2015 ZiPS Projections – Chicago White Sox
After having typically appeared in the very hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have been released at FanGraphs the past couple years. The exercise continues this offseason. Below are the projections for the Chicago White Sox. Szymborski can be found at ESPN and on Twitter at @DSzymborski.
Other Projections: Atlanta / Colorado / Los Angeles AL / Miami / Milwaukee / Oakland / Tampa Bay.
Batters
On the strength of his five wins, Jose Abreu was worth approximately $30 million in 2014. Should he regress a little but still manage the 3.5 WAR projected here by ZiPS, Abreu will have produced approximately $50 million in value over the first two years of the six-year, $68 million contract he signed in October of last year. Even if he ultimately opts in to arbitration (which he’s permitted to do — and almost certainly will do, at this rate — under the terms of his contract), the probability remains that Abreu will have provided an excellent return on investment.
Elsewhere around the field, it’s more difficult to find such optimism. As noted by Jeff Sullivan on Monday, the White Sox’ rate above average only at first base and DH according to the Steamer projections. Indeed, ZiPS paints a similar portrait — with the exception of center field Adam Eaton, perhaps, for whose 2015 season it’s decidedly more encouraging.
Pitchers
With the recent addition of Jeff Samardzija to the Sox’ rotation, the club now features three starters forecast to produce something in the vicinity of 12 wins collectively — which is to say, slightly better than the average total WAR figures produced by whole starting rotations in 2014. After that triumvirate, however, there’s a significant dearth of useful options, it appears.
The top-five relievers for the White Sox are projected to produce a 2.0 WAR as a group according to ZiPS. In a very related development, free-agent signings Zach Duke and David Robertson are projected to produce about 1.5 WAR together. Featuring below-average velocity for a reliever but also a split-finger pitch he threw over 50% of the time in 2014, Zach Putnam would appear to be the most qualified candidate for a seventh-inning role in front of Duke and Robertson.
Bench/Prospects
Ignoring for a moment the effect it might have both on his development and also his free-agent timetable, it seems as though installing Carlos Rodon into the rotation at the beginning of the season might allow the club to win an extra game or two by the end of the year. He’s the only White Sox starter (besides the top three mentioned above) projected to record better than replacement-level value. With regard to field players, ZiPS continues to see enough in the minor-league resume of outfielder Jordan Danks to project him as a useful bench player.
Depth Chart
Below is a rough depth chart for the present incarnation of the White Sox, with rounded projected WAR totals for each player. For caveats regarding WAR values see disclaimer at bottom of post. Click to embiggen 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jose Abreu | R | 28 | 1B | 582 | 83 | 149 | 28 | 1 | 33 | 88 | 3 | 4 |
| Adam Eaton | L | 26 | CF | 653 | 83 | 158 | 28 | 7 | 5 | 50 | 26 | 12 |
| Alexei Ramirez | R | 33 | SS | 641 | 71 | 163 | 32 | 2 | 10 | 62 | 19 | 6 |
| Melky Cabrera | B | 30 | LF | 598 | 81 | 163 | 29 | 4 | 14 | 67 | 9 | 4 |
| Adam LaRoche | L | 35 | 1B | 494 | 61 | 102 | 17 | 1 | 25 | 74 | 2 | 0 |
| Tyler Flowers | R | 29 | C | 338 | 35 | 71 | 13 | 1 | 13 | 36 | 1 | 1 |
| Jordan Danks | L | 28 | CF | 466 | 53 | 96 | 15 | 1 | 14 | 45 | 9 | 3 |
| Kevan Smith | R | 27 | C | 459 | 48 | 97 | 20 | 1 | 11 | 47 | 1 | 2 |
| Conor Gillaspie | L | 27 | 3B | 522 | 57 | 124 | 23 | 4 | 12 | 57 | 1 | 3 |
| Carlos Sanchez | B | 23 | 2B | 582 | 57 | 135 | 20 | 4 | 6 | 44 | 15 | 6 |
| Tyler Saladino | R | 25 | SS | 456 | 49 | 93 | 15 | 3 | 9 | 41 | 14 | 5 |
| Dayan Viciedo | R | 26 | RF | 570 | 66 | 134 | 24 | 3 | 22 | 71 | 0 | 1 |
