7 3.1 1.1 3.8 0.4 0.8 4.9 0.277 0.305 0.461 0.767 100
Carlos Santana CLE C, 1B, 3B C, 1B, 3B 13.0 8.1 5.7 25.5 22.0 5.5 3.2 0.9 3.3 0.1 2.9 4.8 0.250 0.376 0.453 0.829 100
Adam LaRoche WAS 1B 1B 12.8 15.2 6.7 28.2 25.6 7.2 3.7 1.1 4.1 0.2 2.3 5.9 0.283 0.376 0.488 0.863 96
Asdrubal Cabrera WAS 2B, SS 2B, SS 12.5 9.7 6.7 27.6 25.9 7.3 3.4 0.7 3.2 0.5 1.4 4.5 0.282 0.342 0.443 0.785 98
Javier Baez CHN 2B, SS 2B, SS 12.2 8.9 5.7 24.6 23.3 5.2 2.9 1.1 2.7 0.8 1.0 8.3 0.224 0.268 0.399 0.667 89
Alex Rios TEX OF OF 12.0 13.6 6.3 24.7 23.9 6.9 3.1 0.6 3.3 1.1 0.7 4.5 0.291 0.324 0.444 0.768 70
Brandon Moss OAK 1B, OF 1B, OF 11.9 12.3 6.3 23.6 21.7 5.4 3.3 1.3 3.9 0.1 1.6 6.1 0.249 0.333 0.475 0.808 86
Allen Craig BOS 1B, OF 1B, OF 11.8 12.1 5.5 22.1 21.0 6.3 2.9 0.8 3.3 0.1 1.0 5.3 0.299 0.351 0.492 0.843 46
Ben Zobrist TB 2B, SS, OF 2B, SS, OF 11.5 9.5 5.7 26.3 23.9 6.7 3.6 0.5 2.9 0.4 2.2 4.3 0.282 0.363 0.418 0.781 99
Anthony Rizzo CHN 1B 1B 11.5 12.6 5.4 21.7 20.0 5.4 2.9 1.1 3.2 0.3 1.7 4.2 0.270 0.348 0.500 0.848 100
Aramis Ramirez MIL 3B 3B 11.3 12.6 5.7 23.9 22.3 6.2 2.9 1.0 3.5 0.1 1.4 3.9 0.277 0.344 0.467 0.811 98
Neil Walker PIT 2B 2B 11.3 8.7 6.7 28.1 26.3 7.0 3.4 0.9 3.7 0.1 1.6 4.7 0.267 0.332 0.436 0.768 100
Adrian Gonzalez LAN 1B 1B 11.2 14.9 5.7 24.9 23.6 6.7 3.0 1.1 3.7 0.0 1.3 4.4 0.283 0.345 0.478 0.823 100
Rajai Davis DET OF OF 11.0 13.3 5.9 23.2 22.3 6.3 3.0 0.4 2.4 1.8 0.8 4.2 0.281 0.319 0.406 0.725 96
Matt Kemp LAN OF OF 10.9 11.7 5.7 23.8 22.4 6.0 2.7 1.0 3.3 0.4 1.3 5.7 0.269 0.326 0.442 0.768 100
David Wright NYN 3B 3B 10.7 10.4 5.7 24.4 22.7 6.4 2.9 0.7 3.0 0.4 1.6 5.0 0.280 0.352 0.434 0.787 67
Starling Marte PIT OF OF 10.5 12.6 6.7 26.2 24.9 6.7 3.2 0.7 3.0 1.3 1.3 7.4 0.267 0.315 0.427 0.742 100
Jhonny Peralta STL SS SS 10.3 6.5 5.6 23.2 21.8 5.7 2.8 0.8 3.2 0.1 1.5 4.7 0.260 0.324 0.417 0.742 100
Evan Longoria TB 3B 3B 10.3 11.5 5.7 24.0 22.1 5.9 3.1 1.0 3.6 0.1 1.7 5.4 0.266 0.339 0.466 0.805 100
Khris Davis MIL OF OF 10.0 10.4 5.3 21.8 20.1 5.1 2.8 1.1 3.3 0.2 1.4 5.3 0.256 0.326 0.461 0.787 79
Josh Harrison PIT 2B, 3B, OF 2B, SS, 3B, OF 9.9 8.4 5.7 25.8 24.9 6.8 3.2 0.5 2.7 0.8 0.8 4.2 0.272 0.306 0.419 0.724 100
Michael Brantley CLE OF OF 9.8 10.7 5.6 25.2 23.5 6.9 2.9 0.6 3.0 0.5 1.5 3.3 0.292 0.357 0.433 0.789 100
Mike Napoli BOS 1B 1B 9.8 11.8 5.0 21.2 19.0 5.1 2.9 1.0 3.4 0.1 1.8 6.5 0.269 0.360 0.495 0.855 87
Yasiel Puig LAN OF OF 9.7 10.4 4.8 21.8 20.3 5.7 2.8 0.7 2.5 0.5 1.3 4.8 0.280 0.350 0.452 0.802 100
Alex Gordon KC OF OF 9.6 10.4 6.7 27.5 25.2 6.5 3.5 1.0 3.6 0.4 2.1 6.1 0.257 0.340 0.443 0.783 100
Buster Posey SF C,1B C, 1B 9.5 4.9 6.6 27.3 25.7 7.3 2.9 0.8 3.2 0.1 1.5 4.0 0.282 0.341 0.432 0.773 100
Josh Reddick OAK OF OF 9.2 9.8 6.1 22.9 21.4 5.7 3.0 1.1 3.5 0.3 1.4 4.9 0.264 0.319 0.455 0.775 43
Hanley Ramirez LAN SS SS 9.2 7.1 5.4 21.2 19.8 5.3 2.4 0.7 2.5 0.6 1.3 3.5 0.269 0.335 0.428 0.762 100
Todd Frazier CIN 1B, 3B 1B, 3B 9.2 8.2 5.7 23.1 21.6 5.3 2.7 0.9 2.9 0.6 1.3 5.8 0.247 0.316 0.436 0.752 100
Dioner Navarro TOR C C 9.1 5.1 5.6 22.4 21.0 5.6 2.6 0.8 3.0 0.1 1.0 3.9 0.268 0.326 0.418 0.744 33
Justin Upton ATL OF OF 8.9 9.7 6.7 26.7 24.3 6.4 3.4 1.0 3.5 0.4 2.2 7.7 0.262 0.352 0.450 0.802 100
Russell Martin PIT C C 8.5 1.0 6.1 25.0 22.7 5.3 2.7 0.8 2.9 0.3 2.1 4.6 0.233 0.325 0.376 0.701 79
Billy Hamilton CIN OF OF 8.4 11.7 5.4 22.2 21.1 5.3 2.4 0.3 1.5 2.5 1.0 4.8 0.249 0.295 0.346 0.641 98
Avisail Garcia CHA OF OF 8.2 9.1 6.3 26.8 25.7 6.8 3.0 0.8 3.4 0.6 1.0 5.8 0.262 0.309 0.414 0.723 65
Kyle Seager SEA 3B 3B 8.1 8.0 6.7 26.1 24.2 6.4 3.2 0.9 3.4 0.4 1.3 5.3 0.263 0.328 0.430 0.758 100
Christian Yelich MIA OF OF 7.9 9.6 6.7 30.6 28.7 7.8 3.8 0.6 2.5 1.0 1.7 6.8 0.272 0.336 0.398 0.734 100
Pedro Alvarez PIT 3B 1B, 3B 7.8 8.5 6.3 23.6 22.0 5.3 2.8 1.2 3.6 0.3 1.4 7.5 0.242 0.308 0.446 0.754 57
Dee Gordon LAN 2B, SS 2B, SS 7.8 11.7 5.6 27.2 26.1 7.0 2.8 0.1 1.8 2.2 0.9 4.6 0.270 0.301 0.334 0.634 100
Rougned Odor TEX 2B 2B 7.6 5.8 6.2 24.4 23.6 6.5 2.7 0.5 2.9 0.7 0.6 4.7 0.276 0.308 0.413 0.721 43
Jed Lowrie OAK 2B, SS 2B, SS 7.6 3.6 6.7 25.7 24.0 6.6 3.2 0.7 3.3 0.0 1.6 4.1 0.273 0.345 0.434 0.779 58
Brett Gardner NYA OF OF 7.5 8.9 5.9 24.4 22.5 6.1 3.3 0.5 2.5 1.0 1.8 5.3 0.272 0.350 0.419 0.769 95
Desmond Jennings TB OF OF 7.4 9.1 5.7 25.7 23.8 5.9 3.3 0.5 2.6 1.0 1.7 5.9 0.248 0.319 0.380 0.699 61
Eric Hosmer KC 1B 1B 7.3 7.2 6.7 26.0 24.3 6.4 3.2 0.8 3.4 0.4 1.6 4.8 0.262 0.330 0.440 0.769 93
Brian Dozier MIN 2B 2B 7.0 4.9 6.5 27.5 25.7 6.3 3.4 0.7 2.8 0.7 1.7 4.8 0.244 0.310 0.386 0.696 99
Jason Heyward ATL OF OF 7.0 7.9 5.8 24.7 22.4 5.9 3.2 0.8 2.7 0.6 2.0 5.6 0.265 0.352 0.424 0.776 92
Adam Lind TOR 1B 1B 7.0 10.3 6.3 23.9 22.3 6.3 3.2 1.0 3.7 0.0 1.4 5.0 0.283 0.348 0.484 0.832 67
Elvis Andrus TEX SS SS 6.9 8.4 6.4 27.6 26.1 7.5 3.4 0.1 2.6 1.4 1.3 3.8 0.286 0.342 0.369 0.711 93
Alexei Ramirez CHA SS SS 6.7 5.5 6.4 26.8 25.9 6.8 3.0 0.5 2.7 0.8 0.8 3.3 0.262 0.302 0.389 0.692 99
Yadier Molina STL C C, 1B 6.7 3.7 4.6 18.4 17.6 5.0 2.2 0.4 2.5 0.1 0.8 2.7 0.286 0.338 0.433 0.771 94
Devin Mesoraco CIN C C 6.7 3.2 4.7 18.6 17.3 4.2 2.1 0.8 2.5 0.1 1.1 4.7 0.244 0.314 0.439 0.753 92
Wilson Ramos WAS C C 6.7 5.0 5.3 21.5 20.7 6.0 2.3 0.8 2.8 0.0 0.6 3.0 0.289 0.327 0.449 0.776 75
Dayan Viciedo CHA OF OF 6.6 6.8 6.3 24.7 23.6 6.1 2.9 1.1 3.5 0.0 1.0 4.8 0.258 0.303 0.439 0.742 39
Josh Willingham KC OF OF 6.4 6.8 6.7 27.5 24.2 5.3 3.4 1.4 3.8 0.1 3.1 8.6 0.220 0.333 0.404 0.737 25
Daniel Murphy NYN 2B, 3B 1B, 2B, 3B 6.4 5.0 5.7 25.7 24.6 7.0 2.9 0.4 2.4 0.5 0.9 3.3 0.286 0.320 0.397 0.718 88
Jason Kipnis CLE 2B 2B 6.3 4.9 5.2 22.3 20.4 5.2 2.6 0.5 2.2 0.8 1.6 4.9 0.255 0.338 0.397 0.736 92
Steve Pearce BAL 1B, OF 1B, OF 6.3 6.6 4.8 19.5 18.0 4.6 2.7 0.8 2.7 0.2 1.4 3.9 0.256 0.336 0.453 0.789 86
Dexter Fowler HOU OF OF 6.2 7.2 5.7 25.7 23.1 6.0 3.3 0.5 2.7 0.6 2.3 5.9 0.259 0.355 0.393 0.749 64
