2018 ZiPS Projections – Chicago Cubs

by
February 5, 2018

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for half a decade. The exercise continues this offseason. Below are the projections for the Chicago Cubs. Szymborski can be found at ESPN and on Twitter at @DSzymborski.

ZiPS Projections
2018 2017

Batters
Dan Szymborski’s computer projects only three Cubs — Kris Bryant (670 PA, 5.8 zWAR), Anthony Rizzo (658, 4.9), and Addison Russell (508, 3.0) — to produce three wins or more in 2018, yet all eight of the positions on the depth-chart image below are forecast to reach that mark (within a rounding error, at least).

The cause of that discrepancy is as obvious as the deep, unabating terror in every mortal heart: the Cubs use platoons often and to good effect. Ben Zobrist (478, 1.9), for example, lacks a set role but is likely to complement Javier Baez (507, 1.7) and Jason Heyward (538, 2.3) at second base and right field, respectively. Ian Happ (545, 2.2), meanwhile, will probably share center and left fields with Albert Almora (437, 1.2) and Kyle Schwarber (511, 1.2).

As for weaknesses, no obvious one exists in the starting lineup as it’s presently constructed. That said, neither Almora nor Schwarber seem to be great candidates for a full-time role on a championship club — or, not according to ZiPS, at least. Were Happ to suffer an injury or fail to compensate for his strikeout rates with sufficient power on contact, then the team might be compelled to look for help elsewhere.

Pitchers
Towards the end of January, Craig Edwards contended that the Cubs would need to add Yu Darvish in order to secure their designation as one of the league’s “super teams.” The grounds for his argument: while Chicago’s starting five were strong enough as a group, that wasn’t sufficient. Most playoff teams, Edwards notes, require eight useful starters in a given season.

While not the nominal ace, left-hander Jose Quintana (186.0 IP, 4.9 zWAR) receives the top forecast from ZiPS. Kyle Hendricks (155.0, 3.2) and Jon Lester (170.3, 3.5), meanwhile, remain strong front-end starters. After Tyler Chatwood (142.1, 1.7) and Mike Montgomery (116.0, 1.5), however, the options are scarce.

In the bullpen, the club has assembled an incredibly balanced unit. Brandon Morrow (51.2 IP, 72 ERA-, 1.1 zWAR) is the closer, but Carl Edwards Jr. (60.0, 72, 1.2) and Justin Wilson (56.0, 70, 1.2) are also forecast to record adjusted ERAs about 30% lower than league average. Steve Cishek (51.1, 76, 0.9) and Pedro Strop (54.0, 76, 1.0) nearly rival that mark, as well.

Bench/Prospects
Because the Cubs are likely once again to employ job shares with some frequency, the club will roster less in the way of strict “bench players.” Tommy La Stella (286 PA, 0.3 zWAR) is probably the closest thing to a real reserve type. Victor Caratini (443, 1.2), meanwhile, earns the tops wins projection from ZiPS of all those players omitted from the depth-chart image below.

Among the possible internal means to addressing Chicago’s lack of rotation depth is right-hander Alec Mills (88.7 IP, 1.3 zWAR). As noted in Eric Longenhagen’s audit of the Cubs system, Mills doesn’t possess particularly impressive arm speed but does a number of other things well. He receives the top projection among those starters omitted from the depth chart below.

Depth Chart
Below is a rough depth chart for the present incarnation of the Cubs, with rounded projected WAR totals for each player. For caveats regarding WAR values see disclaimer at bottom of post. Click to enlarge image.

Ballpark graphic courtesy Eephus League. Depth charts constructed by way of those listed here at site and author’s own haphazard reasoning.