| Trayce Thompson | R | 24 | CF | 594 | 68 | 109 | 24 | 4 | 20 | 63 | 15 | 5 |
| Matt Davidson | R | 24 | 3B | 576 | 62 | 111 | 21 | 1 | 20 | 61 | 1 | 1 |
| Leury Garcia | B | 24 | 3B | 320 | 31 | 70 | 9 | 3 | 3 | 19 | 21 | 5 |
| Avisail Garcia | R | 24 | RF | 433 | 50 | 109 | 13 | 4 | 13 | 55 | 9 | 5 |
| Rob Brantly | L | 25 | C | 433 | 38 | 95 | 18 | 1 | 5 | 37 | 0 | 1 |
| Micah Johnson | L | 24 | 2B | 544 | 58 | 126 | 16 | 6 | 8 | 44 | 31 | 18 |
| Rangel Ravelo | R | 23 | 1B | 525 | 60 | 121 | 27 | 2 | 11 | 54 | 8 | 5 |
| Jeff Keppinger | R | 35 | 3B | 269 | 27 | 69 | 9 | 1 | 4 | 25 | 0 | 0 |
| Andy Wilkins | L | 26 | 1B | 562 | 68 | 126 | 27 | 1 | 24 | 75 | 2 | 2 |
| Adrian Nieto | B | 25 | C | 285 | 28 | 54 | 11 | 0 | 6 | 23 | 2 | 2 |
| Michael Taylor | R | 29 | RF | 522 | 56 | 106 | 23 | 1 | 12 | 50 | 7 | 2 |
| Christian Marrero | L | 28 | 1B | 337 | 36 | 64 | 14 | 2 | 9 | 33 | 5 | 2 |
| Juan Diaz | L | 26 | SS | 520 | 51 | 111 | 21 | 1 | 9 | 47 | 4 | 2 |
| Tony Campana | L | 29 | CF | 436 | 47 | 93 | 9 | 4 | 1 | 22 | 27 | 9 |
| Tim Anderson | R | 22 | SS | 491 | 53 | 108 | 16 | 4 | 9 | 40 | 13 | 9 |
| Dan Black | B | 27 | 1B | 494 | 55 | 103 | 20 | 1 | 14 | 53 | 4 | 2 |
| J.B. Shuck | L | 28 | LF | 512 | 58 | 118 | 17 | 4 | 4 | 41 | 9 | 5 |
| Brennan Boesch | L | 30 | RF | 389 | 44 | 88 | 17 | 3 | 14 | 46 | 6 | 2 |
| Matt Tuiasosopo | R | 29 | LF | 416 | 45 | 76 | 12 | 0 | 10 | 41 | 2 | 1 |
| Courtney Hawkins | R | 21 | LF | 495 | 55 | 83 | 16 | 2 | 22 | 62 | 8 | 4 |
| Jared Mitchell | L | 26 | LF | 484 | 52 | 77 | 11 | 3 | 15 | 43 | 12 | 7 |
| Paul Konerko | R | 39 | 1B | 318 | 25 | 68 | 11 | 0 | 7 | 34 | 0 | 0 |
***
Batters, Rates and Averages
| Player | PA | BB% | K% | ISO | BABIP | AVG | OBP | SLG | wOBA |
|---|---|---|---|---|---|---|---|---|---|
| Jose Abreu | 582 | 9.6% | 18.6% | .252 | .310 | .292 | .371 | .544 | .389 |
| Adam Eaton | 653 | 8.9% | 17.0% | .099 | .331 | .274 | .354 | .373 | .327 |
| Alexei Ramirez | 641 | 3.9% | 11.2% | .109 | .291 | .270 | .303 | .379 | .302 |
| Melky Cabrera | 598 | 6.7% | 12.0% | .144 | .318 | .296 | .342 | .440 | .342 |
| Adam LaRoche | 494 | 12.8% | 23.9% | .221 | .268 | .241 | .336 | .462 | .342 |
| Tyler Flowers | 338 | 6.5% | 33.4% | .175 | .317 | .231 | .294 | .406 | .310 |
| Jordan Danks | 466 | 9.7% | 30.7% | .143 | .314 | .232 | .308 | .375 | .303 |
| Kevan Smith | 459 | 6.1% | 21.8% | .131 | .277 | .232 | .290 | .363 | .289 |
| Conor Gillaspie | 522 | 7.7% | 16.7% | .141 | .295 | .261 | .319 | .402 | .313 |
| Carlos Sanchez | 582 | 5.3% | 17.9% | .087 | .301 | .253 | .297 | .340 | .281 |
| Tyler Saladino | 456 | 7.9% | 23.0% | .118 | .281 | .227 | .294 | .345 | .287 |
| Dayan Viciedo | 570 | 5.8% | 20.9% | .182 | .286 | .254 | .302 | .436 | .321 |
| Trayce Thompson | 594 | 7.9% | 35.0% | .172 | .285 | .204 | .272 | .376 | .289 |
| Matt Davidson | 576 | 8.0% | 33.2% | .161 | .293 | .214 | .286 | .375 | .293 |
| Leury Garcia | 320 | 4.7% | 27.5% | .080 | .322 | .235 | .273 | .315 | .264 |
| Avisail Garcia | 433 | 4.6% | 24.0% | .148 | .330 | .269 | .309 | .417 | .319 |
| Rob Brantly | 433 | 5.1% | 18.9% | .086 | .280 | .235 | .275 | .321 | .264 |
| Micah Johnson | 544 | 5.9% | 18.9% | .104 | .302 | .253 | .299 | .357 | .287 |
| Rangel Ravelo | 525 | 7.8% | 17.9% | .136 | .296 | .256 | .319 | .392 | .313 |
| Jeff Keppinger | 269 | 4.8% | 7.8% | .092 | .284 | .275 | .310 | .367 | .297 |
| Andy Wilkins | 562 | 6.6% | 24.2% | .195 | .281 | .242 | .294 | .437 | .318 |
| Adrian Nieto | 285 | 8.4% | 32.6% | .113 | .304 | .212 | .283 | .325 | .272 |
| Michael Taylor | 522 | 9.4% | 23.6% | .132 | .282 | .228 | .307 | .360 | .300 |
| Christian Marrero | 337 | 11.0% | 23.4% | .153 | .263 | .218 | .304 | .371 | .298 |
| Juan Diaz | 520 | 4.8% | 29.4% | .103 | .310 | .227 | .266 | .330 | .264 |
| Tony Campana | 436 | 5.7% | 22.5% | .050 | .304 | .232 | .280 | .282 | .255 |
| Tim Anderson | 491 | 3.7% | 25.5% | .111 | .302 | .235 | .276 | .346 | .275 |