Carl Crawford LAN OF OF 6.1 7.4 5.2 21.7 21.0 6.0 2.2 0.4 2.4 1.0 0.7 4.0 0.284 0.316 0.417 0.733 96
Freddie Freeman ATL 1B 1B 6.0 8.6 6.7 28.1 25.6 7.2 3.6 0.9 3.5 0.1 2.3 6.6 0.283 0.367 0.445 0.812 100
Albert Pujols LAA 1B 1B 5.9 8.3 5.7 23.0 21.6 5.7 2.9 0.9 3.2 0.1 1.3 3.4 0.264 0.323 0.450 0.773 100
Mark Trumbo ARI 1B, OF 1B, OF 5.9 6.5 5.7 23.1 21.8 5.4 2.4 1.0 3.1 0.2 1.0 6.7 0.249 0.296 0.446 0.742 99
Will Middlebrooks BOS 3B 3B 5.7 6.2 5.0 19.6 19.0 5.0 2.4 0.8 2.9 0.2 0.7 5.1 0.261 0.298 0.452 0.750 24
Derek Jeter NYA SS SS 5.6 4.5 6.1 26.7 25.3 7.3 3.2 0.3 2.6 0.5 1.2 4.3 0.289 0.339 0.376 0.715 21
Austin Jackson SEA OF OF 5.5 6.7 6.7 28.8 26.8 7.3 3.6 0.5 2.7 0.8 1.6 7.4 0.273 0.334 0.401 0.736 94
Salvador Perez KC C C 5.5 2.8 6.7 26.8 25.6 6.7 3.0 0.9 3.4 0.0 0.9 3.9 0.260 0.303 0.408 0.711 94
Mark Teixeira NYA 1B 1B 5.5 8.9 5.9 24.5 22.0 5.4 3.2 1.2 3.6 0.0 2.3 5.4 0.243 0.345 0.468 0.813 71
Jay Bruce CIN OF OF 5.1 5.8 5.1 19.6 18.1 4.4 2.3 0.9 2.6 0.4 1.4 5.3 0.244 0.317 0.458 0.775 83
Danny Valencia TOR 1B, 3B 1B, 2B, 3B 5.1 5.9 5.8 22.6 21.6 5.7 2.7 0.9 3.1 0.2 0.8 4.8 0.263 0.298 0.422 0.720 2
Matt Carpenter STL 2B, 3B 2B, 3B 5.1 3.9 5.3 24.4 22.3 6.0 3.4 0.4 2.2 0.2 1.9 4.2 0.271 0.355 0.401 0.756 99
Kolten Wong STL 2B 2B 5.0 3.6 5.1 20.2 19.3 4.9 2.3 0.5 2.1 0.7 0.8 3.4 0.256 0.298 0.376 0.674 81
Ryan Zimmerman WAS 3B, OF 3B, OF 4.9 5.9 6.7 23.6 22.1 6.5 2.9 0.7 3.3 0.1 1.3 4.7 0.292 0.355 0.469 0.824 77
Chase Headley NYA 3B 1B, 3B 4.9 4.8 5.8 21.1 19.4 5.2 2.7 0.7 2.8 0.4 1.7 4.5 0.269 0.361 0.440 0.801 77
Ben Revere PHI OF OF 4.9 7.2 5.6 25.7 24.8 7.1 2.7 0.1 1.7 1.6 0.7 2.9 0.285 0.314 0.329 0.643 100
Alejandro De Aza BAL OF OF 4.8 5.8 5.3 22.4 21.2 5.7 2.7 0.5 2.3 0.8 1.1 4.7 0.270 0.318 0.408 0.726 87
Joe Mauer MIN C,1B C, 1B 4.7 1.0 6.5 26.9 25.0 6.9 3.1 0.4 3.0 0.1 1.8 6.0 0.277 0.354 0.385 0.738 100
Evan Gattis ATL C, OF C, OF 4.6 2.8 5.3 21.1 20.0 4.9 2.3 0.9 2.9 0.0 1.1 4.7 0.244 0.301 0.443 0.743 75
Xander Bogaerts BOS SS, 3B SS, 3B 4.4 2.5 4.7 18.6 17.7 4.8 2.2 0.5 2.3 0.2 0.9 4.5 0.270 0.320 0.428 0.748 98
Danny Santana MIN SS, OF SS, OF 4.4 4.4 5.9 26.2 25.5 6.6 3.0 0.4 2.3 1.0 0.6 4.7 0.260 0.285 0.355 0.641 100
Chase Utley PHI 2B 2B 4.3 2.5 5.7 24.5 22.7 5.9 2.7 0.6 2.6 0.4 1.6 3.5 0.259 0.332 0.388 0.720 100
Lucas Duda NYN 1B, OF 1B, OF 4.2 4.5 5.4 23.1 20.9 5.1 2.5 0.9 2.9 0.1 1.9 5.2 0.243 0.335 0.420 0.755 95
Chris Young NYA OF OF 4.1 4.4 3.4 14.1 12.9 3.2 1.9 0.7 2.0 0.3 1.1 3.2 0.247 0.326 0.431 0.757 65
Dustin Ackley SEA 2B, OF 1B, 2B, OF 3.8 2.4 5.5 23.4 21.7 5.5 2.7 0.6 2.5 0.4 1.3 4.1 0.251 0.319 0.391 0.710 57
Colby Rasmus TOR OF OF 3.7 3.9 5.3 19.5 18.2 4.4 2.4 1.0 2.7 0.1 1.2 5.9 0.243 0.309 0.449 0.758 51
Gregory Polanco PIT OF OF 3.6 5.0 6.0 23.5 22.3 5.6 2.8 0.5 2.4 1.0 1.1 3.8 0.252 0.304 0.375 0.679 38
Alex Avila DET C C 3.6 0.6 5.3 21.4 19.3 4.7 2.4 0.7 2.6 0.1 1.9 6.0 0.241 0.331 0.375 0.706 1
Robbie Grossman HOU OF OF 3.6 4.6 5.0 23.3 21.3 5.3 2.7 0.5 2.1 0.7 1.9 6.0 0.249 0.333 0.361 0.694 5
Luis Valbuena CHN 2B, 3B 2B, 3B 3.5 1.7 5.4 21.7 19.9 4.9 2.4 0.7 2.7 0.2 1.8 5.0 0.244 0.327 0.413 0.740 58
Nick Markakis BAL OF OF 3.4 3.7 5.6 25.0 23.5 6.3 3.1 0.6 2.6 0.1 1.4 2.8 0.266 0.328 0.397 0.725 85
Matt Adams STL 1B 1B 3.4 4.8 4.6 19.2 18.3 4.8 2.2 0.7 2.8 0.1 0.7 3.9 0.263 0.301 0.441 0.742 90
Kevin Pillar TOR OF OF 3.3 4.0 4.4 16.8 16.3 4.6 2.1 0.3 2.0 0.7 0.6 2.5 0.286 0.318 0.422 0.740 0
Jake Marisnick HOU OF OF 3.1 4.2 5.2 20.3 19.4 4.7 2.2 0.6 2.3 0.7 0.8 5.4 0.243 0.289 0.380 0.669 43
Arismendy Alcantara CHN 2B, OF 2B, OF 2.9 1.4 5.3 21.2 20.2 4.8 2.1 0.5 2.0 0.8 0.9 5.4 0.239 0.284 0.384 0.667 61
Jorge Soler CHN OF OF 2.9 3.1 4.2 16.0 15.2 4.0 1.8 0.6 2.2 0.1 0.8 4.0 0.263 0.319 0.452 0.771 100
Jose Ramirez CLE 2B, SS 2B, SS 2.9 4.4 5.4 25.2 23.8 6.2 2.5 0.2 2.0 1.3 1.1 3.9 0.261 0.310 0.357 0.667 37
Daniel Nava BOS 1B, OF 1B, OF 2.8 3.2 5.1 19.5 18.2 5.2 2.5 0.4 2.4 0.2 1.3 4.5 0.284 0.361 0.434 0.795 7
Adam Dunn OAK 1B 1B, OF 2.5 4.8 5.3 20.9 18.7 4.0 2.7 1.1 3.0 0.1 2.0 6.0 0.213 0.322 0.414 0.736 52
Norichika Aoki KC OF OF 2.5 3.6 5.9 24.6 23.0 6.4 3.1 0.2 2.1 0.8 1.5 2.7 0.280 0.347 0.395 0.742 83
Yan Gomes CLE C C 2.4 0.9 4.5 19.2 18.1 4.7 2.1 0.6 2.2 0.0 0.9 5.0 0.257 0.310 0.423 0.734 94
Wil Myers TB OF OF 2.4 2.9 4.9 21.2 19.6 4.9 2.4 0.6 2.5 0.3 1.3 4.9 0.251 0.319 0.407 0.726 73
Eduardo Nunez MIN SS, 3B, OF SS, 3B, OF 2.3 1.8 3.3 13.4 12.9 3.4 1.4 0.4 1.5 0.4 0.5 3.7 0.264 0.299 0.379 0.677 0
Mike Zunino SEA C C 2.3 -0.2 4.6 17.0 16.0 3.5 1.9 0.7 2.3 0.1 0.9 5.7 0.215 0.271 0.404 0.675 29
Jimmy Rollins PHI SS SS 2.2 2.0 5.4 24.3 22.6 5.3 2.7 0.4 2.0 0.9 1.5 4.1 0.235 0.305 0.348 0.653 77
Aaron Hill ARI 2B 2B, 3B 2.1 0.4 5.7 23.1 21.9 5.7 2.4 0.6 2.5 0.2 0.9 4.3 0.260 0.300 0.398 0.697 65
Sam Fuld OAK OF OF 2.1 3.1 4.9 20.4 18.9 4.8 2.6 0.2 2.1 0.9 1.3 4.0 0.253 0.329 0.373 0.702 10
Curtis Granderson NYN OF OF 2.1 2.9 5.5 23.2 21.1 4.8 2.7 0.8 2.5 0.4 1.9 6.3 0.227 0.316 0.400 0.716 87
Carlos Ruiz PHI C C 2.0 -0.9 4.3 17.5 16.2 4.2 1.7 0.4 1.8 0.1 1.2 2.6 0.259 0.339 0.377 0.716 11
Brock Holt BOS 2B, SS, 3B, OF 1B,3B,OF 2.0 1.9 2.7 12.8 12.2 3.6 1.7 0.1 1.1 0.3 0.5 2.1 0.293 0.337 0.400 0.737 49
Marcell Ozuna MIA OF OF 2.0 2.5 6.7 27.9 27.0 6.7 2.7 0.9 3.4 0.3 1.0 7.5 0.250 0.290 0.406 0.696 100
Stephen Vogt OAK C, 1B, OF C, 1B, OF 2.0 1.7 2.0 8.3 7.9 2.5 1.2 0.3 1.4 0.0 0.3 0.9 0.317 0.354 0.509 0.863 35
Corey Dickerson COL OF OF 2.0 2.8 5.5 23.5 22.4 5.5 2.2 0.7 2.4 0.5 0.9 4.8 0.245 0.299 0.450 0.749 100
Chris Coghlan CHN OF 3B, OF 2.0 2.9 5.4 23.9 22.1 5.8 2.6 0.4 2.3 0.6 1.4 5.0 0.260 0.324 0.397 0.721 32
Lonnie Chisenhall CLE 1B, 3B 1B, 3B 2.0 2.6 5.3 21.3 19.9 5.2 2.4 0.7 2.5 0.1 1.2 4.3 0.261 0.323 0.431 0.753 75
Travis d’Arnaud NYN C C 2.0 0.2 5.1 20.4 19.1 4.9 2.1 0.7 2.4 0.0 1.0 3.5 0.259 0.310 0.416 0.726 38
