After a Bad 2018, Cody Allen Heads to Angels

After he signed with the Yankees, Adam Ottavino became the ninth reliever on our Top 50 Free Agent list to get a contract for next season. The Yankees taking Ottavino off the board meant there were just two relievers to go. One is Craig Kimbrel, who has been one of the better relievers in baseball over the last half-dozen seasons. The other is Cody Allen, who was one of the better relievers in baseball in 2015, solid in 2016 and 2017, and not very good last year. His poor 2018 season showing plunged him down our rankings and left him as one of the less desirable proven-reliever types available this offseason. His track record did mean something, though, and per Ken Rosenthal, he’s landed a one-year, $8.5 million deal with the Angels that has the chance of being worth $11 million based on games finished.

Allen, picked in the 23rd round of the 2010 draft, moved quickly through the Cleveland system as a reliever, reaching Double-A a year after he was drafted and hitting the majors one year later. He was a good reliever in 2013 and 2014, with sub-3.00 FIPs and ERAs better than that. He took over the closer role in 2014 and had his best season the following year, striking out 35% of batters, walking 9% and giving up just two home runs all season, to go along with a 15% infield fly rate. When Cleveland acquired Andrew Miller in 2015, the club could afford to put the lefty in high leverage situations in the middle of games without worrying about the ninth because Allen was closing. He didn’t give up a run during their playoff run to the World Series and struck out 24 of the 55 batters he faced.

Allen had another solid season in 2017, though not as good as his 2015 peak due to a slight decline in strikeouts and an increase in homers. In 2018, Allen started off the first two months of the season pitching much like he had his prior two years. His strikeout rate had dipped to 25%, but his walk rate was good and he only gave up two homers on his way to a 3.54 FIP and 3.00 ERA. He wasn’t great, but he was getting the job done. From June to the end of the season, his strikeout rate was up at 29%, but his walk rate went up to 13% and his home run rate more than doubled. He had a 5.14 FIP and 5.65 ERA the last four months of the season, leading to an overall replacement-level campaign. In the playoffs, he faced nine batters and retired just three of them. Read the rest of this entry »


Jeff Sullivan FanGraphs Chat — 1/18/19

9:04

Jeff Sullivan: Hello friends

9:04

Jeff Sullivan: Welcome to Friday baseball chat

9:05

Mookie Butts: Why are the Yankees trying to win so much?  It feels like a personal attack.

9:05

Jeff Sullivan: It should feel like a personal attack

9:05

Jeff Sullivan: Do you remember what happened in 2018

9:06

Jeff Sullivan: I don’t think the Yankees liked that very much

Read the rest of this entry »


2019 ZiPS Projections – St. Louis Cardinals

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for more than half a decade. The exercise continues this offseason. Below are the projections for the St. Louis Cardinals.

Batters

Yeah, there’s a Paul Goldschmidt on the roster now, but the thing that jumps out at me the most is just how deep the Cardinals’ bench is. You essentially have a spare league-averageish right fielder (ZiPS sees Dexter Fowler bouncing back to a degree) and an above-average spare infielder in Jedd Gyorko, so long as you don’t get the idea that he should be playing shortstop. ZiPS gives 10 two-WAR projections to St. Louis. Quite obviously, the Cardinals won’t actually have that many two-win players, simply because there aren’t enough at-bats for all of them to hit that threshold. Even among the fringe minor leaguers — like Rangel Ravelo, who ZiPS never really cared much for with the A’s or White Sox — there are a lot of players who, while not actually projected to be viable starters, wouldn’t be disastrous fill-in candidates.

As a thought exercise, imagine that St. Louis’s starting lineup comes down with some violent illness that involves projectile vomit (gross) and 180 days of bed rest. Such maladies would leave St. Louis with a lineup looking like this:

Cardinals Outbreak Lineup
Position Player(s)
C Andrew Knizner/Jose Godoy
1B Rangel Ravelo
2B Ramon Urias
SS Tommy Edman
3B Jedd Gyorko
LF Tyler O’Neill
CF Lane Thomas/Drew Robinson
RF Justin Williams

Even in this absolutely absurd scenario — with this many players injured so severely, and the Cards content to stand pat, and not make any moves to compensate — the lineup still projects to be worth 14 WAR given assumed full-season playing time. That’s more or less what Kansas City’s projected starters are pegged for if everyone’s healthy (I’m picking on the Royals simply because I just wrote them up and had them handy; I could have chosen other dreadful teams as well). Using the WAR Add ’em Up technique that you should never, ever use, the outbreak lineup would still leave the Cardinals with an 80-win team.