| Dan Black | 494 | 8.9% | 25.5% | .144 | .290 | .233 | .302 | .377 | .302 |
| J.B. Shuck | 512 | 7.2% | 9.2% | .079 | .272 | .254 | .307 | .333 | .284 |
| Brennan Boesch | 389 | 5.7% | 22.6% | .180 | .281 | .243 | .288 | .423 | .311 |
| Matt Tuiasosopo | 416 | 10.8% | 30.0% | .115 | .286 | .209 | .301 | .324 | .286 |
| Courtney Hawkins | 495 | 7.3% | 42.4% | .191 | .276 | .185 | .251 | .376 | .277 |
| Jared Mitchell | 484 | 9.9% | 43.0% | .147 | .304 | .182 | .272 | .329 | .269 |
| Paul Konerko | 318 | 6.3% | 17.6% | .110 | .264 | .234 | .289 | .344 | .282 |
***
Batters, Assorted Other
| Player | PA | RC/27 | OPS+ | Def | zWAR | No.1 Comp |
|---|---|---|---|---|---|---|
| Jose Abreu | 582 | 7.6 | 147 | -4 | 3.5 | Willie Horton |
| Adam Eaton | 653 | 5.0 | 100 | 1 | 2.8 | Kenny Lofton |
| Alexei Ramirez | 641 | 4.4 | 85 | 1 | 2.0 | Bill Russell |
| Melky Cabrera | 598 | 5.8 | 113 | -4 | 1.7 | Shannon Stewart |
| Adam LaRoche | 494 | 5.6 | 116 | 1 | 1.5 | Robin Ventura |
| Tyler Flowers | 338 | 4.2 | 89 | -1 | 1.3 | Tim Glass |
| Jordan Danks | 466 | 4.1 | 86 | 3 | 1.3 | Kurt Airoso |
| Kevan Smith | 459 | 3.6 | 78 | 1 | 1.3 | Blake Barthol |
| Conor Gillaspie | 522 | 4.6 | 96 | -4 | 1.1 | Scott Cooper |
| Carlos Sanchez | 582 | 3.7 | 74 | 6 | 0.9 | Martin Prado |
| Tyler Saladino | 456 | 3.6 | 74 | 1 | 0.8 | Raul Rodarte |
| Dayan Viciedo | 570 | 4.7 | 99 | -5 | 0.4 | Scott Thorman |
| Trayce Thompson | 594 | 3.6 | 75 | 0 | 0.4 | Chad Hermansen |
| Matt Davidson | 576 | 3.7 | 79 | -2 | 0.4 | Jared Sandberg |
| Leury Garcia | 320 | 3.4 | 61 | 6 | 0.4 | Mike Jirschele |
| Avisail Garcia | 433 | 4.7 | 97 | -3 | 0.3 | David Green |
| Rob Brantly | 433 | 3.1 | 63 | 1 | 0.2 | Jorge Fabregas |
| Micah Johnson | 544 | 3.8 | 79 | -3 | 0.2 | Herman Iribarren |
| Rangel Ravelo | 525 | 4.5 | 94 | 0 | 0.1 | Juan Tejeda |
| Jeff Keppinger | 269 | 4.2 | 85 | -3 | 0.1 | Jim Davenport |
| Andy Wilkins | 562 | 4.5 | 97 | -2 | 0.0 | Andy Marte |
| Adrian Nieto | 285 | 3.1 | 66 | -3 | 0.0 | David Ross |
| Michael Taylor | 522 | 3.9 | 82 | 0 | 0.0 | Derrick White |
| Christian Marrero | 337 | 3.9 | 84 | 2 | 0.0 | Michael Tullier |
| Juan Diaz | 520 | 3.1 | 62 | 0 | -0.2 | Greg Porter |
| Tony Campana | 436 | 3.0 | 55 | 5 | -0.2 | David Hulse |
| Tim Anderson | 491 | 3.3 | 69 | -5 | -0.4 | Chris Moritz |
| Dan Black | 494 | 4.0 | 85 | 0 | -0.4 | Paul Torres |
| J.B. Shuck | 512 | 3.7 | 76 | 1 | -0.5 | Ken Ramos |
| Brennan Boesch | 389 | 4.4 | 92 | -9 | -0.7 | Mark Saccomanno |
| Matt Tuiasosopo | 416 | 3.3 | 72 | -1 | -0.7 | Kurt Airoso |
| Courtney Hawkins | 495 | 3.1 | 69 | 3 | -0.7 | Keith Kimsey |
| Jared Mitchell | 484 | 2.9 | 64 | 3 | -0.9 | Tony Beal |
| Paul Konerko | 318 | 3.5 | 73 | -3 | -1.1 | Todd Zeile |
***
Pitchers, Counting Stats
| Player | T | Age | G | GS | IP | K | BB | HR | H | R | ER |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chris Sale | L | 26 | 28 | 28 | 189.7 | 209 | 44 | 18 | 161 | 65 | 61 |
| Jeff Samardzija | R | 30 | 30 | 30 | 194.0 | 197 | 52 | 24 | 187 | 90 | 84 |
| Jose Quintana | L | 26 | 32 | 32 | 190.0 | 151 | 54 | 17 | 193 | 88 | 82 |
| Carlos Rodon | L | 22 | 21 | 21 | 110.7 | 125 | 69 | 15 | 98 | 58 | 54 |
| David Robertson | R | 30 | 64 | 0 | 61.0 | 83 | 21 | 6 | 48 | 21 | 20 |
| Zach Putnam | R | 27 | 55 | 0 | 61.0 | 54 | 26 | 5 | 56 | 27 | 25 |
| Zach Duke | L | 32 | 61 | 0 | 53.3 | 59 | 18 | 6 | 49 | 24 | 22 |
| Dan Jennings | L | 28 | 59 | 0 | 58.0 | 56 | 23 | 6 | 57 | 27 | 25 |
| Nate Jones | R | 29 | 37 | 0 | 40.3 | 43 | 16 | 4 | 37 | 18 | 17 |
| Jake Petricka | R | 27 | 68 | 0 | 72.3 | 54 | 34 | 6 | 72 | 35 | 33 |
| Matt Lindstrom | R | 35 | 48 | 0 | 45.0 | 32 | 16 | 4 | 49 | 24 | 22 |
| Eric Surkamp | L | 27 | 38 | 13 | 92.0 | 74 | 33 | 15 | 100 | 56 | 52 |
| Ronald Belisario | R | 32 | 66 | 0 | 64.7 | 50 | 25 | 6 | 67 | 34 | 32 |
| Matt Zaleski | R | 33 | 13 | 8 | 48.3 | 33 | 18 | 7 | 56 | 32 | 30 |
| Javy Guerra | R | 29 | 49 | 0 | 56.7 | 44 | 26 | 6 | 59 | 32 | 30 |