James Loney TB 1B 1B 1.9 2.7 5.5 22.4 21.3 6.0 2.6 0.4 2.6 0.1 1.1 3.1 0.281 0.338 0.407 0.745 83
Kendrys Morales SEA 1B 1B 1.9 5.3 6.7 26.1 24.6 6.7 3.0 0.9 3.7 0.0 1.3 5.2 0.270 0.320 0.442 0.762 54
Justin Morneau COL 1B 1B 1.9 3.4 5.6 23.9 22.6 6.1 2.4 0.8 2.8 0.1 1.3 3.6 0.269 0.322 0.459 0.781 100
Logan Morrison SEA 1B 1B, OF 1.8 2.0 4.3 16.5 15.1 3.8 2.0 0.6 2.1 0.2 1.1 2.2 0.254 0.332 0.430 0.762 23
Christian Vazquez BOS C C 1.7 -1.6 4.4 17.2 16.6 4.4 1.9 0.2 1.8 0.2 0.7 3.6 0.263 0.310 0.376 0.686 1
Jean Segura MIL SS SS 1.6 1.5 4.4 15.8 15.2 4.1 1.7 0.3 1.6 0.7 0.7 2.6 0.269 0.315 0.381 0.696 75
Jarrod Dyson KC OF OF 1.6 2.2 2.6 10.3 9.6 2.4 1.2 0.1 0.8 1.0 0.7 2.4 0.249 0.319 0.362 0.681 37
Tomas Telis TEX C C 1.6 -1.3 4.4 17.5 17.1 4.8 1.7 0.1 1.9 0.2 0.4 1.7 0.283 0.307 0.381 0.687 0
Kole Calhoun LAA OF OF 1.5 2.2 5.4 23.8 22.5 5.6 2.7 0.6 2.2 0.4 1.2 4.9 0.247 0.299 0.380 0.678 100
J.J. Hardy BAL SS SS 1.5 -1.0 5.1 19.8 19.1 4.8 2.1 0.6 2.4 0.0 0.7 3.4 0.250 0.288 0.390 0.678 81
Ryan Rua TEX OF 1B, OF 1.5 1.8 5.0 20.0 19.0 5.1 2.2 0.5 2.5 0.1 1.0 4.3 0.270 0.319 0.420 0.739 6
J.P. Arencibia TEX C,1B C, 1B 1.5 1.0 2.8 10.7 10.1 2.4 1.3 0.5 1.6 0.0 0.3 2.8 0.236 0.275 0.441 0.717 7
Jonathan Villar HOU SS SS 1.5 2.5 5.1 18.3 17.2 4.1 1.9 0.4 1.8 1.3 1.0 5.0 0.238 0.292 0.345 0.637 13
Kevin Kiermaier TB OF OF 1.4 2.2 4.8 18.2 17.3 4.6 2.2 0.3 2.1 0.5 0.9 3.6 0.266 0.317 0.397 0.714 14
Nick Castellanos DET 3B 3B, OF 1.4 2.4 6.5 27.8 26.5 7.2 2.9 0.6 3.3 0.1 1.3 6.6 0.271 0.320 0.419 0.740 71
Marcus Semien CHA 2B, 3B 2B, 3B 1.4 0.9 3.1 12.5 11.6 2.7 1.4 0.4 1.5 0.3 0.9 3.4 0.234 0.315 0.403 0.719 7
Chris Owings ARI 2B, SS 2B, SS 1.4 0.7 5.1 21.6 21.1 5.4 2.0 0.4 2.0 0.6 0.4 4.3 0.258 0.280 0.371 0.651 43
Andre Ethier LAN OF OF 1.3 1.6 4.5 19.0 17.6 4.8 1.9 0.5 2.2 0.1 1.1 3.4 0.272 0.337 0.421 0.758 11
Jason Castro HOU C C 1.3 -0.4 4.8 19.8 18.3 4.4 2.1 0.7 2.4 0.0 1.3 5.0 0.238 0.307 0.380 0.687 37
Hunter Pence SF OF OF 1.3 2.1 6.7 27.6 26.3 6.7 2.9 0.7 2.9 0.5 1.4 5.5 0.253 0.305 0.399 0.704 100
Eric Sogard OAK 2B, SS 2B, SS 1.2 1.1 2.9 9.9 9.2 2.8 1.3 0.1 1.2 0.4 0.5 0.8 0.300 0.354 0.427 0.780 0
Travis Snider PIT OF OF 1.2 1.3 3.9 14.8 14.0 3.7 1.8 0.5 1.8 0.1 0.8 3.5 0.262 0.317 0.419 0.736 21
Ryan Flaherty BAL 2B, SS, 3B 2B, SS, 3B 1.2 0.0 3.5 14.1 13.4 3.1 1.5 0.5 1.6 0.1 0.6 3.5 0.232 0.280 0.381 0.661 6
Omar Infante KC 2B 2B 1.2 0.0 6.0 24.8 23.7 6.3 2.6 0.4 2.5 0.4 0.9 3.5 0.268 0.310 0.378 0.688 37
Corey Hart SEA 1B OF 1.1 2.8 5.5 20.0 18.7 4.8 2.5 0.8 2.8 0.1 1.2 4.4 0.256 0.318 0.448 0.766 24
Charlie Blackmon COL OF OF 1.1 2.5 5.6 25.6 24.4 6.1 2.4 0.6 1.9 0.9 1.1 4.6 0.249 0.291 0.386 0.677 100
Scooter Gennett MIL 2B 2B 1.1 0.0 5.3 21.7 20.6 5.9 2.3 0.3 2.0 0.3 0.6 3.4 0.284 0.317 0.398 0.716 54
Oswaldo Arcia MIN OF OF 1.0 1.2 6.1 23.5 22.4 5.4 2.6 0.9 3.1 0.1 1.1 6.6 0.241 0.293 0.407 0.700 77
Ichiro Suzuki NYA OF OF 1.0 1.7 5.0 18.3 17.7 5.0 2.1 0.2 2.0 0.7 0.7 2.2 0.285 0.333 0.381 0.714 0
Randal Grichuk STL OF OF 0.9 0.9 1.8 7.2 6.9 1.7 0.9 0.4 1.1 0.0 0.3 2.9 0.241 0.279 0.414 0.693 0
Matt Joyce TB OF OF 0.9 1.4 5.5 20.4 18.5 4.8 2.5 0.6 2.5 0.2 1.7 4.3 0.259 0.349 0.422 0.771 35
Danny Espinosa WAS 2B 2B, SS 0.9 0.3 3.5 13.5 12.7 3.0 1.5 0.4 1.6 0.4 0.8 4.4 0.237 0.295 0.373 0.668 6
Wilin Rosario COL C C 0.8 -1.8 4.5 18.3 17.6 4.4 1.6 0.6 1.8 0.1 0.7 4.1 0.247 0.276 0.386 0.662 93
Josh Phegley CHA C C 0.8 0.1 3.5 13.9 13.3 3.3 1.4 0.5 1.7 0.0 0.5 2.0 0.248 0.286 0.410 0.696 0
Adam Eaton CHA OF OF 0.8 1.8 5.7 24.1 22.5 6.1 3.0 0.2 1.9 0.8 1.4 3.8 0.269 0.338 0.368 0.706 89
Miguel Montero ARI C C 0.8 -1.0 5.2 20.4 18.7 4.8 2.2 0.6 2.1 0.0 1.4 4.5 0.258 0.339 0.399 0.739 76
Matthew Clark MIL 1B 1B 0.7 1.3 2.1 7.4 6.9 1.8 0.9 0.4 1.1 0.0 0.5 1.6 0.264 0.327 0.479 0.806 0
Robinson Chirinos TEX C C 0.7 0.2 2.6 10.3 9.7 2.5 1.2 0.3 1.2 0.0 0.5 2.7 0.258 0.315 0.414 0.730 2
Brandon Phillips CIN 2B 2B 0.7 -0.7 5.4 20.6 19.5 5.1 2.1 0.5 2.3 0.2 0.9 3.9 0.261 0.309 0.394 0.703 76
Gerardo Parra MIL OF OF 0.6 1.2 4.5 18.9 17.6 4.7 2.1 0.3 1.9 0.4 1.0 3.2 0.268 0.326 0.394 0.720 32
Alex Presley HOU OF OF 0.6 0.7 2.1 8.2 7.8 2.2 1.1 0.2 1.0 0.1 0.4 1.2 0.283 0.335 0.434 0.769 0
Ike Davis PIT 1B 1B 0.6 1.1 3.4 10.9 9.9 2.6 1.4 0.5 1.6 0.1 1.0 2.6 0.262 0.354 0.457 0.811 13
Michael Saunders SEA OF OF 0.5 0.6 2.2 7.7 7.1 1.8 1.0 0.3 0.9 0.2 0.6 2.0 0.251 0.333 0.433 0.767 7
Michael Choice TEX OF OF 0.5 0.7 4.0 14.6 13.7 3.6 1.9 0.4 1.7 0.1 0.8 3.7 0.266 0.331 0.423 0.755 0
Lorenzo Cain KC OF OF 0.5 1.7 6.1 23.7 22.3 5.7 2.5 0.4 2.4 0.9 1.4 5.0 0.256 0.313 0.373 0.686 93
Zach Walters CLE 3B OF 0.4 0.5 2.6 9.8 9.3 2.3 1.1 0.4 1.3 0.1 0.4 3.3 0.243 0.283 0.434 0.717 4
Kennys Vargas MIN 1B 1B 0.4 3.5 6.3 27.5 26.4 6.5 3.0 0.9 3.5 0.0 1.2 7.4 0.246 0.291 0.398 0.688 90
Ezequiel Carrera DET OF OF 0.4 0.4 0.8 3.7 3.5 1.0 0.3 0.0 0.3 0.2 0.2 1.0 0.286 0.340 0.380 0.720 0
Kelly Johnson BAL 1B, 2B, 3B, OF 1B, 2B, 3B, OF 0.4 0.3 1.4 5.9 5.5 1.3 0.7 0.2 0.7 0.1 0.4 1.6 0.233 0.306 0.409 0.716 10
Jimmy Paredes BAL 3B, OF 3B, OF 0.4 0.1 2.1 8.1 7.9 2.0 0.8 0.1 0.9 0.3 0.2 2.7 0.247 0.280 0.386 0.665 12
Josmil Pinto MIN C C 0.4 0.1 1.7 6.9 6.4 1.6 0.7 0.2 0.8 0.0 0.4 1.2 0.247 0.312 0.400 0.712 2
Mark Reynolds MIL 1B, 3B 1B, 3B 0.4 0.5 2.0 6.8 6.1 1.4 0.8 0.4 1.0 0.0 0.7 2.0 0.228 0.331 0.454 0.785 27
Michael Taylor CHA OF OF 0.3 0.3 2.1 7.9 7.2 1.9 0.9 0.4 1.0 0.0 0.6 0.5 0.258 0.331 0.402 0.732 1
David Peralta ARI OF OF 0.3 0.5 3.4 13.9 13.4 3.8 1.3 0.3 1.4 0.2 0.4 1.8 0.281 0.311 0.424 0.736 23
Paul Konerko CHA 1B 1B 0.3 0.5 1.7 7.1 6.6 1.7 0.8 0.3 0.9 0.0 0.5 0.5 0.257 0.329 0.420 0.749 1
Andrew Lambo PIT OF OF 0.3 0.3 1.4 5.3 5.0 1.3 0.7 0.2 0.8 0.1 0.2 0.9 0.251 0.303 0.436 0.738 0
Brandon Guyer TB OF OF 0.2 0.3 2.0 8.5 8.0 2.1 0.9 0.1 0.9 0.2 0.4 1.5 0.263 0.316 0.377 0.693 0
David Ross BOS C C 0.2 0.1 1.5 5.5 5.1 1.2 0.7 0.2 0.7 0.0 0.3 2.1 0.233 0.294 0.411 0.705 0