Pitchers

Here you can see the consequences of the Paul Goldschmidt trade in terms of the team’s pitching depth. Luke Weaver wasn’t a star, but he was also an extra arm at the back-end of the rotation, one that will be needed because Carlos Martinez, Alex Reyes, Michael Wacha, and Adam Wainwright have all missed significant time recently due to injury (and with Waino, there’s a quality concern). That isn’t to say the Cardinals shouldn’t have made the Goldschmidt trade – he’s a giant short-term addition to the offense and the domino effect gives the team additional depth. It simply means that St. Louis ought to address their pitching issue over the rest of the offseason. Now, they don’t need to convince the Mets to trade them Jacob deGrom; a move of that magnitude isn’t necessary, though it would certainly be nice. But a No. 3 or 4 starter who can eat some innings would be good. J.A. Happ or a returning Lance Lynn would have been ideal for this, but Gio Gonzalez remains available. It’s weird to think about, but Mike Leake actually would be quite useful right now.

With the team apparently not spending money on Manny Machado or Bryce Harper (though I guess that still isn’t certain), they ought to be going after Dallas Keuchel. Yes, there’s a risk of over-engineering your rotation and ending up with too many starting pitchers, but has that ever truly been a problem for any team in baseball history? The Astros figured out what to do with their extra starters just last year. Serious, contending teams ought to be more open to depth of this kind and avoid getting too hung up on efficiency.

Bench and Prospects

Dagnabit, I already talked about the bench quite a bit up top, so I kind of broke the rules that I’m in no way obligated to follow, so nyah nyah nyah nyah nyah nyah, Carson!

The top of the minors has a lot of players who look like they will be useful role players, but outside of possibly Alex Reyes, who would fall out of the prospect list with just an additional out, the system’s largely missing that zing, zazz, zork, kapowza, the mazuma in the bank. Kiley and Eric only give eight players in the farm system a future value above 40 and ZiPS doesn’t offer a ton of disagreement. ZiPS does like Elehuris Montero’s power potential (so does McDongenhagen), but his defense is a worry, and based on what rudimentary minor league data is available, ZiPS is a bit concerned as well. If he is a -6 right now, it may be enough to require a move off of third by the time he’s 25, meaning he’ll need another bump in his offense to avoid becoming a tweener.

One pedantic note for 2019: for the WAR graphic, I’m using FanGraphs’ depth chart playing time, not the playing time ZiPS spits out, so there will be occasional differences in WAR totals.

Ballpark graphic courtesy Eephus League. Depth charts constructed by way of those listed here at site.