| Daniel Webb | R | 25 | 52 | 0 | 64.3 | 54 | 41 | 6 | 63 | 36 | 34 |
| Michael Ynoa | R | 23 | 28 | 8 | 56.0 | 47 | 33 | 8 | 59 | 36 | 34 |
| John Danks | L | 30 | 23 | 23 | 133.7 | 84 | 51 | 23 | 151 | 87 | 81 |
| Joe Savery | L | 29 | 43 | 0 | 44.7 | 36 | 21 | 6 | 47 | 27 | 25 |
| Onelki Garcia | L | 25 | 21 | 5 | 32.0 | 28 | 27 | 4 | 33 | 22 | 21 |
| Scott Carroll | R | 30 | 24 | 15 | 94.0 | 50 | 39 | 13 | 110 | 62 | 58 |
| Felipe Paulino | R | 31 | 8 | 8 | 37.0 | 31 | 21 | 8 | 42 | 28 | 26 |
| Chris Beck | R | 24 | 26 | 26 | 139.3 | 70 | 49 | 21 | 167 | 92 | 86 |
| Erik Johnson | R | 25 | 24 | 24 | 120.3 | 82 | 60 | 17 | 135 | 80 | 75 |
| Henry Rodriguez | R | 28 | 31 | 0 | 31.3 | 37 | 39 | 4 | 27 | 22 | 21 |
| Raul Fernandez | R | 25 | 40 | 0 | 41.3 | 33 | 27 | 7 | 45 | 29 | 27 |
| Frankie Montas | R | 22 | 19 | 19 | 84.3 | 65 | 48 | 15 | 95 | 61 | 57 |
| Maikel Cleto | R | 26 | 47 | 3 | 70.3 | 71 | 54 | 12 | 71 | 49 | 46 |
| Hector Noesi | R | 28 | 30 | 22 | 138.7 | 94 | 52 | 27 | 160 | 96 | 90 |
| Tommy Hanson | R | 28 | 18 | 17 | 89.0 | 71 | 43 | 19 | 104 | 66 | 62 |
| Charlie Leesman | L | 28 | 21 | 18 | 86.3 | 66 | 55 | 16 | 101 | 66 | 62 |
| Daniel McCutchen | R | 32 | 25 | 8 | 66.3 | 46 | 25 | 19 | 84 | 56 | 52 |
***
Pitchers, Rates and Averages
| Player | IP | TBF | K% | BB% | BABIP | ERA | FIP | ERA- | FIP- |
|---|---|---|---|---|---|---|---|---|---|
| Chris Sale | 189.7 | 774 | 27.0% | 5.7% | .291 | 2.89 | 3.20 | 71 | 78 |
| Jeff Samardzija | 194.0 | 821 | 24.0% | 6.3% | .302 | 3.90 | 3.80 | 95 | 93 |
| Jose Quintana | 190.0 | 817 | 18.5% | 6.6% | .298 | 3.88 | 3.79 | 95 | 93 |
| Carlos Rodon | 110.7 | 499 | 25.0% | 13.8% | .294 | 4.39 | 4.93 | 107 | 121 |
| David Robertson | 61.0 | 252 | 32.9% | 8.3% | .298 | 2.95 | 2.94 | 72 | 72 |
| Zach Putnam | 61.0 | 265 | 20.4% | 9.8% | .287 | 3.69 | 3.92 | 90 | 96 |
| Zach Duke | 53.3 | 227 | 26.0% | 7.9% | .301 | 3.71 | 3.55 | 91 | 87 |
| Dan Jennings | 58.0 | 254 | 22.0% | 9.1% | .304 | 3.88 | 3.95 | 95 | 97 |
| Nate Jones | 40.3 | 174 | 24.7% | 9.2% | .300 | 3.79 | 3.69 | 93 | 90 |
| Jake Petricka | 72.3 | 323 | 16.7% | 10.5% | .291 | 4.11 | 4.26 | 101 | 104 |
| Matt Lindstrom | 45.0 | 200 | 16.0% | 8.0% | .308 | 4.40 | 4.21 | 108 | 103 |
| Eric Surkamp | 92.0 | 409 | 18.1% | 8.1% | .302 | 5.09 | 5.06 | 125 | 124 |
| Ronald Belisario | 64.7 | 286 | 17.5% | 8.7% | .303 | 4.45 | 4.03 | 109 | 98 |
| Matt Zaleski | 48.3 | 219 | 15.1% | 8.2% | .312 | 5.59 | 5.23 | 137 | 128 |
| Javy Guerra | 56.7 | 255 | 17.2% | 10.2% | .303 | 4.76 | 4.60 | 116 | 112 |
| Daniel Webb | 64.3 | 297 | 18.2% | 13.8% | .295 | 4.76 | 4.84 | 116 | 118 |
| Michael Ynoa | 56.0 | 260 | 18.1% | 12.7% | .302 | 5.46 | 5.40 | 134 | 132 |
| John Danks | 133.7 | 603 | 13.9% | 8.5% | .291 | 5.45 | 5.58 | 133 | 137 |
| Joe Savery | 44.7 | 202 | 17.8% | 10.4% | .299 | 5.04 | 4.96 | 123 | 121 |
| Onelki Garcia | 32.0 | 156 | 17.9% | 17.3% | .309 | 5.91 | 6.03 | 145 | 148 |
| Scott Carroll | 94.0 | 431 | 11.6% | 9.0% | .302 | 5.55 | 5.55 | 136 | 136 |
| Felipe Paulino | 37.0 | 174 | 17.8% | 12.1% | .304 | 6.32 | 6.35 | 155 | 155 |
| Chris Beck | 139.3 | 634 | 11.0% | 7.7% | .300 | 5.56 | 5.51 | 136 | 135 |
| Erik Johnson | 120.3 | 556 | 14.8% | 10.8% | .303 | 5.61 | 5.51 | 137 | 135 |
| Henry Rodriguez | 31.3 | 160 | 23.1% | 24.4% | .292 | 6.03 | 6.48 | 148 | 158 |
| Raul Fernandez | 41.3 | 196 | 16.8% | 13.8% | .302 | 5.88 | 6.13 | 144 | 150 |
| Frankie Montas | 84.3 | 396 | 16.4% | 12.1% | .305 | 6.08 | 6.04 | 149 | 148 |
| Maikel Cleto | 70.3 | 336 | 21.1% | 16.1% | .306 | 5.89 | 6.06 | 144 | 148 |
| Hector Noesi | 138.7 | 628 | 15.0% | 8.3% | .295 | 5.84 | 5.69 | 143 | 139 |
| Tommy Hanson | 89.0 | 414 | 17.1% | 10.4% | .307 | 6.27 | 6.04 | 153 | 148 |
| Charlie Leesman | 86.3 | 415 | 15.9% | 13.3% | .311 | 6.46 | 6.31 | 158 | 154 |
| Daniel McCutchen | 66.3 | 308 | 14.9% | 8.1% | .304 | 7.06 | 6.95 | 173 | 170 |
***
Pitchers, Assorted Other
| Player | IP | K/9 | BB/9 | HR/9 | ERA+ | zWAR | No. 1 Comp |