Batters, Counting Stats
Player B Age PO PA R H 2B 3B HR RBI SB CS
Kris Bryant R 26 3B 670 106 153 31 4 34 95 10 5
Anthony Rizzo L 28 1B 658 94 152 33 3 33 110 10 5
Addison Russell R 24 SS 508 63 113 27 3 17 71 4 2
Willson Contreras R 26 C 475 56 112 22 2 17 68 5 5
Jason Heyward L 28 RF 538 64 126 24 3 11 60 10 4
Ian Happ B 23 RF 545 77 124 23 3 28 85 11 5
Ben Zobrist B 37 2B 478 61 106 21 3 11 52 3 3
Javier Baez R 25 2B 507 63 119 21 2 20 71 12 3
Kyle Schwarber L 25 LF 511 78 103 19 3 31 81 2 2
Albert Almora R 24 CF 437 49 111 23 3 9 50 4 2
Victor Caratini B 24 C 443 49 104 25 3 9 47 1 0
David Bote R 25 2B 484 52 103 24 2 10 47 5 3
Mark Zagunis R 25 LF 442 55 89 20 3 11 48 7 4
Charcer Burks R 23 LF 531 60 109 21 4 12 45 13 10
Jon Jay L 33 CF 391 48 93 15 2 2 28 4 2
Jason Vosler L 24 3B 514 54 102 22 2 13 53 1 3
Ian Rice R 24 C 377 44 72 13 1 15 43 0 1
Chris Gimenez R 35 C 245 28 47 9 1 7 20 1 0
Tommy La Stella L 29 2B 286 27 66 14 1 4 29 1 1
Ryan Court R 30 SS 422 42 85 18 2 8 37 4 3
Bijan Rademacher L 27 RF 375 42 82 16 2 9 40 2 3
Elliot Soto R 28 SS 327 31 64 11 2 2 21 5 2
Ryan Kalish L 30 RF 323 35 71 13 3 7 32 5 3
Stephen Bruno R 27 3B 301 31 65 12 1 6 31 6 1
Jemile Weeks B 31 2B 249 25 50 12 3 1 18 4 0
Peter Bourjos R 31 LF 273 33 57 12 4 6 22 6 5
Taylor Davis R 28 C 381 40 82 21 2 6 38 0 2
Eddy Martinez R 23 RF 519 57 112 16 3 15 55 5 6
Jacob Hannemann L 27 CF 474 48 92 21 4 9 41 21 6
Chesny Young R 25 2B 522 52 122 21 2 2 33 9 7
Osvaldo Martinez R 30 SS 340 29 73 9 1 3 24 5 1
Ali Solis R 30 C 191 15 36 7 0 3 15 1 0
Mike Freeman L 30 SS 420 43 91 14 4 4 31 7 0
Efren Navarro L 32 1B 529 55 114 20 2 8 43 1 3
Yasiel Balaguert R 25 1B 518 50 111 21 1 14 57 1 1
Trey Martin R 25 CF 367 29 66 11 2 4 25 9 5
Roberto Caro B 24 LF 302 24 48 7 5 1 16 12 8
Daniel Spingola L 25 LF 386 36 76 16 5 4 29 4 1