Erik Kratz KC C C 0.2 0.2 0.7 2.3 2.1 0.5 0.3 0.2 0.4 0.0 0.1 0.5 0.230 0.294 0.409 0.703 0
David Lough BAL OF OF 0.2 0.2 1.7 6.4 6.2 1.7 0.7 0.1 0.7 0.2 0.2 0.9 0.269 0.301 0.396 0.698 1
Craig Gentry OAK OF OF 0.2 0.7 3.1 12.6 11.7 3.1 1.4 0.0 1.2 0.7 0.8 2.8 0.266 0.337 0.358 0.695 0
Nick Franklin TB 2B 2B, SS 0.2 -0.1 2.6 10.2 9.5 2.3 1.1 0.2 1.0 0.2 0.6 2.5 0.242 0.316 0.377 0.694 5
Starlin Castro CHN SS SS 0.2 -1.3 5.7 21.3 20.4 5.5 2.2 0.4 2.3 0.3 0.8 3.9 0.270 0.309 0.397 0.706 74
Joc Pederson LAN OF OF 0.2 0.3 1.0 4.0 3.7 0.9 0.4 0.1 0.4 0.2 0.3 1.1 0.238 0.319 0.389 0.708 14
Emilio Bonifacio ATL 2B, OF 2B, 3B, OF 0.1 -0.3 4.0 17.1 16.0 4.1 1.8 0.1 1.2 0.9 0.8 3.8 0.255 0.305 0.318 0.623 32
Juan Francisco TOR 1B, 3B 1B, 3B 0.1 0.1 0.8 2.6 2.5 0.6 0.3 0.1 0.4 0.0 0.1 1.0 0.236 0.303 0.463 0.765 19
Kirk Nieuwenhuis NYN OF OF 0.1 0.1 1.1 4.9 4.6 1.1 0.5 0.2 0.5 0.1 0.3 1.0 0.233 0.296 0.389 0.685 0
Nate Schierholtz WAS OF OF 0.1 0.1 1.3 5.1 4.9 1.3 0.5 0.1 0.7 0.1 0.2 1.2 0.270 0.314 0.439 0.752 8
John Mayberry TOR 1B, OF 1B, OF 0.1 0.1 1.3 4.6 4.3 1.1 0.5 0.2 0.5 0.0 0.3 0.4 0.256 0.318 0.433 0.751 0
Anthony Recker NYN C C 0.1 0.1 0.8 3.3 3.1 0.7 0.3 0.2 0.5 0.0 0.2 1.2 0.218 0.278 0.368 0.646 0
Michael Bourn CLE OF OF 0.1 1.5 5.6 26.3 24.6 6.4 3.0 0.1 2.0 0.9 1.6 6.8 0.260 0.321 0.361 0.682 56
Jemile Weeks BOS 2B 2B 0.1 0.1 1.0 3.9 3.7 1.0 0.5 0.0 0.5 0.1 0.2 0.2 0.283 0.345 0.400 0.744 0
Don Kelly DET 1B, 3B, OF 1B, 3B, OF 0.1 0.0 1.8 7.1 6.6 1.7 0.8 0.2 0.7 0.1 0.5 0.7 0.252 0.325 0.356 0.680 0
Eugenio Suarez DET SS SS 0.1 -0.5 4.3 16.6 15.6 3.9 1.8 0.3 1.8 0.4 0.9 3.8 0.249 0.302 0.368 0.670 2
Jose Lobaton WAS C C 0.1 -0.1 1.7 7.3 6.8 1.7 0.8 0.1 0.8 0.0 0.5 1.5 0.251 0.321 0.364 0.685 0
Chris Denorfia SEA OF OF 0.1 0.3 3.3 11.8 11.1 3.0 1.5 0.2 1.3 0.2 0.6 2.1 0.273 0.333 0.401 0.734 1
Brett Jackson ARI OF OF 0.0 0.0 0.3 1.0 1.0 0.2 0.1 0.0 0.1 0.1 0.1 0.5 0.219 0.286 0.362 0.648 0
Daniel Butler BOS C C 0.0 0.0 0.4 1.5 1.5 0.4 0.2 0.1 0.2 0.0 0.1 0.5 0.258 0.312 0.404 0.715 0
Scott Hairston WAS OF OF 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.1 0.0 0.3 0.244 0.287 0.402 0.689 0
Tyler Moore WAS 1B, OF 1B, OF 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.1 0.3 0.264 0.315 0.431 0.746 0
Logan Schafer MIL OF OF 0.0 0.0 0.4 1.7 1.6 0.4 0.2 0.0 0.1 0.0 0.1 0.4 0.241 0.306 0.357 0.663 0
Christian Bethancourt ATL C C 0.0 -1.3 2.4 9.4 9.1 2.2 0.7 0.1 0.9 0.2 0.3 2.3 0.245 0.272 0.335 0.607 0
Junior Lake CHN OF OF 0.0 0.0 0.3 1.1 1.0 0.3 0.1 0.1 0.1 0.0 0.0 0.5 0.244 0.277 0.367 0.644 17
Bryce Brentz BOS OF OF 0.0 0.0 0.3 1.2 1.1 0.3 0.1 0.1 0.1 0.0 0.1 0.5 0.272 0.309 0.462 0.771 1
Dan Johnson TOR 1B 1B 0.0 0.0 0.4 1.4 1.3 0.3 0.1 0.1 0.1 0.0 0.1 0.2 0.242 0.340 0.420 0.760 0
Cristhian Adames COL SS SS 0.0 0.0 0.3 1.2 1.1 0.3 0.1 0.0 0.1 0.0 0.0 0.3 0.231 0.277 0.306 0.583 0
Jordany Valdespin MIA 2B, OF 2B, OF 0.0 0.0 0.7 2.2 2.1 0.6 0.2 0.0 0.2 0.1 0.1 0.5 0.264 0.309 0.396 0.706 0
Jeff Kobernus WAS OF OF 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.1 0.0 0.5 0.286 0.321 0.350 0.671 0
Steven Souza WAS OF OF 0.0 0.0 0.4 1.2 1.2 0.3 0.2 0.0 0.1 0.1 0.1 0.3 0.270 0.336 0.429 0.765 0
Nick Ahmed ARI SS SS 0.0 0.0 0.3 1.2 1.1 0.3 0.1 0.0 0.1 0.0 0.0 0.0 0.241 0.276 0.322 0.598 0
Austin Romine NYA C C 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.1 0.1 0.252 0.302 0.372 0.674 0
Luis Jimenez LAA 3B 3B 0.0 0.0 0.3 1.0 1.0 0.2 0.1 0.0 0.1 0.0 0.0 0.0 0.234 0.259 0.339 0.598 0
Raul Ibanez KC OF 1B, OF 0.0 0.1 1.2 4.1 3.7 0.9 0.5 0.2 0.5 0.0 0.3 0.9 0.229 0.308 0.424 0.732 2
Hernan Perez DET 2B 2B 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.0 0.1 0.277 0.299 0.373 0.673 0
Chris Dominguez SF 3B 3B 0.0 0.0 0.4 1.4 1.3 0.3 0.1 0.0 0.1 0.0 0.0 0.6 0.219 0.247 0.321 0.568 0
Sandy Leon WAS C C 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.1 0.3 0.233 0.301 0.328 0.629 0
Marc Krauss HOU 1B, OF 1B, OF 0.0 0.0 0.9 3.3 3.1 0.8 0.3 0.1 0.4 0.0 0.2 0.9 0.246 0.323 0.403 0.726 0
A.J. Pierzynski STL C C 0.0 -0.1 0.8 3.3 3.2 0.8 0.3 0.1 0.4 0.0 0.1 0.4 0.247 0.282 0.368 0.649 14
Max Stassi HOU C C 0.0 0.0 0.5 2.0 2.0 0.5 0.2 0.1 0.3 0.0 0.1 0.8 0.230 0.270 0.355 0.625 0
Carlos Rivero BOS SS SS 0.0 0.0 0.4 1.5 1.5 0.4 0.2 0.0 0.2 0.0 0.0 0.3 0.274 0.303 0.399 0.702 0
Tyler Collins DET OF OF 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.1 0.2 0.243 0.301 0.380 0.682 0
Xavier Scruggs STL 1B 1B 0.0 0.0 0.6 2.3 2.1 0.5 0.3 0.1 0.3 0.0 0.2 0.7 0.232 0.303 0.388 0.691 0
Garin Cecchini BOS 3B 3B 0.0 0.0 0.5 1.7 1.6 0.5 0.2 0.0 0.2 0.0 0.1 0.5 0.280 0.341 0.407 0.748 0
Tony Campana LAA OF OF 0.0 0.0 0.3 1.3 1.3 0.3 0.1 0.0 0.1 0.0 0.1 0.2 0.224 0.264 0.265 0.529 0
Michael Taylor WAS OF OF 0.0 0.0 0.4 1.4 1.3 0.3 0.2 0.0 0.2 0.1 0.1 0.4 0.243 0.296 0.360 0.656 1
Steve Clevenger BAL C C 0.0 0.0 0.4 1.2 1.2 0.3 0.1 0.0 0.1 0.0 0.1 0.3 0.264 0.308 0.373 0.681 0
Jordan Pacheco ARI C,1B C, 1B 0.0 0.0 0.3 1.1 1.1 0.3 0.1 0.0 0.1 0.0 0.0 0.0 0.246 0.284 0.332 0.616 0
Nolan Reimold ARI OF OF 0.0 0.0 0.3 1.1 1.0 0.2 0.1 0.0 0.1 0.0 0.1 0.3 0.220 0.283 0.368 0.651 0
Guilder Rodriguez TEX SS SS 0.0 0.0 0.4 1.6 1.6 0.4 0.2 0.0 0.1 0.0 0.1 0.1 0.259 0.316 0.315 0.631 0
Carlos Peguero KC OF OF 0.0 0.0 0.5 1.8 1.6 0.4 0.2 0.1 0.2 0.0 0.1 0.5 0.232 0.299 0.434 0.733 0
Ryan Raburn CLE OF OF -0.1 -0.1 0.9 3.1 2.8 0.7 0.3 0.2 0.3 0.0 0.2 0.4 0.237 0.303 0.406 0.709 0
Jarrod Saltalamacchia MIA C C -0.1 -3.0 5.0 20.0 18.6 4.2 1.9 0.6 2.2 0.1 1.4 7.0 0.223 0.297 0.367 0.664 6
Chris Heisey CIN OF OF -0.1 -0.1 1.7 6.4 6.1 1.5 0.7 0.2 0.7 0.2 0.3 1.8 0.243 0.286 0.400 0.686 2
Donald Lutz CIN OF 1B, OF -0.1 -0.1 0.7 2.6 2.4 0.6 0.3 0.1 0.3 0.0 0.1 0.8 0.233 0.274 0.385 0.659 0
JR Murphy NYA C C -0.1 -0.1 0.7 2.7 2.6 0.6 0.3 0.1 0.3 0.0 0.1 0.4 0.245 0.299 0.388 0.687 0