Batters – Counting Stats
Player B Age PO G AB R H 2B 3B HR RBI BB SO SB CS
Paul Goldschmidt R 31 1B 149 549 91 148 28 3 27 89 93 160 13 4
Matt Carpenter L 33 3B 138 496 90 125 33 2 26 75 90 141 3 2
Marcell Ozuna R 28 LF 153 589 74 164 27 3 24 97 44 126 2 2
Paul DeJong R 25 SS 132 503 68 124 26 2 22 77 36 151 2 2
Yadier Molina R 36 C 122 449 46 119 22 1 12 66 27 65 4 3
Harrison Bader R 25 CF 140 446 62 108 19 3 15 50 33 146 14 7
Jedd Gyorko R 30 3B 124 386 47 98 16 1 16 58 40 90 2 1
Tyler O’Neill R 24 LF 130 452 74 114 20 2 29 83 39 157 7 1
Kolten Wong L 28 2B 124 379 49 97 20 3 9 44 35 69 8 4
Jose Martinez R 30 RF 144 480 61 137 26 1 15 71 43 93 3 2
Andrew Knizner R 24 C 95 351 40 91 16 1 6 35 22 59 0 1
Rangel Ravelo R 27 1B 101 358 48 96 21 2 10 48 31 63 1 1
Ramon Urias R 25 2B 98 335 46 86 19 2 10 43 24 77 3 4
Dexter Fowler R 33 RF 104 370 58 88 17 4 11 44 52 99 8 3
Tommy Edman B 24 SS 122 493 56 121 20 5 5 41 36 96 17 5
Lane Thomas R 23 CF 127 498 59 117 18 7 14 59 39 149 12 11
John Nogowski R 26 1B 89 325 39 87 12 0 5 31 30 39 1 1
Wilfredo Tovar R 27 SS 116 398 41 99 17 2 4 34 23 58 15 7
Evan Mendoza R 23 3B 129 507 52 124 20 3 8 44 30 119 3 2
Yairo Munoz R 24 SS 129 435 51 110 21 2 11 55 29 99 9 6
Max Schrock L 24 2B 112 448 49 113 17 1 7 39 28 61 3 2
Jose Godoy L 24 C 81 276 28 63 11 1 3 23 22 55 1 1
Elehuris Montero R 20 3B 127 480 58 115 27 3 14 58 32 126 2 1
Justin Williams L 23 RF 119 453 52 116 22 1 12 55 27 99 4 4
Drew Robinson L 27 CF 109 380 50 77 16 3 15 45 44 156 8 6
Jeremy Martinez R 24 C 65 211 22 46 8 0 2 16 19 35 1 0
Edmundo Sosa R 23 SS 126 463 48 109 23 2 8 42 21 102 6 4
Chase Pinder R 23 CF 91 327 38 70 12 2 4 27 39 91 3 6
Joe Hudson R 28 C 64 205 20 40 11 0 4 18 19 61 0 0
Adolis Garcia R 26 RF 125 451 56 107 23 2 16 61 22 119 10 7
Dylan Carlson B 20 RF 120 448 57 97 20 3 12 50 50 131 6 5
Francisco Pena R 29 C 65 198 20 46 9 0 4 19 9 47 1 0
Alex Mejia R 28 SS 115 368 38 90 14 1 4 31 20 68 4 2
Randy Arozarena R 24 LF 118 402 50 93 21 2 9 42 31 105 18 8
Stefan Trosclair R 24 1B 112 409 46 86 14 3 11 43 32 140 5 4
Johan Mieses R 23 RF 125 465 50 91 17 2 17 54 29 158 2 0
Conner Capel L 22 CF 123 471 53 105 20 4 10 46 39 122 12 13
Victor Roache R 27 LF 110 384 36 67 13 2 11 38 31 174 3 1