***

Batters, Rates and Averages
Player PA BB% K% ISO BABIP AVG OBP SLG wOBA wRC+
Kris Bryant 670 13.1% 23.4% .250 .316 .272 .381 .522 .381 134
Anthony Rizzo 658 12.6% 15.0% .251 .282 .276 .389 .527 .385 137
Addison Russell 508 8.3% 24.0% .184 .299 .247 .317 .431 .317 92
Willson Contreras 475 9.5% 22.1% .182 .315 .265 .342 .448 .339 106
Jason Heyward 538 9.3% 15.1% .131 .294 .262 .335 .393 .316 92
Ian Happ 545 9.0% 29.0% .233 .315 .255 .325 .488 .341 107
Ben Zobrist 478 11.7% 14.0% .145 .279 .255 .345 .400 .323 96
Javier Baez 507 5.1% 27.0% .181 .315 .254 .300 .435 .306 85
Kyle Schwarber 511 11.5% 31.3% .265 .280 .231 .325 .496 .345 110
Albert Almora 437 4.8% 15.8% .137 .305 .272 .306 .409 .305 84
Victor Caratini 443 8.1% 19.9% .146 .310 .261 .325 .407 .313 89
David Bote 484 7.0% 23.3% .132 .292 .235 .300 .367 .290 74
Mark Zagunis 442 13.1% 25.1% .157 .306 .237 .348 .395 .326 98
Charcer Burks 531 9.8% 26.0% .139 .301 .232 .315 .371 .299 80
Jon Jay 391 6.9% 18.4% .072 .330 .267 .339 .339 .300 81
Jason Vosler 514 7.4% 26.7% .141 .282 .221 .292 .361 .284 70
Ian Rice 377 11.7% 28.9% .183 .277 .220 .316 .402 .312 89
Chris Gimenez 245 10.2% 26.5% .150 .280 .220 .306 .369 .296 78
Tommy La Stella 286 10.1% 14.0% .111 .294 .261 .339 .372 .311 88
Ryan Court 422 8.3% 30.3% .121 .313 .224 .295 .345 .281 68
Bijan Rademacher 375 9.1% 22.7% .140 .300 .245 .318 .385 .305 84
Elliot Soto 327 9.5% 21.1% .073 .284 .223 .299 .296 .263 57
Ryan Kalish 323 7.4% 21.7% .137 .295 .242 .305 .379 .297 79
Stephen Bruno 301 4.0% 22.9% .116 .291 .236 .288 .353 .279 67
Jemile Weeks 249 9.2% 16.1% .095 .271 .226 .301 .321 .276 65
Peter Bourjos 273 5.9% 25.3% .152 .290 .228 .281 .380 .283 70
Taylor Davis 381 7.3% 13.6% .124 .260 .237 .293 .361 .284 70
Eddy Martinez 519 6.0% 22.9% .139 .277 .232 .283 .371 .282 69
Jacob Hannemann 474 6.1% 29.1% .129 .288 .212 .269 .342 .265 58
Chesny Young 522 6.9% 15.9% .065 .304 .256 .310 .321 .280 67
Osvaldo Martinez 340 4.7% 17.4% .063 .271 .230 .267 .292 .245 45
Ali Solis 191 3.1% 29.8% .089 .270 .200 .232 .289 .226 32
Mike Freeman 420 7.1% 21.4% .089 .298 .237 .294 .326 .271 62
Efren Navarro 529 8.7% 20.4% .100 .290 .238 .304 .338 .281 68
Yasiel Balaguert 518 5.0% 27.0% .133 .288 .227 .266 .361 .269 60
Trey Martin 367 4.1% 35.1% .078 .291 .192 .231 .270 .219 27
Roberto Caro 302 8.9% 33.1% .075 .283 .181 .263 .257 .234 37
Daniel Spingola 386 7.0% 31.3% .108 .314 .216 .278 .324 .262 56

***

Batters, Assorted Other
Player PA RC/27 OPS+ Def zWAR No.1 Comp
Kris Bryant 670 7.4 139 3 5.8 Ron Santo
Anthony Rizzo 658 7.7 142 6 4.9 Norm Cash
Addison Russell 508 4.9 98 11 3.0 Tony Batista
Willson Contreras 475 5.5 109 -1 2.8 Geovany Soto
Jason Heyward 538 4.9 94 14 2.3 Tommy Gregg
Ian Happ 545 5.8 114 2 2.2 Chili Davis
Ben Zobrist 478 4.9 99 3 1.9 Ray Durham
Javier Baez 507 4.9 94 4 1.7 Bret Boone
Kyle Schwarber 511 5.6 115 -8 1.2 Franklin Stubbs
Albert Almora 437 4.7 89 3 1.2 Curt Flood
Victor Caratini 443 4.9 95 -7 1.2 Dave Sax
David Bote 484 3.9 78 4 0.8 Adam Heether
Mark Zagunis 442 4.8 99 -4 0.7 Chase Headley
Charcer Burks 531 4.0 83 6 0.6 Glenn Reeves
Jon Jay 391 4.2 83 -1 0.5 Rick Miller
Jason Vosler 514 3.5 74 3 0.4 Brad Seitzer
Ian Rice 377 4.3 91 -11 0.4 Jonathan Still
Chris Gimenez 245 4.0 80 -3 0.4 Jim Sundberg
Tommy La Stella 286 4.6 90 -4 0.3 Bret Barberie
Ryan Court 422 3.5 71 -1 0.2 Nick Green
Bijan Rademacher 375 4.3 87 0 0.2 Bubba Carpenter
Elliot Soto 327 3.1 61 3 0.2 Chris Gutierrez
Ryan Kalish 323 4.1 82 0 0.0 Ted Wood
Stephen Bruno 301 3.7 70 -1 0.0 Adam Fox
Jemile Weeks 249 3.6 67 -1 0.0 Kelvin Chapman
Peter Bourjos 273 3.6 75 3 -0.1 Jarvis Brown
Taylor Davis 381 3.6 74 -7 -0.1 Dave Van Gorder
Eddy Martinez 519 3.5 73 6 -0.1 Angel Echevarria
Jacob Hannemann 474 3.4 62 -1 -0.4 Richie Robnett
Chesny Young 522 3.5 70 -3 -0.4 Liu Rodriguez
Osvaldo Martinez 340 2.8 50 2 -0.4 Jackie Gutierrez
Ali Solis 191 2.3 38 1 -0.4 Charlie Greene
Mike Freeman 420 3.6 66 -7 -0.5 Jose Uribe
Efren Navarro 529 3.5 72 5 -0.6 Chris Pritchett
Yasiel Balaguert 518 3.3 66 3 -1.2 Bryan Lahair
Trey Martin 367 2.1 34 5 -1.3 Jim Murphy
Roberto Caro 302 2.1 40 2 -1.3 Randy Strijek
Daniel Spingola 386 3.2 61 -5 -1.5 Kevin Wiggins