|---|---|---|---|---|---|---|---|
| Chris Sale | 189.7 | 9.92 | 2.09 | 0.85 | 142 | 5.8 | Tom Glavine |
| Jeff Samardzija | 194.0 | 9.14 | 2.41 | 1.11 | 105 | 3.4 | Pedro Astacio |
| Jose Quintana | 190.0 | 7.15 | 2.56 | 0.81 | 106 | 3.4 | Mark Thurmond |
| Carlos Rodon | 110.7 | 10.16 | 5.61 | 1.22 | 93 | 1.2 | Billy Wagner |
| David Robertson | 61.0 | 12.25 | 3.10 | 0.89 | 139 | 1.1 | Darren Holmes |
| Zach Putnam | 61.0 | 7.97 | 3.84 | 0.74 | 111 | 0.5 | Mark Acre |
| Zach Duke | 53.3 | 9.96 | 3.04 | 1.01 | 111 | 0.4 | Mike Stanton |
| Dan Jennings | 58.0 | 8.69 | 3.57 | 0.93 | 106 | 0.4 | Juan Agosto |
| Nate Jones | 40.3 | 9.60 | 3.57 | 0.89 | 108 | 0.3 | Jason Bulger |
| Jake Petricka | 72.3 | 6.72 | 4.23 | 0.75 | 100 | 0.2 | Horacio Pina |
| Matt Lindstrom | 45.0 | 6.40 | 3.20 | 0.80 | 93 | 0.0 | Fred Gladding |
| Eric Surkamp | 92.0 | 7.24 | 3.23 | 1.47 | 81 | -0.1 | Ryan O’Malley |
| Ronald Belisario | 64.7 | 6.96 | 3.48 | 0.83 | 92 | -0.1 | Marc Wilkins |
| Matt Zaleski | 48.3 | 6.15 | 3.35 | 1.30 | 73 | -0.3 | Chris Nichting |
| Javy Guerra | 56.7 | 6.98 | 4.13 | 0.95 | 86 | -0.3 | Jake Robbins |
| Daniel Webb | 64.3 | 7.56 | 5.74 | 0.84 | 86 | -0.3 | Heathcliff Slocumb |
| Michael Ynoa | 56.0 | 7.55 | 5.30 | 1.29 | 75 | -0.4 | Glendon Rusch |
| John Danks | 133.7 | 5.65 | 3.43 | 1.55 | 75 | -0.4 | Don Collins |
| Joe Savery | 44.7 | 7.25 | 4.23 | 1.21 | 81 | -0.4 | Ken Vining |
| Onelki Garcia | 32.0 | 7.88 | 7.59 | 1.13 | 70 | -0.4 | Bob Weiland |
| Scott Carroll | 94.0 | 4.79 | 3.73 | 1.24 | 74 | -0.5 | Rick Rodriguez |
| Felipe Paulino | 37.0 | 7.54 | 5.11 | 1.95 | 65 | -0.5 | Charlie Puleo |
| Chris Beck | 139.3 | 4.52 | 3.17 | 1.36 | 74 | -0.6 | Luis Mendoza |
| Erik Johnson | 120.3 | 6.13 | 4.49 | 1.27 | 73 | -0.6 | Brian Moehler |
| Henry Rodriguez | 31.3 | 10.64 | 11.21 | 1.15 | 68 | -0.7 | Eric Cammack |
| Raul Fernandez | 41.3 | 7.19 | 5.88 | 1.53 | 70 | -0.8 | Brad Tweedlie |
| Frankie Montas | 84.3 | 6.94 | 5.12 | 1.60 | 67 | -0.9 | Matt Goodson |
| Maikel Cleto | 70.3 | 9.09 | 6.91 | 1.54 | 70 | -1.2 | Orlando Roman |
| Hector Noesi | 138.7 | 6.10 | 3.37 | 1.75 | 70 | -1.2 | Rusty Richards |
| Tommy Hanson | 89.0 | 7.18 | 4.35 | 1.92 | 65 | -1.2 | Giovanni Carrara |
| Charlie Leesman | 86.3 | 6.88 | 5.74 | 1.67 | 64 | -1.4 | Steve Smyth |
| Daniel McCutchen | 66.3 | 6.24 | 3.39 | 2.58 | 58 | -1.8 | Jeff Harris |
***
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 2014. 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 3.93 ERA and the NL having a 3.75 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.

Little Miggy at 0 WAR?
Was surprised/disappointed to see that, too
Well, well, well. It appears even ZiPS is in on the grand sabermetric conspiracy against the white sox.
Hopefully some learned scholars of the eye test will show up with some anecdotes and conclusory statements proving wrong all of this mathy nonsense.
Maybe you should reserve the right to be a condescending ass, and not exercise it preemptively before anybody has actually said anything.
You you got on Arc for posting a preemptive response 4 minutes before you posted the very thing he was criticizing. ZiPS and Steamer HATE the White Sox. Get over it. If you want to question the methodology, ask Szymborski (not the way you did below). He is usually very responsive.
That’s what makes it hilarious.
They don’t hate the White Sox either, for a couple reasons.
#1, they view them as an improved team that is in the .500 range.
#2, it’s a projection system, not a human being. It’s incapable of feelings.
Where did I allege the projection systems are a conspiracy? Where did I rely on “the eye test” in place of objective statistics? What I posted was decidedly not what he presupposed, and I guess I should be sorry for not refreshing the main article, which took more than four minutes to read, before posting a comment?
Don’t have twitter – hate the medium – but will hit him up on his gmail, hopefully he’ll give some insight. Good idea.