Batters – Rate Stats
Player BA OBP SLG OPS+ ISO BABIP RC/27 Def WAR No. 1 Comp
Paul Goldschmidt .270 .379 .479 130 .209 .334 6.8 4 4.4 Kevin Youkilis
Matt Carpenter .252 .371 .484 129 .232 .301 6.5 -3 4.2 Eddie Mathews
Marcell Ozuna .278 .330 .457 110 .178 .319 5.6 4 3.0 Rick Reichardt
Paul DeJong .247 .306 .437 98 .191 .309 4.8 0 2.5 Brook Jacoby
Yadier Molina .265 .310 .399 90 .134 .288 4.5 5 2.3 Paul Lo Duca
Harrison Bader .242 .307 .399 89 .157 .326 4.4 8 2.2 Mark Whiten
Jedd Gyorko .254 .324 .425 100 .171 .293 5.0 4 2.2 Tim Naehring
Tyler O’Neill .252 .315 .498 115 .246 .320 5.8 -3 2.1 Jesse Barfield
Kolten Wong .256 .336 .396 97 .140 .292 4.8 4 2.1 Rob Wilfong
Jose Martinez .285 .345 .438 110 .152 .328 5.7 -2 2.0 Ollie Brown
Andrew Knizner .259 .312 .362 81 .103 .297 4.0 2 1.3 Joe Azcue
Rangel Ravelo .268 .334 .422 103 .154 .302 5.2 1 1.2 Mike Brown
Ramon Urias .257 .327 .415 99 .158 .306 4.8 -4 1.2 Brendan Harris
Dexter Fowler .238 .337 .395 97 .157 .296 4.8 -1 1.1 Michael Tucker
Tommy Edman .245 .298 .337 71 .091 .296 3.7 2 1.0 Kurt Stillwell
Lane Thomas .235 .293 .384 81 .149 .307 3.8 2 1.0 Xavier Paul
John Nogowski .268 .334 .351 86 .083 .292 4.3 6 0.9 Mike Eylward
Wilfredo Tovar .249 .292 .332 68 .083 .283 3.5 4 0.8 Alex Prieto
Evan Mendoza .245 .290 .343 70 .099 .305 3.5 6 0.7 Aurelio Rodriguez
Yairo Munoz .253 .304 .386 85 .133 .305 4.2 -5 0.7 Jose Castro
Max Schrock .252 .301 .342 73 .089 .279 3.7 1 0.6 Jack Brohamer
Jose Godoy .228 .296 .308 64 .080 .275 3.2 3 0.5 Tom Wieghaus
Elehuris Montero .240 .294 .396 84 .156 .297 4.1 -6 0.5 Jeff Hamilton
Justin Williams .256 .302 .389 85 .132 .304 4.2 1 0.4 Andre Ethier
Drew Robinson .203 .286 .379 78 .176 .297 3.6 -1 0.4 Jon VanEvery
Jeremy Martinez .218 .286 .284 55 .066 .253 2.9 4 0.4 Mike Nickeas
Edmundo Sosa .235 .274 .346 66 .110 .286 3.3 2 0.3 Dean DeCillis
Chase Pinder .214 .305 .300 64 .086 .284 2.9 4 0.3 David Howell
Joe Hudson .195 .267 .307 55 .112 .257 2.7 4 0.3 Tom Nieto
Adolis Garcia .237 .277 .404 81 .166 .288 3.9 2 0.2 Ken Ford
Dylan Carlson .217 .302 .355 77 .138 .279 3.6 2 0.2 Kurt Bierek
Francisco Pena .232 .268 .338 62 .106 .286 3.2 0 0.0 Mike DiFelice
Alex Mejia .245 .287 .321 64 .076 .291 3.3 -2 -0.1 Ray Olmedo
Randy Arozarena .231 .304 .361 79 .129 .292 3.9 -3 -0.1 Jordan Parraz
Stefan Trosclair .210 .281 .340 67 .130 .291 3.2 6 -0.3 Rich Murray
Johan Mieses .196 .250 .351 60 .155 .255 3.0 6 -0.6 John Lindsey
Conner Capel .223 .284 .346 69 .123 .280 3.1 -5 -0.7 Karl Herren
Victor Roache .174 .241 .305 46 .130 .281 2.4 4 -1.3 Nick Wilfong