***

Pitchers, Counting Stats
Player T Age G GS IP K BB HR H R ER
Jose Quintana L 29 30 30 186.0 188 51 16 166 71 66
Jon Lester L 34 29 29 170.3 169 48 21 156 72 67
Kyle Hendricks R 28 27 27 155.0 136 39 18 146 64 60
Jake Arrieta R 32 29 29 161.7 158 56 20 143 71 66
Tyler Chatwood R 28 30 25 142.3 115 66 15 133 72 67
John Lackey R 39 26 25 146.0 129 44 26 144 77 72
Mike Montgomery L 28 38 15 116.0 96 51 13 107 57 53
Alec Mills R 26 20 18 88.7 70 25 10 93 43 40
Carl Edwards Jr. R 26 67 0 60.0 92 38 5 38 22 21
Justin Wilson L 30 64 0 56.0 78 30 4 40 20 19
Brandon Morrow R 33 53 0 51.7 54 17 4 46 19 18
Pedro Strop R 33 63 0 54.0 64 23 5 42 21 20
Drew Smyly L 29 21 20 113.0 106 37 22 113 63 59
Steve Cishek R 32 57 0 51.3 56 21 5 43 20 19
Rob Zastryzny L 26 25 15 94.0 78 38 12 98 49 46
Williams Perez R 27 23 22 113.7 85 50 13 118 63 59
Brian Duensing L 35 57 0 53.3 48 17 5 50 22 21
Dillon Maples R 26 59 0 65.7 94 56 5 50 31 29
Anthony Bass R 30 27 12 82.7 71 36 11 84 46 43
Koji Uehara R 43 42 0 37.0 45 10 6 33 16 15
Daury Torrez R 25 45 1 70.7 49 19 9 77 35 33
James Pugliese R 25 41 7 77.0 54 40 7 81 42 39
Trevor Clifton R 23 22 22 102.0 82 51 14 109 60 56
Dario Alvarez L 29 43 0 47.0 63 25 7 40 22 21
Cory Mazzoni R 28 30 0 40.3 44 10 5 39 18 17
Zach Hedges R 25 24 24 135.0 65 43 19 162 81 76
Dan Camarena L 25 23 22 113.0 73 37 20 128 68 64
Jen-Ho Tseng R 23 25 24 132.3 97 43 24 150 80 75
Justin Grimm R 29 62 0 61.7 72 31 9 54 31 29
Kyle Ryan L 26 62 0 60.3 42 34 4 59 31 29
Matt Carasiti R 26 52 0 55.3 63 29 8 51 29 27
Craig Brooks R 25 45 0 56.3 68 48 5 47 30 28
Eddie Butler R 27 23 21 111.3 65 47 16 126 68 64
Brad Markey R 26 34 9 86.7 59 27 15 99 51 48
Adbert Alzolay R 23 22 22 103.3 77 43 18 115 64 60
Manny Parra L 35 35 0 26.7 22 12 3 27 14 13
Duane Underwood R 23 24 23 118.0 78 58 17 132 74 69
Randy Rosario L 24 33 5 71.7 48 37 9 78 43 40
Oscar de la Cruz R 23 14 13 55.0 43 20 11 62 34 32
Justin Hancock R 27 47 0 53.7 44 31 6 54 30 28
Tom Hatch R 23 26 26 117.7 97 65 17 128 75 70
Scott Carroll R 33 21 14 84.0 45 37 14 98 56 52
David Rollins L 28 34 3 46.3 36 22 9 52 31 29
Alberto Baldonado L 25 50 0 56.3 58 36 10 56 35 33
Ross Detwiler L 32 30 11 76.7 55 39 13 87 51 48
Matt Swarmer R 24 23 11 90.0 73 33 21 104 61 57
David Garner R 25 38 0 46.3 47 34 9 48 33 31
Jake Stinnett R 26 18 15 79.0 65 45 17 89 57 53
Luke Farrell R 27 28 19 109.0 75 56 22 127 79 74