What’s wrong with his comment below? Looks pretty reasonable and nuanced to me.
I’m with Arc on this one.
Why do you take this so personally? You’re making us White Sox fans look like fucking meatball Bears fans. Please stop.
Couldn’t agree more. This means nothing.
Now “Power Rankings” on the other hand…
You mean, fangraphs undervalued the White Sox? Shocking! The comical thing about your comment is that you feel that people are overrating and valuing the White Sox based on their signing, when in reality fangraphs has a history of underrating/valuing the White Sox. So much so that fangraphs has written a piece on about it:
http://www.fangraphs.com/blogs/the-white-sox-and-beating-projections/
The White Sox have continually beat projections; nearly every year – even last year the bad White Sox beat projections despite blowing 21 saves and in general, being awful.
So fangraphs has acknowledged that the White Sox play above their projections over an extended period of time, but here you are…. talking about how wrong people are who believe the White Sox are underprojected.
The White Sox blew 21 saves last year; their bullpen was appalling. I’d love to see what the White Sox run differential was with their starters in the game. If the White Sox blow 11 games last year, around league average, they win 84 games. They have improved on their biggest weaknesses significantly.
ZIPS and Steamer have consistently undervalued the White Sox. That won’t stop this year. Fangraphs has accepted that, but apparently you have not. White Sox over/under win total will be set around 84 wins by sportsbooks; that’s a much better indicator of wins.
If the White Sox were a better team they would have won more games. Powerful insight from the rational [biased] fan. With 30 trials (teams) over 7 years, we would expect a team to beat their projections like the white sox have. I’ll lay 30-1 that they aren’t going to do the same over the next 7 years though.
Los, fangraphs has already addressed the fact that their is a good possibility that Herm Schneider and Don Cooper have effected White Sox projections. They’ve also accepted the fact that it’s a good possibility that the White Sox will continue to out perform expectations as long as those two are their; because they have proven to have an ability to keep pitchers healthier than the league average.
Clearly you didn’t even bother to read the article I posted, so I won’t waste anymore time on this subject with you. You can continue to be disproved. Sportsbooks W/L over unders are a much better basis of win expectations than ZIPS/Steamer and the likes. Sorry if that hurts your feelings.
Also, if they were a better team? Huh. The Sox bullpen was poor last year, but bullpens are extremely volatile as is from year to year – even with the same group of guys. If a bullpen is the main factor that held a team back, then it’s something that can change drastically the next season – even with minimal changes to the unit. The sample size alone for RP’s is so small that variance is greater with bullpens than any other position in baseball.
Also, there’s a reason for variance in life that isn’t simply attributable to “well, that’s how math works.” Fact of the matter is that there are reasons for success that aren’t quantifiable. Just as being clutch DOES exist, it’s simply not quantifiable beforehand so it’s irrelevant when evaluating a team or player.
The White Sox, PROBABLY, have a reason for their variance and probably have created their own variance. Just because you can’t quantify it, does not mean it didn’t happen. I’m about as involved in all metrics in baseball as you can be; I also understand there are other non-quantifiable variables in sports that do matter despite not being predictive.
I read the article when it was first run and I read it again yesterday. The fact that bullpens are highly volatile doesn’t mean that having good pitchers is a bad thing. Their bullpen is improved this year for sure but find me one person who thought the White Sox had anything but a bottom 5 bullpen last year.
There MAY be a signal with the White Sox. It MAY be noise. When random variation tells me that I should expect a team to do exactly what the white sox have done, I will tend to believe that this is simply noise. I love Don Cooper as a pitching coach and I am open to start believing more if the trend continues, but the fact remains that the White Sox pitching in incredible thin after the first 3 (unless Rodon is on the staff quickly)
No way the Sox O/U is 84. If it is though you can be sure I’m taking the U
ZIPS does not hate the White Sox. It does not care.
Steamer does not hate the White Sox. It does not care.
Pecota does not hate the White Sox. It does not care.
None of them are missing some magical grittiness that only a real fan can see. Statistical models regress towards the mean. It’s the way it is.
Most of all, Fangraphs does not hate the White Sox. They love Sale and Abreu. They have loved the last month. Those things are exciting. The rest of the team is still somewhere between “bad” and “hopefully good.”
Hell, Jeff has written so many articles about the Sox the last year or so I’m starting to believe he bought season tickets.
Am I the only one who assumes that arc is trolling?
Poe’s law…
Can someone explain to me why Steamer and ZiPS see only 2.4 and 3.4 WAR out of Quintana, respectively? Maybe he’s not 5.3, like last season, but he posted better numbers across the board – era estimators, k rate, walk rate – and while his HR% is due for regression, that should be offset to an extent by his poor luck on batted balls and strand rate last season. He’ll be in his age 26 season, and improved substantially on a 3.7 WAR 2013.
I don’t want this to be another flame-war comment like the one on the “White Sox Aren’t Very Good” article from yesterday, as I don’t think they’re appreciably better than .500, but it seems to me that there’s no good justification for the Quintana protections from either Steamer or ZiPS. Can someone with more familiarity with the systems clue me in on what inputs are driving these low figures? Cos from my layperson’s perspective, I don’t see any good reason to believe that Quintana won’t repeat a top 10 performance.
Szymborski runs ZiPS. Hit him up on twitter.
It’s the HR rate thing. It was well below league average and half of his career rate.
To ham-fistedly flesh out: if you double his HR number from last year (given that his HR/FB rate was half his career rate and a tick higher than half league average) and distribute it among 1-run, 2-run, and 3-run HRs at a 50%, 40%, 10% clip respectively, his last season ERA clocks in at 4.05. Against that, ZiPS and Steamer see Quintana due for an improvement. That makes sense, because as Chris observed, he’s both likely to improve as such and have better batted ball/LOB% luck.
Just nitpicking, could you explain why you left grand slams out of that?
Haha add in 3% for grand slams if you’d like (or whatever the league average is). Also, for what it’s worth, xFIP normalizes for HR rate in addition to things like BABIP — Quintana’s was 3.37.