Pitchers – Counting Stats
Player T Age W L ERA G GS IP H ER HR BB SO
Carlos Martinez R 27 12 9 3.53 29 29 168.3 153 66 15 68 163
Miles Mikolas R 30 12 8 3.59 29 29 175.7 177 70 18 38 135
Jack Flaherty R 23 12 9 3.62 32 32 169.0 146 68 22 59 192
Daniel Poncedeleon R 27 8 7 4.15 28 22 119.3 112 55 12 61 109
Dakota Hudson R 24 11 11 4.32 44 23 150.0 153 72 13 64 99
Andrew Miller L 34 4 2 2.77 49 0 48.7 37 15 4 17 65
Michael Wacha R 27 8 7 4.26 23 22 120.3 121 57 15 45 104
Giovanny Gallegos R 27 3 2 3.02 39 0 59.7 50 20 6 17 73
Mike Hauschild R 29 7 7 4.54 22 21 111.0 115 56 13 50 86
Williams Perez R 28 6 6 4.35 20 19 103.3 109 50 11 34 72
Austin Gomber L 25 8 9 4.49 35 22 132.3 133 66 18 55 121
Adam Wainwright R 37 6 6 4.30 19 18 96.3 102 46 11 32 79
Alex Reyes R 24 4 3 4.08 12 12 64.0 58 29 6 38 66
Jordan Hicks R 22 4 3 3.79 75 0 76.0 67 32 3 48 67
John Brebbia R 29 4 3 3.46 57 0 65.0 57 25 9 18 75
Harold Arauz R 24 7 8 4.76 26 22 126.7 136 67 19 44 97
John Gant R 26 8 9 4.64 31 24 137.7 139 71 19 61 116
Luke Gregerson R 35 3 2 3.54 45 0 40.7 36 16 5 12 44
Tyler Webb L 28 2 2 3.99 47 1 58.7 55 26 8 21 60
Bud Norris R 34 4 4 3.83 60 0 51.7 46 22 7 22 61
Ryan Meisinger R 25 3 3 4.14 48 1 67.3 63 31 8 28 65
Tommy Layne L 34 1 1 3.48 37 0 31.0 28 12 2 13 29
Anthony Shew R 25 8 9 4.85 25 24 133.7 150 72 20 42 92
Genesis Cabrera L 22 8 9 5.00 26 24 122.3 127 68 15 72 100
Ryan Helsley R 24 5 6 4.76 18 17 87.0 84 46 11 50 81
Chasen Shreve L 28 4 3 4.17 58 0 54.0 47 25 8 28 64
Connor Jones L 24 6 7 4.80 22 19 95.7 104 51 10 45 63
Derian Gonzalez R 24 4 4 4.56 25 11 53.3 54 27 5 30 41
Seth Elledge R 23 6 5 4.13 47 0 52.3 47 24 5 28 55
Austin Warner L 25 6 7 4.92 23 22 120.7 130 66 16 53 86
Mike Mayers R 27 2 2 4.29 58 0 63.0 64 30 8 23 56
Dominic Leone R 27 3 3 4.33 52 0 52.0 50 25 7 21 53
Evan Kruczynski L 24 5 7 4.99 20 20 97.3 108 54 14 37 68
Brett Cecil L 32 2 2 4.47 53 0 44.3 46 22 5 19 37
Andrew Morales R 26 3 3 4.40 48 0 59.3 56 29 7 33 59
Edward Mujica R 35 2 2 4.56 45 0 47.3 53 24 8 7 32
Hunter Cervenka L 29 2 2 4.62 40 0 37.0 34 19 4 23 36
Chris Beck R 28 1 2 5.00 48 1 63.0 65 35 8 35 45
Roel Ramirez R 24 2 3 5.10 41 2 60.0 64 34 9 28 49
Will Latcham R 23 4 5 4.96 42 0 49.0 48 27 6 31 45
Landon Beck R 26 3 3 4.91 45 0 58.7 61 32 8 31 47
Junior Fernandez R 22 2 3 5.40 21 8 53.3 57 32 5 39 32
Jake Woodford R 22 8 11 5.32 27 26 133.7 153 79 17 67 76
Casey Meisner R 24 5 8 5.80 23 22 111.7 126 72 18 62 73