***

Pitchers, Rates and Averages
Player IP TBF K% BB% BABIP ERA FIP ERA- FIP-
Jose Quintana 186.0 775 24.3% 6.6% .292 3.19 3.13 73 72
Jon Lester 170.3 715 23.6% 6.7% .286 3.54 3.69 81 85
Kyle Hendricks 155.0 650 20.9% 6.0% .284 3.48 3.74 80 86
Jake Arrieta 161.7 684 23.1% 8.2% .278 3.67 3.94 84 91
Tyler Chatwood 142.3 626 18.4% 10.5% .277 4.24 4.34 97 100
John Lackey 146.0 626 20.6% 7.0% .282 4.44 4.75 102 109
Mike Montgomery 116.0 506 19.0% 10.1% .278 4.11 4.43 94 102
Alec Mills 88.7 384 18.2% 6.5% .303 4.06 4.03 93 92
Carl Edwards Jr. 60.0 256 35.9% 14.8% .277 3.15 3.12 72 72
Justin Wilson 56.0 238 32.8% 12.6% .290 3.05 2.91 70 67
Brandon Morrow 51.7 218 24.8% 7.8% .298 3.14 3.12 72 72
Pedro Strop 54.0 227 28.2% 10.1% .282 3.33 3.38 76 78
Drew Smyly 113.0 489 21.7% 7.6% .282 4.70 4.79 108 110
Steve Cishek 51.3 218 25.7% 9.6% .284 3.33 3.47 76 80
Rob Zastryzny 94.0 418 18.7% 9.1% .302 4.40 4.53 101 104
Williams Perez 113.7 509 16.7% 9.8% .296 4.67 4.57 107 105
Brian Duensing 53.3 227 21.1% 7.5% .290 3.54 3.53 81 81
Dillon Maples 65.7 303 31.0% 18.5% .313 3.97 4.03 91 92
Anthony Bass 82.7 368 19.3% 9.8% .297 4.68 4.55 107 104
Koji Uehara 37.0 154 29.2% 6.5% .293 3.65 3.56 84 82
Daury Torrez 70.7 308 15.9% 6.2% .300 4.20 4.32 96 99
James Pugliese 77.0 352 15.3% 11.4% .301 4.56 4.65 105 107
Trevor Clifton 102.0 466 17.6% 10.9% .304 4.94 5.01 113 115
Dario Alvarez 47.0 206 30.6% 12.1% .306 4.02 4.14 92 95
Cory Mazzoni 40.3 170 25.9% 5.9% .312 3.79 3.48 87 80
Zach Hedges 135.0 610 10.7% 7.0% .301 5.07 5.14 116 118
Dan Camarena 113.0 504 14.5% 7.3% .293 5.10 5.31 117 122
Jen-Ho Tseng 132.3 590 16.4% 7.3% .301 5.10 5.16 117 118
Justin Grimm 61.7 270 26.7% 11.5% .288 4.23 4.28 97 98
Kyle Ryan 60.3 274 15.3% 12.4% .288 4.33 4.27 99 98
Matt Carasiti 55.3 246 25.6% 11.8% .301 4.39 4.44 101 102
Craig Brooks 56.3 264 25.8% 18.2% .302 4.47 4.56 103 105
Eddie Butler 111.3 507 12.8% 9.3% .294 5.17 5.21 119 120
Brad Markey 86.7 386 15.3% 7.0% .300 4.98 5.12 114 118
Adbert Alzolay 103.3 468 16.5% 9.2% .299 5.23 5.35 120 123
Manny Parra 26.7 119 18.5% 10.1% .296 4.39 4.32 101 99
Duane Underwood 118.0 544 14.3% 10.7% .299 5.26 5.34 121 123
Randy Rosario 71.7 330 14.5% 11.2% .297 5.02 5.17 115 119
Oscar de la Cruz 55.0 247 17.4% 8.1% .300 5.24 5.45 120 125
Justin Hancock 53.7 246 17.9% 12.6% .296 4.70 4.76 108 109
Tom Hatch 117.7 546 17.8% 11.9% .308 5.35 5.20 123 119
Scott Carroll 84.0 387 11.6% 9.6% .295 5.57 5.75 128 132
David Rollins 46.3 213 16.9% 10.3% .299 5.63 5.55 129 128
Alberto Baldonado 56.3 261 22.2% 13.8% .299 5.27 5.43 121 125
Ross Detwiler 76.7 356 15.4% 11.0% .303 5.63 5.61 129 129
Matt Swarmer 90.0 407 17.9% 8.1% .303 5.70 5.87 131 135
David Garner 46.3 221 21.3% 15.4% .305 6.02 6.05 138 139
Jake Stinnett 79.0 371 17.5% 12.1% .301 6.04 6.21 139 143
Luke Farrell 109.0 510 14.7% 11.0% .299 6.11 6.11 140 140