Tucker Barnhart CIN C C -0.1 -0.1 0.5 1.5 1.5 0.3 0.1 0.0 0.1 0.0 0.1 0.0 0.227 0.288 0.320 0.609 0
Tim Federowicz LAN C C -0.1 -0.1 0.4 1.4 1.3 0.3 0.1 0.1 0.1 0.0 0.1 0.4 0.225 0.268 0.328 0.597 0
Moises Sierra CHA OF OF -0.1 -0.1 0.6 2.0 1.9 0.5 0.2 0.0 0.2 0.0 0.1 0.4 0.241 0.295 0.391 0.687 0
Doug Bernier MIN SS SS, 3B -0.1 -0.1 0.6 1.9 1.8 0.4 0.1 0.0 0.2 0.0 0.1 0.3 0.242 0.300 0.337 0.637 0
Cameron Rupp PHI C C -0.1 -0.1 0.3 1.2 1.1 0.2 0.1 0.0 0.1 0.0 0.1 0.5 0.207 0.263 0.313 0.576 0
Darwin Barney LAN 2B 2B -0.1 -0.1 0.5 2.0 1.9 0.4 0.1 0.0 0.1 0.0 0.1 0.4 0.229 0.267 0.296 0.563 0
Kevin Frandsen WAS 1B, 2B, 3B, OF 1B, 2B, 3B, OF -0.1 -0.1 0.7 2.6 2.5 0.7 0.3 0.0 0.2 0.0 0.1 0.1 0.291 0.324 0.371 0.696 0
Chris Gimenez CLE C C, 1B -0.1 -0.1 0.4 1.4 1.3 0.3 0.1 0.0 0.1 0.0 0.1 0.4 0.229 0.311 0.325 0.636 0
Tony Gwynn PHI OF OF -0.1 -0.1 0.4 1.5 1.4 0.3 0.1 0.0 0.1 0.0 0.1 0.2 0.220 0.294 0.286 0.580 0
Chris Herrmann MIN C, OF C, OF -0.1 -0.1 0.7 2.4 2.3 0.5 0.2 0.1 0.2 0.0 0.1 0.6 0.232 0.285 0.327 0.612 0
Grant Green LAA 2B, OF 2B, 3B, OF -0.1 -0.1 0.4 1.5 1.4 0.4 0.1 0.0 0.1 0.0 0.1 0.4 0.252 0.291 0.352 0.643 1
Ed Lucas MIA 1B, 2B, SS, 3B 1B, 2B, SS, 3B, OF -0.1 -0.1 0.4 1.5 1.4 0.3 0.1 0.0 0.1 0.0 0.0 0.4 0.247 0.283 0.323 0.606 0
Ryan Kalish CHN OF OF -0.1 -0.1 1.3 4.3 4.1 1.0 0.5 0.1 0.5 0.1 0.2 1.4 0.242 0.295 0.379 0.674 0
Alberto Callaspo OAK 1B, 2B, 3B 1B, 2B, 3B -0.1 -0.7 4.1 15.4 14.3 3.9 1.7 0.3 1.7 0.0 1.0 2.6 0.271 0.337 0.386 0.723 1
Jeff Baker MIA 1B, 2B, OF 1B, 2B, 3B, OF -0.1 -0.2 1.2 4.5 4.3 1.1 0.5 0.1 0.5 0.0 0.2 1.5 0.249 0.294 0.373 0.667 0
Adam Duvall SF 1B 1B -0.1 0.0 0.5 1.6 1.6 0.3 0.2 0.1 0.2 0.0 0.1 0.6 0.211 0.254 0.345 0.599 0
Mark Ellis STL 2B 2B -0.1 -0.1 0.4 1.5 1.4 0.3 0.2 0.0 0.2 0.0 0.1 0.1 0.248 0.303 0.344 0.647 0
Ramiro Pena ATL 2B, SS, 3B 2B, SS, 3B -0.1 -0.1 0.5 1.8 1.7 0.4 0.2 0.0 0.2 0.0 0.1 0.4 0.243 0.297 0.322 0.619 0
Erisbel Arruebarrena LAN SS SS -0.1 -0.1 0.4 1.6 1.5 0.3 0.1 0.0 0.1 0.0 0.1 0.5 0.202 0.237 0.269 0.505 0
Jake Elmore CIN SS 2B, SS, OF -0.1 -0.1 0.7 2.2 2.1 0.5 0.2 0.0 0.2 0.1 0.2 0.0 0.248 0.322 0.324 0.646 0
Marwin Gonzalez HOU 2B, SS, 3B 2B, SS, 3B -0.1 -0.3 2.6 10.3 9.9 2.4 1.0 0.1 1.1 0.2 0.4 1.5 0.246 0.298 0.368 0.665 1
Shawn O’Malley LAA SS OF -0.1 -0.1 0.5 2.0 1.9 0.4 0.2 0.0 0.2 0.1 0.1 0.5 0.228 0.280 0.288 0.568 0
Delmon Young BAL OF OF -0.1 -0.1 2.6 9.9 9.5 2.5 1.1 0.3 1.2 0.0 0.3 2.5 0.262 0.299 0.420 0.720 0
Cesar Hernandez PHI 2B, 3B, OF 2B, 3B, OF -0.1 -0.1 0.4 1.3 1.3 0.3 0.1 0.0 0.1 0.0 0.1 0.2 0.260 0.304 0.319 0.623 0
Josh Thole TOR C C -0.2 -0.3 1.4 4.8 4.4 1.2 0.5 0.1 0.5 0.0 0.3 0.9 0.267 0.332 0.372 0.705 0
Pete Kozma STL SS 2B, SS -0.2 -0.2 0.8 2.9 2.8 0.7 0.4 0.0 0.4 0.0 0.1 0.1 0.236 0.291 0.332 0.623 0
Jesus Aguilar CLE 1B 1B -0.2 -0.2 0.7 2.5 2.3 0.6 0.2 0.0 0.2 0.0 0.2 0.4 0.241 0.316 0.386 0.701 0
Billy Butler KC 1B 1B -0.2 2.6 6.4 24.4 22.5 6.1 2.9 0.8 3.1 0.0 1.7 4.1 0.272 0.345 0.425 0.769 68
Stefen Romero SEA OF OF -0.2 -0.2 1.1 3.8 3.6 1.0 0.5 0.1 0.4 0.0 0.1 0.5 0.271 0.299 0.421 0.720 0
Ryan Doumit ATL C, OF C, OF -0.2 -0.3 1.2 4.7 4.4 1.1 0.4 0.1 0.4 0.0 0.2 0.8 0.242 0.295 0.366 0.662 0
Mike Moustakas KC 3B 3B -0.2 1.0 6.5 24.0 22.5 5.3 2.7 1.0 3.1 0.1 1.4 4.5 0.234 0.298 0.422 0.720 25
Daniel Robertson TEX OF OF -0.2 0.0 2.5 10.1 9.5 2.6 1.2 0.1 0.9 0.2 0.5 1.8 0.280 0.337 0.379 0.716 0
Andres Blanco PHI 3B 2B, SS, 3B -0.2 -0.2 0.6 2.0 1.8 0.4 0.1 0.0 0.1 0.0 0.1 0.3 0.230 0.285 0.316 0.600 0
James Jones SEA OF OF -0.2 -0.1 0.7 2.5 2.4 0.6 0.2 0.0 0.1 0.1 0.1 0.6 0.238 0.289 0.347 0.635 11
Nick Punto OAK 2B, SS, 3B 2B, SS, 3B -0.2 -0.3 0.9 3.1 2.8 0.7 0.3 0.0 0.3 0.0 0.2 0.3 0.248 0.321 0.331 0.652 0
Matt McBride COL OF 1B -0.2 -0.2 0.8 2.6 2.5 0.6 0.2 0.0 0.2 0.0 0.1 0.1 0.254 0.283 0.393 0.676 1
Miguel Rojas LAN SS, 3B SS, 3B -0.2 -0.2 0.5 1.8 1.7 0.4 0.1 0.0 0.1 0.0 0.1 0.1 0.215 0.253 0.265 0.518 0
Hector Gomez MIL SS SS -0.2 -0.2 0.7 2.6 2.5 0.6 0.3 0.0 0.3 0.0 0.1 0.2 0.236 0.274 0.367 0.641 0
Adrian Nieto CHA C C -0.2 -0.3 1.0 3.4 3.2 0.7 0.3 0.1 0.3 0.0 0.2 0.7 0.220 0.280 0.324 0.604 0
Nick Hundley BAL C C -0.3 -0.6 2.5 9.2 8.8 2.1 1.0 0.3 1.1 0.0 0.4 2.8 0.234 0.278 0.377 0.655 0
Eric Fryer MIN C C -0.3 -0.4 0.9 3.2 3.0 0.7 0.3 0.0 0.3 0.0 0.2 0.9 0.221 0.283 0.324 0.607 0
Jason Giambi CLE DH DH -0.3 -0.2 0.8 2.8 2.5 0.5 0.3 0.1 0.2 0.0 0.2 0.8 0.218 0.315 0.371 0.686 0
Jonny Gomes OAK OF OF -0.3 -0.3 3.8 11.4 10.2 2.6 1.5 0.6 1.5 0.0 0.9 2.6 0.256 0.354 0.429 0.783 2
Elian Herrera MIL SS, 3B, OF 2B, SS, 3B, OF -0.3 -0.3 0.9 3.3 3.1 0.8 0.3 0.0 0.3 0.1 0.2 0.9 0.243 0.303 0.324 0.627 0
Jason Bourgeois CIN OF OF -0.3 -0.2 0.9 3.4 3.3 0.8 0.3 0.0 0.3 0.1 0.1 0.0 0.257 0.302 0.341 0.643 0
Tyler Flowers CHA C C -0.3 -3.1 5.3 19.7 18.5 4.0 2.1 0.7 2.3 0.1 1.1 6.8 0.215 0.277 0.377 0.654 5
Jack Hannahan CIN 1B, 3B 1B, 3B -0.3 -0.3 0.9 3.4 3.1 0.7 0.3 0.1 0.3 0.0 0.2 1.1 0.228 0.296 0.342 0.638 0
Zelous Wheeler NYA 3B 3B, OF -0.3 -0.2 1.5 4.1 3.9 1.0 0.5 0.2 0.6 0.0 0.2 1.5 0.262 0.315 0.410 0.725 0
Matt Duffy SF 2B 2B, SS -0.3 -0.3 0.8 3.0 2.9 0.7 0.2 0.0 0.2 0.0 0.1 0.2 0.227 0.272 0.294 0.566 0
Clint Barmes PIT 2B, SS 2B, SS -0.3 -0.3 0.8 2.7 2.6 0.6 0.2 0.0 0.2 0.0 0.1 1.1 0.219 0.264 0.323 0.587 0
Marlon Byrd PHI OF OF -0.3 0.0 4.9 20.1 19.1 4.8 2.0 0.6 2.3 0.1 0.9 5.2 0.252 0.301 0.397 0.698 85
Luis Sardinas TEX 2B, SS 2B, SS, 3B -0.3 -0.2 2.5 9.6 9.4 2.7 0.9 0.0 1.0 0.3 0.2 1.2 0.284 0.308 0.359 0.667 0
Matt Szczur CHN OF OF -0.3 -0.3 1.0 3.5 3.3 0.8 0.3 0.0 0.3 0.1 0.1 0.9 0.249 0.293 0.328 0.622 0
Mike Olt CHN 1B, 3B 1B, 3B -0.3 -0.2 1.5 5.7 5.3 1.1 0.6 0.2 0.6 0.1 0.4 2.0 0.208 0.276 0.363 0.639 11
Leury Garcia CHA 2B, 3B, OF 2B, SS, 3B, OF -0.3 -0.3 1.0 3.8 3.6 0.8 0.4 0.0 0.3 0.1 0.1 0.4 0.231 0.260 0.301 0.561 0
Justin Smoak SEA 1B 1B -0.4 -0.4 1.1 3.8 3.5 0.9 0.4 0.0 0.3 0.0 0.3 0.3 0.253 0.318 0.392 0.710 18