Pitchers – Rate Stats
Player TBF K/9 BB/9 HR/9 BABIP ERA+ ERA- FIP WAR No. 1 Comp
Carlos Martinez 724 8.71 3.64 0.80 .295 113 88 3.76 3.2 Bob Gibson
Miles Mikolas 737 6.92 1.95 0.92 .296 112 90 3.73 3.0 Frank Sullivan
Jack Flaherty 711 10.22 3.14 1.17 .290 111 90 3.79 2.9 Aaron Sele
Daniel Poncedeleon 529 8.22 4.60 0.91 .293 96 104 4.31 1.3 Kirby Higbe
Dakota Hudson 662 5.94 3.84 0.78 .292 93 108 4.38 1.3 George Culver
Andrew Miller 201 12.02 3.14 0.74 .297 149 67 2.84 1.3 Randy Myers
Michael Wacha 520 7.78 3.37 1.12 .299 94 107 4.21 1.2 Ed Wojna
Giovanny Gallegos 245 11.01 2.56 0.91 .301 137 73 3.02 1.1 Rollie Fingers
Mike Hauschild 497 6.97 4.05 1.05 .299 91 110 4.66 0.9 Don Schwall
Williams Perez 451 6.27 2.96 0.96 .299 92 109 4.30 0.9 Jim Bagby
Austin Gomber 583 8.23 3.74 1.22 .301 89 112 4.50 0.9 Terry Mulholland
Adam Wainwright 419 7.38 2.99 1.03 .310 93 107 4.08 0.9 Mel Harder
Alex Reyes 287 9.28 5.34 0.84 .299 98 102 4.23 0.8 Tim Birtsas
Jordan Hicks 345 7.93 5.68 0.36 .291 106 95 4.07 0.8 Turk Farrell
John Brebbia 269 10.38 2.49 1.25 .293 116 86 3.61 0.8 Rod Beck
Harold Arauz 557 6.89 3.13 1.35 .300 87 115 4.78 0.7 Michael Macdonald
John Gant 609 7.58 3.99 1.24 .295 86 116 4.72 0.7 Mike Dunne
Luke Gregerson 168 9.74 2.66 1.11 .292 117 86 3.54 0.5 Joe Borowski
Tyler Webb 251 9.20 3.22 1.23 .296 104 97 4.10 0.5 Mike Gallo
Bud Norris 224 10.63 3.83 1.22 .300 104 96 4.06 0.5 Kane Davis
Ryan Meisinger 291 8.69 3.74 1.07 .294 100 100 4.15 0.5 Keith Shepherd
Tommy Layne 133 8.42 3.77 0.58 .295 119 84 3.47 0.4 Luis Arroyo
Anthony Shew 589 6.19 2.83 1.35 .304 83 121 4.82 0.4 Nate Cornejo
Genesis Cabrera 564 7.36 5.30 1.10 .303 83 121 5.05 0.4 Greg Kubes
Ryan Helsley 394 8.38 5.17 1.14 .296 84 119 4.83 0.4 Preston Hanna
Chasen Shreve 235 10.67 4.67 1.33 .291 99 101 4.32 0.3 Ron Villone
Connor Jones 434 5.93 4.23 0.94 .303 83 120 4.79 0.3 Derek Thompson
Derian Gonzalez 243 6.92 5.06 0.84 .299 88 114 4.69 0.3 Foster Edwards
Seth Elledge 232 9.46 4.82 0.86 .298 97 103 4.07 0.3 Anthony Chavez
Austin Warner 543 6.41 3.95 1.19 .299 81 123 4.94 0.3 Jeff Kaiser
Mike Mayers 274 8.00 3.29 1.14 .304 93 107 4.26 0.2 Ehren Wassermann
Dominic Leone 224 9.17 3.63 1.21 .303 92 108 4.13 0.1 Miguel Saladin
Evan Kruczynski 435 6.29 3.42 1.29 .303 80 125 4.95 0.1 Ryan Spille
Brett Cecil 196 7.51 3.86 1.02 .306 93 108 4.30 0.1 Mike Venafro
Andrew Morales 266 8.95 5.01 1.06 .299 91 110 4.52 0.1 Marc Pisciotta
Edward Mujica 200 6.08 1.33 1.52 .298 91 110 4.57 0.1 Dick Hall
Hunter Cervenka 167 8.76 5.59 0.97 .294 87 115 4.64 0.0 Matt Whisenant
Chris Beck 287 6.43 5.00 1.14 .291 83 121 5.18 -0.1 Bobby Reis
Roel Ramirez 272 7.35 4.20 1.35 .302 81 123 5.07 -0.2 Jason Szuminski
Will Latcham 226 8.27 5.69 1.10 .298 81 124 4.99 -0.2 Rick Greene
Landon Beck 267 7.21 4.76 1.23 .298 82 123 5.06 -0.2 Barry Hertzler
Junior Fernandez 256 5.40 6.58 0.84 .295 74 135 5.59 -0.2 Mike Thompson
Jake Woodford 618 5.12 4.51 1.14 .302 75 133 5.35 -0.3 Jake Dittler
Casey Meisner 521 5.88 5.00 1.45 .299 69 145 5.79 -0.9 Jason Standridge

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 2019. 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 I have made a mistake. This is very possible, as a lot of minor-league signings go generally unreported in the offseason.