***

Pitchers, Assorted Other
Player IP K/9 BB/9 HR/9 ERA+ zWAR No. 1 Comp
Jose Quintana 186.0 9.10 2.47 0.77 136 4.9 Andy Pettitte
Jon Lester 170.3 8.93 2.54 1.11 123 3.5 Tom Glavine
Kyle Hendricks 155.0 7.90 2.26 1.05 123 3.2 Tim Hudson
Jake Arrieta 161.7 8.80 3.12 1.11 117 3.0 Dave Stieb
Tyler Chatwood 142.3 7.27 4.17 0.95 101 1.7 Paul Foytack
John Lackey 146.0 7.95 2.71 1.60 98 1.6 Bob Forsch
Mike Montgomery 116.0 7.45 3.96 1.01 106 1.5 Lou Brissie
Alec Mills 88.7 7.11 2.54 1.02 107 1.3 John Hope
Carl Edwards Jr. 60.0 13.80 5.70 0.75 136 1.2 Armando Benitez
Justin Wilson 56.0 12.54 4.82 0.64 140 1.2 Will Ohman
Brandon Morrow 51.7 9.41 2.96 0.70 138 1.1 Chad Bradford
Pedro Strop 54.0 10.67 3.83 0.83 129 1.0 Turk Wendell
Drew Smyly 113.0 8.44 2.95 1.75 92 0.9 Curt Young
Steve Cishek 51.3 9.82 3.68 0.88 129 0.9 Turk Wendell
Rob Zastryzny 94.0 7.47 3.64 1.15 97 0.9 Scott Sauerbeck
Williams Perez 113.7 6.73 3.96 1.03 92 0.8 Steve Arlin
Brian Duensing 53.3 8.10 2.87 0.84 123 0.7 Tony Castillo
Dillon Maples 65.7 12.88 7.68 0.69 109 0.6 Duane Ward
Anthony Bass 82.7 7.73 3.92 1.20 93 0.6 Mike Heathcott
Koji Uehara 37.0 10.95 2.43 1.46 119 0.5 Russ Springer
Daury Torrez 70.7 6.24 2.42 1.15 102 0.5 Donnie Moore
James Pugliese 77.0 6.31 4.68 0.82 94 0.5 Walt Masterson
Trevor Clifton 102.0 7.24 4.50 1.24 87 0.5 Ed Wojna
Dario Alvarez 47.0 12.06 4.79 1.34 107 0.4 Tim Fortugno
Cory Mazzoni 40.3 9.82 2.23 1.12 113 0.4 Brandon Lyon
Zach Hedges 135.0 4.33 2.87 1.27 85 0.4 Michael Macdonald
Dan Camarena 113.0 5.81 2.95 1.59 85 0.4 Eric Knott
Jen-Ho Tseng 132.3 6.60 2.92 1.63 84 0.4 Sergio Mitre
Justin Grimm 61.7 10.51 4.52 1.31 101 0.4 Todd Wellemeyer
Kyle Ryan 60.3 6.27 5.07 0.60 100 0.3 Mike Cosgrove
Matt Carasiti 55.3 10.25 4.72 1.30 98 0.2 Roy Corcoran
Craig Brooks 56.3 10.86 7.67 0.80 97 0.2 Clay Bryant
Eddie Butler 111.3 5.25 3.80 1.29 83 0.2 Kevin Hodges
Brad Markey 86.7 6.13 2.80 1.56 86 0.2 Brad Rigby
Adbert Alzolay 103.3 6.71 3.75 1.57 82 0.1 Jake Joseph
Manny Parra 26.7 7.43 4.05 1.01 98 0.1 Darold Knowles
Duane Underwood 118.0 5.95 4.42 1.30 81 0.1 Jake Dittler
Randy Rosario 71.7 6.03 4.65 1.13 86 0.1 Scott Rice
Oscar de la Cruz 55.0 7.04 3.27 1.80 82 0.1 Daniel Haigwood
Justin Hancock 53.7 7.38 5.20 1.01 91 0.0 Horacio Pina
Tom Hatch 117.7 7.42 4.97 1.30 80 0.0 Jake Joseph
Scott Carroll 84.0 4.82 3.96 1.50 78 -0.2 Sid Hudson
David Rollins 46.3 6.99 4.27 1.75 77 -0.3 Danny Young
Alberto Baldonado 56.3 9.27 5.75 1.60 81 -0.3 Carlos Cabassa
Ross Detwiler 76.7 6.46 4.58 1.53 76 -0.4 Hal Elliott
Matt Swarmer 90.0 7.30 3.30 2.10 75 -0.4 Ken Ryan
David Garner 46.3 9.13 6.60 1.75 71 -0.6 Adalberto Mendez
Jake Stinnett 79.0 7.41 5.13 1.94 71 -0.7 Mark Woodyard
Luke Farrell 109.0 6.19 4.62 1.82 71 -1.0 Mike Windham