Also, I don’t think you can just add in another 10-12 HR and distribute them like that because it assume those 10-12 HR were all previously outs that didn’t score runs. What if some of them were wallball doubles, sac flies, gappers, etc.? They’d still affect his ERA, but they wouldn’t add the 16 runs you’re adding. They may have only added 7 or 8 (purely guessing).
His LOB% was much worse than his career average, and given the upgrade in the bullpen his LOB% should revert even more-so to the mean. The LOB% cancels out the HR numbers.
Fact is, Quintana will be undervalued by Steamer for the 3rd year in a row. Similarly to how Mark Buehrle was undervalued.
Steamer puts everyone on an even basis; meaning the likelihood of injuries is the same for every team… when in reality, the White Sox have shown an ability to keep starters healthy better than any other team. This means his projections will almost always be lesser than reality.
Quintana is the most underrated player in baseball.
I believe the projections bake in some possibility for injury, no? A pitcher is more likely than a position player to get hurt in spring training and never play a game, so all pitcher’s projections get pulled down a bit. I could be wrong about that though.
Response from Szymborski:
A few things at work there. The asymmetrical risk curve of
above-average pitchers is important. The homer rate is really hard to
keep at that level for any fly ball pitcher – not only do you expect
it to go up quite a bit, but the park gives him significant HR
downside, which makes the negative scenarios worse. A lesser factor
is his velocity, which is very hard to sustain a high K rate at those levels.
Thanks for posting here. Helpful
Yes, he proves again why he favors hard throwers. Steamer under ranked Mark Buehrle 14 of 15 years (something along those lines), and yet he never adjusted. Ever. Steamer fails to accept that there are pitchers who don’t need to throw hard to miss bats.
Also, Quintana has seen a spike in his velocity each of the past three years.
Fact is, this will be the 3rd year in a row that Steamer’s ERA projection for Quintana is higher than his career ERA. He’s now accumulated over 500 big league innings. At some point the projections for someone like Quintana just need to be discarded before the season, because the factors that go into it are weighted against him as is proof by his actual production.
500 innings is not enough to trust ERA over FIP.
I remember when people were saying similar things about Jeremy Hellickson. Some players are able to maintain ERA’s well below their projections, but they’re few and far between (and almost impossible to differentiate from those who have just had multiple consecutive years of good luck). And there’s no evidence of systematic bias against low-velocity pitchers.
Well, his career FIP is nearly identical to his career ERA so….. Not sure why that point even matters. Career FIP of 3.55, career ERA of 3.5. FIP and ERA have each gone down the past three years… yet Steamer now projects him to get worse.
Tinker, pitch to contact pitchers have a bias against them in Steamer/ZIPS and the like. It’s just a fact. If you’re great at keeping hitters off balance, and pitching to contact, your projections will undervalue you every single year. Look no further than Mark Buehrle as I said.
I wasn’t referring to Hellickson’s E-F or E-X differentials, just that projection systems continually projected regression while his proponents claimed he was good at inducing soft contact. Sorry, I should have been more clear with that example.
Guys like Buehrle and Glavine are great examples of where projection systems fail, but they are also anomalies. If there was systematic bias against “pitch to contact” pitchers than that bias would be easy to correct. However, the problem is projections systems cannot differentiate from guys who are trying to induce weak contact and guys that just aren’t very good at missing bats. It is very possible Quintana is one of the former group, but I don’t think we can make that conclusion (yet).
Fair enough Tink, and I tend to agree. In general, the projections are solid and within 1 standard deviation on 98% of it’s predictions. That said, people always want to assume that the outliers are just that – outliers. When in reality, there is probably a reason for them becoming outliers.
Despite it being only one year, Quintana may have learned a lot from Buehrle in regards to pitching pace, and sequence. The added velocity for Quintana last year certainly didn’t hurt.
If there were lines up on such, I would bet A LOT of money that Quintana beats his ZIP/Steamer projections again this year. He’s only improved since he came into the league, not the other way around.
It’s the same way I feel about Abreu; I saw him adjust to the league last year, while the league adjusted to him, and he only got better. Is it possible that he regresses and takes a step back? Absolutely, but based on the evidence I have of his progression last year, I wouldn’t bet on it.
That Kenny Lofton comp on Eaton looks nice. Chris Sale’s top comp is Tom Glavine. You can put that on a list of things that have never been said before.
How are the comps generated for this? Because that is a super random and not good one it seems.
The only thing Glavine and Sale have in common are batted ball rate stats. I can only assume that those are pretty heavily utilized in the comps.
At age 25 Tom Glavine was a lot more like Chris Sale than you would think.
Strikeouts tho
At age 25, Tom Glavine was 3rd in the NL in strikeouts.
Yeah, but with a K/BB just over half of Sale’s.
In the 90s, when strikeouts were less common. If you wanted a K/BB comp, you’d need to select from the pool of active players.
Again, the difference is not at big if you look at how they respectively compare to league averages. K/BB ratios were were noticeably worse league wide in 1991.
I’m curious how ZiPS projects over 100 more PAs from Kevan Smith than from Tyler Flowers
Also, I hope Zaleski cracks the Majors this year…dude’s been a farmhand for nearly a decade with the same Org…I’m hoping he gets a couple garbage time innings during September.
ZiPS is agnostic toward playing time.
please don’t say Heathcliff Slocumb
ever
Abreu is more Tim Horton than Willie Horton imo
Hawk Harrelson might have a coronary if he sees this, can we get someone to show it to him please? (can’t stand that guy)
I love me some Hawk, but we both know he’d have no idea how to read anything on these tables
*he’d have no idea how to read anything
FTFY
If there was a column labeled, “T-Dubya T-Dubya,” he would.
One can explain the divergence simply by assuming Dave and Jeff did the data entry for both ZiPS and Steamer, while Cistulli was manning the wheel of the getaway car.
The perfect crime, almost too perfect…
Paulie with those 300 PA, hehe
Just a quick question:
Does Steamer and/or ZIPS take park effects into account when calculating their WAR figures?
It seems fairly odd to me that C.Sale ist projected by ZIPS to post a FIP of 3.20 in 190 IP and arrive at 5.8 zWAR.