ZiPS’ projections are based on the American League having a 4.29 ERA and the National League having a 4.15 ERA.

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

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


Contract Crowdsourcing Results: Bryce Harper and Manny Machado

In October, we asked you what contracts you expected Bryce Harper and Manny Machado to sign. Months later, Harper and Machado are still looking for an employer, and so on Wednesday, we asked you about the contracts again. The idea was to see whether the community has lost a little faith in the agents or the market. Do you still see the same big contracts, or do you expect smaller terms? What have you made of all the recent reports?

As you all know, you are (probably) not Bryce Harper, Manny Machado, Scott Boras, or Dan Lozano. This is just a fun exercise that means literally nothing in the end. But, it might not surprise you to learn that FanGraphs readers don’t see quite the same dollars anymore. After running the project again yesterday, we’ve received thousands of entries, so everything ought to be stabilized. The results are posted in the table below.

Read the rest of this entry »


Yankees Reassemble Nightmarish Towering Bullpen of Doom

While the baseball world has waited for the Yankees to become involved in the Manny Machado sweepstakes, they re-signed CC Sabathia. They traded for James Paxton. They re-signed J.A. Happ. They signed Troy Tulowitzki. They re-signed Zach Britton. They signed DJ LeMahieu. And now, on Thursday, they’ve signed Brooklyn native Adam Ottavino. You can say that Machado would still be a fit — indeed, Machado would still be a fit — but you can’t accuse the club of inaction. Brian Cashman and his staff have been busy.

Ottavino is signing for three years, with a $27-million guarantee. While so far we’ve seen just two contracts of three or more years given to position players, this is the fourth of the offseason for a reliever, with Ottavino joining Britton, Joe Kelly, and Jeurys Familia. At 33 years old, Ottavino counts as the elder statesman of the group. Teams now tend to be disinclined to give such multi-year guarantees to players entering their mid-30s. But the thing about Ottavino is that he’s great.

Read the rest of this entry »


Job Posting: Mets Baseball Research and Development Analyst

Position: Analyst, Baseball Research & Development

Job Description Summary:
The New York Mets are seeking a Data Analyst to work on its Research and Development team. The employee will analyze baseball data in order to build models that support player development, baseball operations, and scouting. The Analyst will report to the Director, Baseball Research and Development.

Job Description:

  • Assist development team to create and integrate new tools into existing enterprise application.
  • Collaborate with members of Research and Development team to maintain long term information and systems architecture for Baseball Operations.
  • Write scripts which support data collection, automation, and report generation.
  • Interface with Baseball Operations leadership on player evaluation, in-game strategy, scouting, and player development.

Requirements:

  • Bachelor’s degree or equivalent experience in Statistics, Data Science, Mathematics, Physics, Computer Science, or similar quantitative field.
  • Strong experience querying and managing data with SQL.
  • Demonstrated experience using statistical tools and packages in R or Python.
  • Familiarity with baseball specific datasets (i.e., Trackman, Statcast, etc) and knowledge of current baseball research.
  • Preference for candidates who have demonstrated experience building web applications in Python, Java, PHP, Javascript, etc. Please provide a link to the application and/or codebase (Github) if possible.

To Apply:
Please follow the link to the application and apply by January 25th.

The content in this posting was created and provided solely by the New York Mets.


Jay Jaffe FanGraphs Chat – 1/17/2019

12:46
Jay Jaffe: Hey gang, it’s my staycation week but a short window opened up in my schedule and I decided to take some questions. So let’s talk some baseball!

12:47
Mat: Jayson Stark recently posted his HOF ballot. With his voting for a Closer, DH, Coors players, and PED suspected players, do you believe all HOF barriers have now been broken?

12:51
Jay Jaffe: People have been voting for various categories within your list for a long time. We’ve had relievers in the Hall since Hoyt Wilhelm was elected in 1985, with five getting in from 2004 (Eckersley) onward. DH’s — depends on your definition but Paul Molitor (elected 2003) had a plurality of his games there, and now . PEDs, let’s not be naive, there are already users enshrined. And people — not a lot of them, but some — have been voting for Larry Walker since he hit the ballot in 2011.