***

Disclaimer: ZiPS projections are computer-based projections of performance. Performances have not been allocated to predicted playing time in the majors — many of the players listed above are unlikely to play in the majors at all in 2017. ZiPS is projecting equivalent production — a .240 ZiPS projection may end up being .280 in AAA or .300 in AA, for example. Whether or not a player will play is one of many non-statistical factors one has to take into account when predicting the future.

Players are listed with their most recent teams unless Dan has made a mistake. This is very possible as a lot of minor-league signings are generally unreported in the offseason.

ZiPS is projecting based on the AL having a 4.24 ERA and the NL having a 4.18 ERA.

Players that are expected to be out due to injury are still projected. More information is always better than less information and a computer isn’t what should be projecting the injury status of, for example, a pitcher with Tommy John surgery.

Regarding ERA+ vs. ERA- (and FIP+ vs. FIP-) and the differences therein: as Patriot notes here, they are not simply mirror images of each other. Writes Patriot: “ERA+ does not tell you that a pitcher’s ERA was X% less or more than the league’s ERA. It tells you that the league’s ERA was X% less or more than the pitcher’s ERA.”

Both hitters and pitchers are ranked by projected zWAR — which is to say, WAR values as calculated by Dan Szymborski, whose surname is spelled with a z. WAR values might differ slightly from those which appear in full release of ZiPS. Finally, Szymborski will advise anyone against — and might karate chop anyone guilty of — merely adding up WAR totals on depth chart to produce projected team WAR.





 

Carson Cistulli has published a book of aphorisms called Spirited Ejaculations of a New Enthusiast.

41 Comments
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Francoeursteinmember
6 years ago

Is Heyward irretrievably broken at this point? I don’t think that’s a ridiculous claim after two consecutive down years at the plate. If you would have told me after his rookie year that he’d be a defense-first outfielder projected for a 92 wRC+ in his late 20’s, I would have called you crazy.

21
LHPSU
6 years ago
Reply to  Francoeurstein

Surely you understand the feeling, Francoeur.

43
sadtrombonemember
6 years ago
Reply to  Francoeurstein

Something I noticed the other day: Heyward’s opt-out was set to coincide perfectly with Bryce Harper’s free agency.

It’s not too hard to imagine what the Cubs were thinking. Heyward was supposed to put up a wRC+ of about 120 with elite defense in right field, and opt out. Then the Cubs could pursue Harper for an even larger contract, but with another opt-out after Year 3. The Cubs would never have to pay the back half of any of these massive, bloated contracts, and get peak production from superstars in free agency.