Tuffy Gosewisch ARI C C -0.4 -0.4 0.8 3.3 3.2 0.7 0.3 0.0 0.3 0.0 0.1 1.5 0.223 0.254 0.319 0.573 0
Rickie Weeks MIL 2B 2B -0.4 -0.5 2.0 8.0 7.2 1.9 1.1 0.1 0.7 0.0 0.8 1.0 0.260 0.352 0.438 0.790 4
Logan Forsythe TB 2B 2B, SS, 3B, OF -0.4 -0.5 2.0 8.0 7.4 1.8 0.8 0.1 0.8 0.1 0.5 2.0 0.246 0.314 0.351 0.665 4
Howie Kendrick LAA 2B 2B -0.4 -1.6 5.7 23.0 21.9 5.8 2.4 0.3 2.4 0.4 1.0 4.4 0.264 0.307 0.369 0.676 99
Jayson Nix KC 2B, SS, 3B 2B, SS, 3B -0.4 -0.4 1.1 3.6 3.4 0.7 0.4 0.0 0.3 0.0 0.2 0.5 0.217 0.283 0.346 0.630 0
Scott Van Slyke LAN 1B, OF 1B, OF -0.4 -0.4 1.2 3.4 3.1 0.7 0.3 0.1 0.4 0.0 0.2 1.3 0.226 0.306 0.385 0.690 1
Tony Cruz STL C C -0.4 -0.5 1.0 3.6 3.4 0.8 0.3 0.0 0.3 0.0 0.2 1.0 0.232 0.279 0.338 0.617 0
James McCann DET C C -0.4 -0.5 1.2 4.6 4.5 1.2 0.4 0.0 0.4 0.0 0.1 0.6 0.257 0.286 0.369 0.656 0
J.B. Shuck CLE OF OF -0.4 -0.3 1.0 3.5 3.2 0.9 0.3 0.0 0.2 0.1 0.2 0.0 0.265 0.331 0.352 0.683 0
Matt den Dekker NYN OF OF -0.5 -0.2 3.1 12.2 11.5 2.9 1.2 0.2 1.3 0.3 0.6 2.5 0.252 0.307 0.376 0.683 0
Eric Young NYN OF OF -0.5 -0.1 2.5 10.1 9.4 2.2 1.1 0.1 0.7 0.6 0.5 2.7 0.236 0.297 0.305 0.602 33
Jordan Danks CHA OF OF -0.5 -0.4 2.2 7.8 7.2 1.8 0.9 0.2 0.8 0.1 0.5 1.6 0.244 0.318 0.374 0.692 0
Curt Casali TB C C -0.5 -0.6 1.3 5.0 4.6 1.0 0.5 0.0 0.5 0.0 0.3 1.3 0.220 0.293 0.318 0.611 0
Stephen Drew NYA 2B, SS 2B, SS -0.5 -2.6 5.5 19.4 17.7 4.2 2.3 0.5 2.3 0.2 1.6 4.2 0.237 0.317 0.387 0.704 4
Brandon Barnes COL OF OF -0.5 -0.4 1.3 5.3 5.1 1.1 0.4 0.1 0.5 0.1 0.2 1.6 0.218 0.262 0.328 0.590 2
Sean Rodriguez TB 1B, 2B, OF 1B, 2B, SS, 3B, OF -0.5 -0.6 1.5 4.6 4.3 1.0 0.5 0.1 0.4 0.0 0.3 1.5 0.231 0.297 0.375 0.671 1
Ben Paulsen COL 1B 1B -0.5 -0.4 1.0 3.5 3.3 0.8 0.3 0.1 0.3 0.0 0.2 1.1 0.233 0.277 0.381 0.658 1
Reed Johnson MIA OF OF -0.5 -0.4 1.5 5.4 5.3 1.4 0.5 0.1 0.5 0.1 0.2 1.8 0.258 0.282 0.346 0.629 0
Brent Morel PIT 3B 3B -0.5 -0.4 1.1 4.4 4.2 1.0 0.4 0.0 0.4 0.0 0.2 0.7 0.240 0.281 0.332 0.613 0
Jake Smolinski TEX OF OF -0.6 -0.6 3.5 15.1 14.1 3.7 1.6 0.3 1.6 0.0 1.0 2.5 0.260 0.329 0.401 0.730 20
Jesus Guzman HOU 1B, OF 1B, OF -0.6 -0.5 1.3 4.9 4.5 1.1 0.5 0.0 0.5 0.0 0.3 0.3 0.251 0.317 0.394 0.710 0
Brayan Pena CIN C,1B C, 1B -0.6 -2.1 3.2 11.7 11.3 2.8 1.1 0.2 1.1 0.1 0.4 1.3 0.251 0.293 0.369 0.661 1
Mike Aviles CLE 2B, SS, 3B, OF 2B, SS, 3B, OF -0.6 -0.9 2.5 8.7 8.3 2.0 0.9 0.2 0.9 0.2 0.3 1.7 0.244 0.284 0.358 0.642 8
Drew Butera LAN C C -0.7 -0.7 1.0 3.4 3.3 0.6 0.2 0.0 0.2 0.0 0.1 1.2 0.188 0.237 0.269 0.507 0
A.J. Pollock ARI OF OF -0.7 0.1 4.5 18.2 17.4 4.7 1.8 0.2 1.8 0.6 0.7 3.6 0.267 0.303 0.385 0.688 81
Jackson Williams COL C C -0.7 -0.7 1.0 3.8 3.6 0.7 0.2 0.0 0.2 0.0 0.2 1.6 0.198 0.257 0.276 0.533 0
Carlos Corporan HOU C C -0.7 -0.8 1.5 6.3 5.9 1.3 0.6 0.1 0.6 0.0 0.3 0.8 0.227 0.275 0.348 0.623 0
Brennan Boesch LAA OF OF -0.7 -0.6 2.1 7.8 7.5 1.7 0.8 0.2 0.9 0.1 0.3 1.5 0.231 0.272 0.365 0.637 2
Chris Stewart PIT C C -0.7 -0.7 1.3 4.3 4.0 0.9 0.3 0.0 0.3 0.0 0.3 0.1 0.234 0.292 0.311 0.603 0
Lyle Overbay MIL 1B 1B -0.8 -0.5 2.1 7.6 7.0 1.7 0.8 0.2 0.9 0.0 0.6 2.1 0.239 0.323 0.388 0.711 0
Michael McKenry COL C C -0.8 -2.3 3.2 12.5 11.8 2.7 1.0 0.3 1.2 0.1 0.8 3.2 0.228 0.291 0.349 0.640 2
Garrett Jones MIA 1B, OF 1B, OF -0.8 -0.6 5.6 21.3 20.2 5.0 2.3 0.7 2.6 0.1 1.1 5.0 0.247 0.304 0.418 0.722 32
Chris Parmelee MIN 1B, OF 1B, OF -0.8 -0.8 1.8 7.3 6.9 1.7 0.7 0.1 0.7 0.0 0.3 0.6 0.240 0.296 0.356 0.653 1
Francisco Cervelli NYA C C -0.8 -1.0 1.9 7.0 6.5 1.6 0.7 0.1 0.6 0.0 0.5 2.4 0.241 0.321 0.361 0.683 1
Skip Schumaker CIN 2B, OF 2B, OF -0.8 -0.8 1.5 5.8 5.5 1.4 0.5 0.0 0.4 0.1 0.3 1.1 0.257 0.315 0.350 0.665 0
Charlie Culberson COL 2B, SS, 3B, OF 2B, SS, 3B, OF -0.8 -0.8 1.3 4.5 4.3 1.0 0.3 0.1 0.3 0.1 0.2 0.8 0.224 0.256 0.315 0.572 1
Alcides Escobar KC SS SS -0.9 -0.6 6.7 26.8 25.6 6.4 2.7 0.3 2.3 1.1 0.9 4.3 0.249 0.291 0.342 0.633 100
Justin Turner LAN 2B, SS, 3B 1B, 2B, SS, 3B -0.9 -1.2 2.7 10.9 10.3 2.6 1.0 0.1 1.1 0.1 0.5 1.9 0.255 0.309 0.348 0.657 35
Martin Maldonado MIL C C, 1B -0.9 -1.0 1.5 5.4 5.1 1.2 0.4 0.1 0.5 0.0 0.3 0.8 0.232 0.303 0.380 0.683 0
Jose Molina TB C C -0.9 -1.4 1.8 5.9 5.6 1.2 0.5 0.1 0.5 0.1 0.2 1.7 0.218 0.268 0.290 0.558 0
Hank Conger LAA C C -0.9 -1.7 2.2 7.4 7.1 1.6 0.7 0.1 0.7 0.1 0.3 2.1 0.223 0.272 0.315 0.587 0
Kristopher Negron CIN 2B, 3B 2B, 3B -0.9 -1.4 3.2 12.3 11.6 2.6 1.2 0.3 1.1 0.4 0.6 3.3 0.220 0.272 0.330 0.602 0
Alfredo Marte ARI OF OF -0.9 -0.9 2.3 8.8 8.3 1.9 0.9 0.2 1.0 0.0 0.3 2.7 0.234 0.280 0.353 0.633 0
Darin Ruf PHI 1B, OF 1B, OF -0.9 -0.9 2.0 7.0 6.4 1.6 0.7 0.2 0.7 0.0 0.5 2.3 0.249 0.324 0.392 0.716 0
Steve Tolleson TOR 2B, 3B 2B, 3B, OF -0.9 -0.9 2.1 7.2 6.7 1.7 0.8 0.0 0.8 0.1 0.5 0.7 0.259 0.327 0.376 0.703 0
Jake Goebbert SD 1B 1B, OF -0.9 -0.9 1.7 5.0 4.7 1.1 0.4 0.1 0.4 0.1 0.3 1.0 0.237 0.299 0.335 0.634 0
Maikel Franco PHI 3B 1B, 3B -1.0 -0.8 2.3 9.3 8.9 2.2 0.9 0.2 1.0 0.0 0.3 2.0 0.250 0.286 0.384 0.669 5
Caleb Joseph BAL C C -1.0 -3.8 4.5 15.5 14.8 3.5 1.6 0.4 1.7 0.2 0.6 3.4 0.233 0.272 0.356 0.628 0
Gaby Sanchez PIT 1B 1B -1.0 -0.6 2.5 8.1 7.5 1.8 0.9 0.3 1.1 0.0 0.5 0.4 0.243 0.315 0.389 0.704 0
Ramon Santiago CIN 2B, SS, 3B 2B, SS, 3B -1.0 -1.2 2.0 6.9 6.4 1.5 0.6 0.1 0.7 0.1 0.5 1.0 0.230 0.307 0.314 0.621 0
Peter Bourjos STL OF OF -1.1 -0.5 4.8 18.1 17.2 4.2 2.1 0.4 2.0 0.4 1.0 4.5 0.247 0.295 0.378 0.673 5
Michael Cuddyer COL 1B, OF 1B, OF -1.1 -0.5 5.7 24.3 23.1 5.8 2.3 0.6 2.4 0.3 1.2 4.5 0.251 0.305 0.390 0.695 98
Cliff Pennington ARI 2B, SS 2B, SS, 3B -1.1 -1.3 2.3 9.0 8.4 2.1 0.8 0.1 0.8 0.1 0.5 1.6 0.244 0.305 0.336 0.641 1
Brad Miller SEA SS 2B, SS -1.1 -2.8 6.0 20.3 19.0 4.6 2.3 0.5 2.2 0.4 1.2 3.7 0.241 0.304 0.383 0.687 31
Cory Spangenberg SD 2B 3B -1.1 -1.2 2.1 8.0 7.7 1.9 0.6 0.0 0.5 0.2 0.2 1.4 0.249 0.277 0.312 0.589 1
Anthony Gose TOR OF OF -1.2 -0.8 3.3 10.1 9.4 2.3 1.2 0.1 1.0 0.6 0.6 3.2 0.240 0.311 0.366 0.677 5