Steamer projects a FIP of 3.07 in 192 IP and arrives at only 4.7 sWAR.
ZIPS: 190 IP, 3.20 FIP, 5.8 WAR
Steamer: 192 IP, 3.07 FIP, 4.7 WAR
??????
It’s my understanding that ZiPS uses defense-adjusted RA9 WAR.
I think the ZiPS WAR is being calculated from ERA, with adjustments for the park and defence. ZiPS appears to believe Sale has some ability to beat his FIP, so his WAR is higher than it would be with a FIP-based WAR.
I also think there’s an issue with the WAR calculations based on Steamer projections that leads to pitchers being under-rated, in Sales’ case by maybe 0.5 WAR. Going by the depth charts Steamer is projecting the average AL starter to have a 4.06 FIP compared to the actual figure of 3.85 in 2014. I think the WAR calculations are using the latter, so they treat Sale as a bit closer to average than Steamer really rates him.
That bullpen pegged for 2 WAR did put up 4.7 WAR last year.
http://www.fangraphs.com/blogs/team-projections-and-last-seasons-statistics/
Thank you, I hadn’t seen this article. My point was not just that there’s a difference, but that there’s a big difference. It seems drastic. It seems for the bullpen to only put up 2 WAR, pretty much everyone would have to flop a lot. Something I’d love to see Fangraphs and other sites do is show the differences between their projections and what How predictive and therefore how useful are these projections. Are they better or worse for particular player types?
**should say “differences between their projections, and what actually happens.
Essentially the Sox via FA added around 4-5 wins for around 40$M (Melky 2WAR, LaRoche 2WAR, Robertson/Duke 1.5ish WAR).
That’s actually a littler better value than I thought they got.
Paul Konerko’s 318 ABs are going to put this team over the top.
These projection systems are also not saying that the White Sox cannot be a good team. They’re saying that, with the information we have on hand, this is roughly what we can expect. There’s no need to take these things personally. The 2014 Orioles were projected for about 32 zWAR, essentially got lost seasons from 3 of their top 5 projected players, and, with luck and good signings and fortunate breaks and guys stepping up and having big seasons out of nowhere and any other number of other factors, still won 96 games. Occasionally, these things happen. It could happen with the White Sox. These projection systems are in place to say that, if the White Sox do only end up as an approximately .500 team, we should not be surprised.
Where’s the chart for TWTW? “The will to win” is the only stat that you need.
2005 called, asking you to cease and desist use of its copyrighted material.
2005’s lawyer can expect a call from 2013 asking it to stop making false claims of ownership.
So says the commenter deceptively holding him/herself out as a representative of 2013. Ryan has my direct line; he wouldn’t just post something here.
I don’t really see anything in these projections to really say the white sox are being under projected like some sox fans were saying a couple days earlier. After seeing what people have to say about the team with the article a few days ago and these projections I really think a key for this team is avisail Garcia. The team keeps insisting he’s part of the core but projections don’t see it that way and for good reason as he has been a high strikeout low walk meh defense when he’s had time. If he’s more what the white sox see in him than what the projections see in him then that would go a long way to getting them in the playoffs
Pedro Astacio as the comp for Samardzija suprised me, until looking at his player page.
http://www.fangraphs.com/statss.aspx?playerid=862&position=P
In 1999, he accumulated 5.3 WAR with an ERA over 5. It’s amazing what pre-humidor Colorado did to pitchers.
If you replace Noesi in the 5th slot with Rodon (assuming he’s between 1-2 WAR; which seems reasonable given his projected numbers), the numbers on Cistuli’s depth chart here add up to 31-32 WAR, which is 3-4 higher than the Steamer projection.
Also worth considering amidst the angst over the projection systems’ ‘feelings’ towards the white sox is this article from 2013:
http://www.fangraphs.com/blogs/the-white-sox-and-beating-projections/
Projection systems have traditionally undersold the white sox’ fortunes because the the team has been consistently better at keeping pitchers healthy than its competitors. This is something projection systems obviously wouldn’t pick up on, and possibly provides some more room for optimism for white sox fans than the projection systems foresee.
Michael Ynoa is the next Glendon Rusch? I would not have guessed that.
The Carlos Rodon/Billy Wagner comp is not working for me visually. It’s nothing that the right pill wouldn’t fix.
I don’t see Noesi being a -1 WAR pitcher. His WHIP splits from last year suggest that he may be a solid back-of-the-rotation guy.
Mar/Apr: 1.94
May: 1.35
Jun: 1.47
Jul: 1.44
Aug: 1.11
Sep: 1.28
Him getting better during the “dead arm” period of Aug/Sep is encouraging.
Not sure WHIP is the best way to evaluate him, because he’s crazy home-run prone.
HIs ERA followed the same trend, and he ended his year in Chicago with an ERA+ of 88. Not bad for a #5, especially considering that he got better over the course of the season.
Of course, he could regress, but I like his chances under Don Cooper. I actually like Noesi more at this point than Danks.
Of course it’s tough to do worse than the April-July period and stay gainfully employed in the majors for long…
I love these metrics. Just like when they say there good on paper, you still got to play the games. These should only be used as a guide line for acquiring new players. Not to predict a teams win total. The best team does not always win the ring. All they have to do is compete for a spot in the playoffs. Which the additions the White Sox made helped increase their odds of doing. Lets see them play. All it takes is one or two players to click and they are right their in the end. As the term goes Lightning in a bottle. It is hard to get a real measurement for a player like Avisail Garcia because of his limited playing time and rushing back last year. I look forward to an entire year of his services. But door flies both ways, what if Abreu falls back similar to Cespedes after his rookie season. It should be a fun year. Go White Sox.
I enjoy people doubting the White SOx. I know if they succeed people will not eat crow, that is not the point, as variance and stuff. I just enjoy teams that are “supposed” mediocre defying projections. I moved from Chicago to SF, so there’s that.
Maybe if Brennan Boesch can elevate his play to, say, Bob Sacamano, he can elevate the Sox a bit. Still can’t remember when they signed him . . hopefully, I am also forgetting that they already traded Viciedo.