This is one of those “genius if it works” plans that has backfired spectacularly. I know Heyward has a great work ethic, but it doesn’t look good from here.

12
output gap
6 years ago
Reply to  sadtrombone

Jason Heyward’s no trade clause drops from full to 12 teams only if he opts into his deal. It was also front loaded, so he will be traded if the Cubs intend on signing Harper.

Given that Heyward will be 29 after this season and the price of a win is >$9 million, the contract is not nearly debilitating for the franchise.

TL:DR, Heyward will be traded for Melancon next November when they both opt in.

-3
sadtrombonemember
6 years ago
Reply to  output gap

The price of a win is whatever you can get your free agents to take for the expected utility (projected WAR) you’re going to get out of him, not how much WAR you actually received. Let’s take the example we are talking about to demonstrate why the whole $9 million/win number is a fundamental misreading of Matt Swartz’s work:

Using the methodology that Matt Swartz does, here is how we would calculate the “price of a win” if Jason Heyward was the only free agent:

-He signs an 8 year, $184 million contract
-He has put up 2.4 wins in the last two years
-He is projected for 2.3 zWAR next year, and since his defense is likely to decline as he enters his 30s, that will likely be the high-water mark. Let’s put him down for an average of 2.1 WAR for the next 6 years.
-With 15 WAR, we can now conclude that in our fictional free agency scenario, the cost of a win is $12.667 million per win, adjusted by whatever inflation indicator you want to use.

But do we really think Cubs’ expected utility for Jason Heyward was a bunch of years just above 2 WAR? Of course not. What this example shows is that the $9 million/WAR number covers the efficiency of buying wins on the free agent market, and not necessarily the expected utility.

All this is to say, that contract is awful, and it’s more likely they’d swap him for a Johnny Cueto who doesn’t bounce back than Melancon.

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output gap
6 years ago
Reply to  sadtrombone

BR has Heyward worth 3.8 WAR over the last two years versus FG with 2.4. The difference is basically how many runs he’s preventing on defense. Let’s split the difference and call it 3.1, or 1.6 per season.

$106 million over 5 years is the remaining deal after this year is complete. Using the Schwartz 12.667M figure you cited, he’ll be “required” to produce ~8.5 WAR to “justify” the contract (conveniently 1.7 WAR per season). It’s a bad deal. It didn’t work out in Dollar per WAR terms. It’s also only modestly underwater due to changes in the market place. Could 29 year old Heyward after a 92 wRC+ / 2.3 WAR season get $106M/5? No. No one would take the 5 year risk. But if he was going to be priced for a 5 year contract, it would look something like $80/5 with huge error bars.

The loss on this contract has already been booked, in the first 2-3 years of the deal. He has provided 3 WAR at cost of $49.8 Million. That’s atrocious.

If he’s a 90 wRC+ RF with +14 DEF as ZiPS suggests, the remaining deal is not underwater by nearly as much as you are saying.

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sadtrombonemember
6 years ago
Reply to  output gap

I think you missed the point of my prior post. The point was not that Heyward’s contract is underwater, which everyone agrees it is. The point is that the reasoning I’m using here is exactly where that $/win figure you are citing is coming from. I was trying to demonstrate how incorrect it was, and that we use Swartz’s $/win number in completely the wrong way.

To put it another way, $9 million/win does not show how teams value wins. It shows you how efficient spending on free agency is. When it goes up, it means teams are getting less results for their money, not that the valuation changed.

My specific point is that it is unlikely that that a position player would get $80m/5years for an expected utility of 2 wins for the next 5 years. (This, incidentally, is why Mike Moustakas remains unsigned)

If you want to look at the expected cost of a win, take a prospective projection (like zips) for the life of the contract and figure out how much players are signing for. It will be way closer to capturing the expected utility than whether someone opened up the package and found lemons instead of lemonade.

EDIT: The key distinction here is between expected utility and revealed choice. In marketplaces where you are fairly certain you will get what you pay for, the practical difference doesn’t matter so much. In marketplaces like this one, where your expectations of what you’ll get differ from what you actually receive, it is very important to distinguish them.
See here: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.5406&rep=rep1&type=pdf

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