Jon Jay STL OF OF -1.2 -0.8 4.7 19.6 18.3 5.0 2.2 0.2 2.0 0.3 1.2 3.0 0.272 0.337 0.377 0.714 33
Oscar Taveras STL OF OF -1.2 -1.1 3.3 11.4 10.9 2.9 1.2 0.2 1.4 0.1 0.5 1.3 0.268 0.311 0.396 0.706 61
Ender Inciarte ARI OF OF -1.3 0.0 5.4 22.9 22.1 5.8 2.2 0.3 1.8 0.9 0.7 3.1 0.261 0.294 0.342 0.636 65
Tyler Holt CLE OF OF -1.3 -1.2 1.9 6.9 6.4 1.5 0.6 0.0 0.5 0.2 0.5 1.8 0.241 0.313 0.316 0.629 0
Drew Stubbs COL OF OF -1.3 -1.0 3.1 13.0 12.3 2.7 1.1 0.3 1.0 0.4 0.7 3.7 0.224 0.284 0.332 0.616 71
Wil Nieves PHI C C -1.3 -2.0 2.1 7.1 6.9 1.6 0.5 0.1 0.6 0.1 0.2 1.8 0.233 0.268 0.307 0.575 0
Andrew Romine DET 2B, SS, 3B 2B, SS, 3B -1.3 -1.3 2.5 8.0 7.5 1.9 0.8 0.1 0.7 0.3 0.5 1.9 0.249 0.306 0.316 0.622 2
Pablo Sandoval SF 3B 3B -1.3 0.1 6.6 26.7 25.5 6.8 2.7 0.7 3.1 0.0 1.1 4.9 0.267 0.316 0.406 0.722 96
Bryan Holaday DET C C -1.4 -2.5 3.0 10.7 10.3 2.6 1.0 0.1 1.0 0.1 0.4 2.5 0.251 0.293 0.356 0.649 1
Endy Chavez SEA OF OF -1.4 -1.3 2.7 9.2 8.9 2.4 1.0 0.1 0.8 0.2 0.3 1.0 0.266 0.305 0.364 0.669 0
Gregorio Petit HOU SS SS, 3B -1.4 -2.0 3.2 10.9 10.5 2.6 1.2 0.2 1.0 0.1 0.3 2.1 0.251 0.291 0.364 0.655 2
Jose Tabata PIT OF OF -1.4 -1.4 2.1 7.0 6.7 1.7 0.7 0.0 0.6 0.0 0.3 0.4 0.262 0.315 0.373 0.688 0
Joaquin Arias SF 1B, 2B, SS, 3B 1B, 2B, SS, 3B -1.4 -1.4 1.8 6.5 6.3 1.5 0.5 0.0 0.5 0.0 0.2 0.4 0.240 0.250 0.289 0.540 0
Angel Pagan SF OF OF -1.5 -0.6 5.2 22.0 21.0 5.5 2.3 0.2 1.6 0.7 0.8 3.1 0.263 0.305 0.354 0.659 76
Travis Ishikawa SF 1B 1B, OF -1.5 -1.2 2.5 9.8 9.2 2.2 0.9 0.2 0.9 0.0 0.6 3.2 0.239 0.304 0.357 0.661 0
Brendan Ryan NYA 2B, SS 1B, 2B, SS -1.5 -1.4 2.2 7.3 6.8 1.6 0.7 0.0 0.6 0.3 0.4 2.0 0.228 0.289 0.304 0.593 0
Trevor Plouffe MIN 3B 3B -1.6 -0.3 6.5 25.6 24.2 6.0 2.9 0.8 3.1 0.1 1.5 4.7 0.247 0.302 0.411 0.712 86
Andy Parrino OAK SS 2B, SS -1.7 -1.9 2.3 7.3 6.8 1.7 0.7 0.1 0.7 0.0 0.4 0.4 0.244 0.308 0.353 0.662 0
Daniel Descalso STL 2B, SS, 3B 2B, SS, 3B -1.7 -1.9 2.3 8.4 7.9 1.9 0.8 0.1 0.8 0.1 0.5 0.9 0.235 0.296 0.330 0.626 0
Efren Navarro LAA 1B, OF 1B, OF -1.7 -1.7 2.1 6.5 6.2 1.5 0.6 0.1 0.6 0.1 0.3 1.2 0.244 0.291 0.313 0.604 0
Dilson Herrera NYN 2B 2B -1.7 -2.9 5.1 19.8 19.0 4.6 1.7 0.4 1.9 0.5 0.9 4.1 0.242 0.287 0.352 0.640 0
Eric Campbell NYN 1B, 3B, OF 1B, 3B, OF -1.7 -1.6 2.2 8.3 7.7 1.8 0.8 0.1 0.6 0.0 0.6 0.6 0.240 0.322 0.342 0.664 0
Grady Sizemore PHI OF OF -1.7 -1.6 2.5 9.9 9.3 2.2 1.0 0.1 0.9 0.1 0.6 1.4 0.236 0.296 0.358 0.654 6
Andrew Susac SF C C -1.8 -2.0 2.4 9.2 8.6 1.8 0.7 0.2 0.8 0.0 0.6 3.1 0.213 0.278 0.319 0.597 0
Gerald Laird ATL C C -1.8 -2.0 2.2 7.8 7.2 1.7 0.8 0.0 0.6 0.0 0.6 0.6 0.229 0.300 0.308 0.609 0
Justin Bour MIA 1B 1B -1.8 -1.3 3.1 11.9 11.4 2.9 1.2 0.2 1.2 0.0 0.6 2.3 0.258 0.304 0.395 0.699 0
Rafael Ynoa COL 2B 3B -1.9 -2.2 3.0 13.4 12.9 3.0 1.0 0.2 1.0 0.2 0.5 2.1 0.233 0.280 0.322 0.602 4
Domonic Brown PHI OF OF -1.9 -1.3 5.5 21.3 20.1 5.1 2.1 0.5 2.3 0.3 1.2 4.0 0.254 0.309 0.394 0.703 48
Jeff Mathis MIA C C -2.0 -2.2 2.1 7.0 6.7 1.3 0.4 0.1 0.6 0.0 0.3 2.7 0.197 0.242 0.286 0.528 0
Wilmer Flores NYN 2B, SS, 3B 2B, SS, 3B -2.0 -4.4 5.2 20.7 19.9 5.0 1.8 0.5 2.1 0.0 0.7 3.3 0.252 0.287 0.381 0.668 73
Jedd Gyorko SD 2B 2B, 3B -2.0 -3.7 6.0 24.8 23.4 5.4 2.3 0.7 2.6 0.1 1.4 6.0 0.229 0.293 0.378 0.670 77
Nate Freiman OAK 1B 1B -2.1 -1.3 3.8 12.6 11.7 3.1 1.3 0.3 1.6 0.0 0.8 2.9 0.264 0.329 0.445 0.774 0
Jesus Sucre SEA C C -2.1 -2.3 2.4 7.6 7.4 1.8 0.7 0.0 0.7 0.0 0.2 0.5 0.249 0.278 0.337 0.615 0
Jordy Mercer PIT 2B, SS 2B, SS -2.1 -4.5 6.2 23.6 22.4 5.7 2.2 0.5 2.7 0.1 1.1 4.2 0.255 0.297 0.384 0.681 69
David DeJesus TB OF OF -2.1 -1.7 4.5 16.7 15.4 4.0 2.0 0.3 1.7 0.2 1.2 2.9 0.259 0.335 0.386 0.721 0
Kurt Suzuki MIN C C -2.2 -3.3 4.6 18.3 17.6 4.4 1.8 0.4 1.8 0.0 0.8 2.7 0.251 0.300 0.365 0.666 15
Ruben Tejada NYN SS SS -2.2 -2.4 2.4 8.8 8.2 2.1 0.7 0.1 0.7 0.1 0.5 1.8 0.252 0.317 0.312 0.630 0
Tommy La Stella ATL 2B 2B -2.3 -2.5 3.5 13.1 12.2 3.2 1.3 0.1 1.2 0.1 0.9 1.1 0.264 0.343 0.349 0.692 19
Collin Cowgill LAA OF OF -2.3 -2.2 2.7 9.7 9.2 2.1 0.9 0.2 0.9 0.1 0.6 3.1 0.232 0.285 0.322 0.608 1
Ryan Hanigan TB C C -2.3 -2.8 3.0 11.0 10.0 2.4 1.1 0.2 0.9 0.0 0.8 1.4 0.241 0.324 0.324 0.649 0
Christian Walker BAL 1B 1B -2.4 -1.7 3.5 12.1 11.6 2.9 1.2 0.5 1.2 0.0 0.5 3.2 0.250 0.292 0.396 0.688 0
Andy Wilkins CHA 1B 1B -2.4 -1.2 4.9 18.0 17.1 4.1 1.9 0.7 2.2 0.1 0.8 4.0 0.242 0.287 0.395 0.682 0
Matt Dominguez HOU 3B 3B -2.4 -1.1 5.5 21.4 20.3 5.0 2.3 0.7 2.6 0.0 0.8 4.4 0.244 0.284 0.395 0.679 14
Ryan Ludwick CIN OF OF -2.4 -2.3 4.5 17.8 16.7 4.0 1.7 0.5 2.1 0.0 1.1 4.8 0.238 0.302 0.396 0.698 1
Jon Singleton HOU 1B 1B -2.5 -1.4 4.7 17.9 16.2 3.6 2.0 0.7 2.1 0.1 1.7 6.3 0.224 0.316 0.375 0.691 38
Ryan Goins TOR 2B, SS 2B, SS -2.5 -2.8 3.0 9.0 8.7 2.2 0.9 0.1 0.9 0.1 0.4 2.2 0.249 0.289 0.347 0.636 0
Cody Ross ARI OF OF -2.6 -2.3 4.5 16.4 15.5 4.1 1.5 0.4 1.7 0.2 0.7 4.4 0.261 0.300 0.388 0.687 4
Munenori Kawasaki TOR 2B, SS, 3B 2B, SS, 3B -2.7 -2.8 3.0 10.2 9.6 2.4 1.0 0.0 0.9 0.1 0.7 2.1 0.249 0.327 0.346 0.674 0
Jackie Bradley Jr. BOS OF OF -2.8 -2.2 4.5 17.0 16.0 4.0 1.8 0.2 1.7 0.4 1.0 4.0 0.249 0.313 0.380 0.693 2
Chris Taylor SEA SS SS -2.8 -2.8 4.3 14.8 13.8 3.8 1.7 0.1 1.3 0.4 0.9 3.3 0.273 0.331 0.375 0.706 1
Welington Castillo CHN C C -2.8 -4.0 4.5 15.9 14.9 3.6 1.6 0.4 1.8 0.0 0.9 4.4 0.241 0.301 0.382 0.684 1
Aaron Hicks MIN OF OF -2.9 -2.6 3.2 11.4 10.6 2.3 1.1 0.1 1.0 0.4 0.8 3.3 0.221 0.298 0.330 0.628 2
Jordan Schafer MIN OF OF -3.0 -1.3 5.5 20.6 19.5 4.3 2.1 0.1 1.8 1.4 1.0 6.6 0.222 0.283 0.295 0.577 55
David Murphy CLE OF OF -3.0 -2.8 4.5 15.5 14.4 3.8 1.6 0.4 1.7 0.1 0.9 2.9 0.266 0.329 0.399 0.728 20
Andrelton Simmons ATL SS SS -3.0 -4.6 6.5 25.4 24.1 6.0 2.5 0.4 2.4 0.4 1.1 3.4 0.250 0.304 0.364 0.668 54
Freddy Galvis PHI 2B, SS, 3B 2B, SS, 3B, OF -3.0 -3.8 3.4 11.8 11.4 2.6 1.0 0.3 1.1 0.1 0.4 3.3 0.225 0.272 0.336 0.608 2
Chris Valaika CHN 1B, 2B 1B, 2B, SS, 3B -3.2 -3.6 3.5 13.3 12.8 3.0 1.1 0.2 1.2 0.2 0.5 3.2 0.236 0.281 0.340 0.621 1
Roberto Perez CLE C C -3.3 -3.6 2.6 9.5 8.6 1.8 0.8 0.1 0.7 0.0 0.7 3.4 0.206 0.287 0.287 0.574 0
Tommy Medica SD 1B, OF 1B, OF -3.3 -3.3 3.3 10.8 10.1 2.2 1.0 0.3 1.0 0.0 0.6 3.3 0.217 0.293 0.362 0.655 6
Conor Gillaspie CHA 3B 1B, 3B -3.3 -2.4 6.2 24.5 23.1 5.9 2.5 0.6 2.8 0.1 1.3 4.5 0.254 0.309 0.376 0.685 27
Abraham Almonte SD OF OF -3.3 -3.0 3.9 15.4 14.6 3.5 1.4 0.3 1.1 0.4 0.7 3.6 0.237 0.289 0.310 0.599 2
Juan Uribe LAN 3B 3B -3.4 -3.1 4.5 17.0 16.4 4.0 1.5 0.4 1.8 0.1 0.6 3.8 0.245 0.288 0.372 0.660 58
Brandon Belt SF 1B 1B -3.4 -4.1 6.0 20.1 18.9 4.7 2.0 0.6 2.1 0.3 1.2 6.1 0.248 0.317 0.405 0.722 62
Yunel Escobar TB SS SS -3.5 -4.9 5.2 20.2 18.8 4.9 2.0 0.3 2.0 0.1 1.2 2.8 0.260 0.322 0.349 0.670 15
Jonathan Schoop BAL 2B, 3B 2B, 3B -3.5 -5.0 6.0 21.3 20.6 4.7 2.2 0.6 2.5 0.2 0.8 5.2 0.229 0.266 0.356 0.622 17
Josh Hamilton LAA OF OF -3.6 -3.4 5.1 19.6 18.4 4.3 2.1 0.5 2.3 0.1 1.1 5.9 0.235 0.291 0.378 0.669 64
Juan Lagares NYN OF OF -3.6 -2.9 5.1 21.3 20.5 5.4 1.9 0.2 1.8 0.5 0.7 4.7 0.265 0.294 0.356 0.651 23
Casey McGehee MIA 3B 3B -3.7 -3.0 6.6 28.3 26.7 7.2 2.6 0.5 2.9 0.1 1.5 5.2 0.268 0.319 0.364 0.683 85
Enrique Hernandez MIA OF SS, OF -3.8 -3.8 3.5 11.9 11.6 2.9 1.2 0.0 1.3 0.0 0.4 0.3 0.251 0.288 0.351 0.639 0
Yorman Rodriguez CIN OF OF -3.8 -3.6 3.0 11.3 10.9 2.4 0.9 0.0 0.9 0.3 0.5 4.6 0.221 0.267 0.328 0.595 0
Erick Aybar LAA SS SS -3.9 -4.6 5.7 21.9 21.1 5.4 2.2 0.2 2.1 0.5 0.8 3.1 0.257 0.293 0.342 0.635 96
John Baker CHN C C -3.9 -4.3 3.0 11.2 10.4 2.2 0.8 0.2 0.9 0.0 0.7 3.6 0.213 0.283 0.295 0.579 0
Juan Perez SF OF OF -4.0 -3.8 3.5 12.2 11.8 2.8 1.2 0.0 1.2 0.3 0.4 3.1 0.234 0.261 0.319 0.580 0
Ryan Howard PHI 1B 1B -4.2 -1.3 5.7 23.9 22.0 4.9 2.4 0.8 2.8 0.0 1.8 8.2 0.224 0.307 0.390 0.697 81
Eduardo Escobar MIN SS, 3B 2B, SS, 3B -4.3 -4.7 4.0 14.4 14.0 3.6 1.3 0.1 1.4 0.1 0.4 3.4 0.255 0.284 0.345 0.630 6
Cody Asche PHI 3B 3B -4.3 -3.9 4.8 18.3 17.3 4.4 1.7 0.4 1.8 0.1 0.9 3.9 0.256 0.305 0.378 0.684 10
Josh Rutledge COL 2B, SS 2B, SS, 3B -4.5 -5.2 4.7 18.6 18.0 4.2 1.6 0.3 1.4 0.4 0.7 4.4 0.236 0.284 0.340 0.623 55
Yasmani Grandal SD C,1B C, 1B -4.5 -7.5 5.7 24.1 21.8 4.9 2.1 0.5 2.0 0.1 2.1 5.6 0.225 0.314 0.337 0.651 12
Logan Watkins CHN 2B 2B -4.5 -4.9 3.8 11.5 10.8 2.6 1.0 0.1 0.9 0.4 0.6 2.6 0.240 0.298 0.342 0.639 1
Carlos Sanchez CHA 2B 2B -4.6 -5.3 5.6 21.2 20.4 5.2 2.0 0.2 1.7 0.6 0.8 5.6 0.257 0.297 0.331 0.629 0
Nolan Arenado COL 3B 3B -4.6 -4.2 4.3 17.9 17.3 4.2 1.4 0.4 1.6 0.1 0.6 2.1 0.241 0.282 0.359 0.641 63
Chris Johnson ATL 3B 1B, 3B -4.7 -4.0 5.8 23.2 22.2 5.9 2.1 0.4 2.4 0.1 1.0 7.3 0.264 0.309 0.380 0.689 49
C.J. Cron LAA 1B 1B -4.8 -4.2 4.5 15.5 14.9 3.6 1.5 0.4 1.6 0.1 0.5 2.9 0.241 0.276 0.373 0.649 17
Jacob Lamb ARI 3B 3B -4.8 -4.2 4.3 16.0 15.1 3.8 1.4 0.3 1.7 0.0 0.7 3.8 0.255 0.302 0.393 0.695 0
Phil Gosselin ATL 2B 2B, 3B -4.9 -5.3 4.8 20.1 19.3 4.9 2.0 0.1 1.6 0.1 0.8 4.1 0.254 0.296 0.337 0.633 0
Rymer Liriano SD OF OF -5.2 -4.8 4.7 17.6 16.6 3.8 1.6 0.2 1.6 0.4 0.9 4.0 0.228 0.284 0.323 0.607 10
Chris Iannetta LAA C C -5.8 -8.5 4.9 16.9 15.1 3.2 1.7 0.3 1.6 0.1 1.6 4.8 0.212 0.319 0.330 0.649 0
Seth Smith SD OF OF -6.1 -5.9 5.9 24.3 22.4 5.3 2.4 0.5 2.2 0.1 1.7 4.9 0.237 0.317 0.357 0.675 33
Yangervis Solarte SD 2B, 3B SS -6.1 -7.4 6.1 26.7 25.2 6.2 2.4 0.4 2.2 0.0 1.2 4.5 0.246 0.302 0.327 0.628 63
Michael Morse SF 1B, OF 1B, OF -6.4 -6.3 6.3 23.1 22.0 5.2 2.2 0.7 2.6 0.0 1.0 7.4 0.238 0.287 0.387 0.673 57
B.J. Upton ATL OF OF -6.4 -5.1 6.7 23.4 21.5 4.7 2.5 0.6 2.1 0.9 1.7 8.5 0.217 0.296 0.350 0.647 45
Zack Cozart CIN SS SS -6.5 -7.7 4.8 16.0 15.5 3.8 1.5 0.3 1.6 0.1 0.6 2.8 0.242 0.284 0.367 0.650 10
Donovan Solano MIA 2B 2B -6.6 -7.4 6.1 26.6 25.6 6.6 2.5 0.2 2.1 0.2 1.0 5.4 0.257 0.291 0.322 0.613 0
A.J. Ellis LAN C C -6.9 -8.0 4.6 17.6 16.0 3.6 1.4 0.3 1.5 0.0 1.4 3.2 0.225 0.315 0.315 0.630 1
Didi Gregorius ARI 2B, SS 2B, SS -7.1 -8.0 5.4 19.1 18.1 4.5 1.6 0.2 1.8 0.3 0.8 3.1 0.249 0.301 0.365 0.665 4
David Freese LAA 3B 3B -7.1 -6.5 5.5 20.0 18.8 4.7 2.1 0.3 2.1 0.1 1.1 5.8 0.249 0.310 0.363 0.674 74
Rene Rivera SD C C -7.5 -8.9 5.4 20.6 19.6 4.5 1.8 0.4 1.8 0.0 1.1 4.6 0.228 0.282 0.342 0.625 2
Will Venable SD OF OF -7.6 -6.6 6.0 21.8 20.7 4.8 1.9 0.4 1.8 0.7 1.1 5.9 0.235 0.283 0.342 0.625 44
Gordon Beckham LAA 2B, 3B 2B, 3B -8.0 -8.8 5.1 18.5 17.7 4.1 1.8 0.3 1.6 0.1 0.8 3.1 0.231 0.275 0.327 0.602 12
Gregor Blanco SF OF OF -9.1 -7.9 6.2 23.8 22.2 5.3 2.1 0.1 1.9 0.9 1.6 7.5 0.239 0.304 0.315 0.620 52
Joe Panik SF 2B 2B -10.0 -10.6 6.1 26.7 25.5 6.5 2.2 0.1 2.0 0.2 1.1 2.2 0.255 0.292 0.318 0.610 23
Adeiny Hechavarria MIA SS SS -10.7 -11.4 6.7 25.9 25.3 6.3 2.1 0.2 2.2 0.4 0.7 4.1 0.248 0.280 0.332 0.612 12
Cameron Maybin SD OF OF -10.7 -10.1 6.1 21.0 19.7 4.7 1.7 0.3 1.8 0.4 1.1 4.3 0.237 0.298 0.339 0.637 0
DJ LeMahieu COL 2B 2B, 3B -11.3 -11.9 5.5 20.3 19.5 4.8 1.3 0.1 1.3 0.5 0.8 3.6 0.248 0.283 0.300 0.583 32
Brandon Crawford SF SS SS -12.4 -14.2 6.4 23.5 22.0 5.0 1.9 0.3 2.0 0.1 1.4 6.0 0.226 0.291 0.332 0.623 33
Alexi Amarista SD 2B, SS, 3B, OF 2B, SS, 3B, OF -15.5 -16.4 6.3 23.7 22.9 5.2 1.7 0.2 1.7 0.2 0.9 2.6 0.228 0.269 0.297 0.566 60
Geovany Soto OAK C C 2.9 10.0 0

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

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

  9. malamoney

    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

        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

            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

                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

                    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

      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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