Now, whether we get our first Coors player in is another matter…

12:51
B: Is Joey Votto a hall of famer if he retires today?

12:53
Jay Jaffe: From a JAWS standpoint, he’s close enough that I would vote for him — above on peak ( 58.8/46.1/52.4 for him,    66.8/42.7/54.7 for the standards) but with only 1,729 hits, he’d still have the Rule of 2,000 resistance to overcome, and right now that’s pretty daunting.

12:54
Alec: Today is his birthday. Do you think Don Zimmer should be in the Hall Of fame as a ambassador of the game?

Read the rest of this entry »


Let’s Fix MLB’s Salary Arbitration System: Changing the Either/Or Model

In our Introduction, we reviewed some of the issues attendant with the salary arbitration system. Today, we begin to examine solutions. As the system exists currently, I would argue that the largest difference between salary arbitration in baseball and arbitration of the type you see in other disputes is the requirement that the arbitrators must select the position of one side or the other in toto, a feature that seems at odds with arbitration’s goal of helping the parties reach compromise. As MLB’s online glossary explains (emphasis mine):

If the club and player have not agreed on a salary by a deadline in mid-January, the club and player must exchange salary figures for the upcoming season. Unsurprisingly, the club files a lower number than the player does. After the figures are exchanged, a hearing is scheduled in February. If no one-year or multi-year settlement can be reached by the hearing date, the case is brought before a panel of arbitrators. After hearing arguments from both sides, the panel selects either the salary figure of either the player or the club (but not one in between) as the player’s salary for the upcoming season.

This “either/or” approach is unique not just in sports but in arbitration generally. Even the National Hockey League, the only other major North American sport to utilize an arbitration system, doesn’t bind the team and player to only those two options. As one NHL agent explained:

Hockey, unlike baseball, does not have final offer arbitration whereby an arbitrator is bound to pick one side’s proposal or the other. The arbitrator, under current guidelines, is free to pick their own level of compensation anywhere between the two requests.

Hockey’s system, by allowing more freedom to arbitrators in selecting salary figures, granting award rejection rights (in many, but not all, arbitration settings, the losing party has the right to reject the award, at which point various other means ranging from litigation to mediation to a second arbitration are used to reach a resolution), and setting caps on the number of arbitration hearings allowed per team, is substantially more in line with traditional arbitration in other settings. It’s therefore no surprise that hockey has substantially fewer arbitration hearings each year than baseball does.

So why does the ability to select a different number matter? Because the current system in baseball actually incentivizes teams to proceed to hearings, a reality that many teams are now taking advantage of with “file-and-trial” approaches. Consider: as attorney Justin Sievert explained for the Sporting News, when an arbitrator is bound to choose one number or the other, “the panel will choose the offer that is closer to what they believe is the player’s true arbitration value.” To show how this creates issues, let’s look at Dellin Betances’ 2017 arbitration hearing with the Yankees – the one that had Randy Levine so riled up. Betances asked for $5 million; the Yankees countered with $3 million. Let’s say that the panel had concluded Betances was worth $3.95 million. Under current rules, the Yankees win the hearing – and, by extension, are able to pay Betances less than what he has been deemed worth as a result of submitting a lowball figure.

Now you might think that players also benefit from this margin of error: after all, teams that lose arbitrations arguably end up overpaying their players. But that’s not really how it ends up working, for several reasons. First, teams are allowed by the league to confidentially coordinate arbitration filings and salaries, the effects of which can linger long after an individual player’s case is resolved. Per Jeff Passan:

While MLB works diligently and impressively to coordinate the arbitration targets of its 30 teams — this behavior is sanctioned under the collective bargaining agreement and not considered collusive — agents occasionally make far-under-target settlements. The effect, in a comparison-based system, is devastating: A bad settlement can linger and depress prices at a particular position for years.

Why do we care that teams coordinate filings? Because agents, who are in competition for the same clients, clients with disparate individual interests, don’t achieve the same level of cohesion. Imagine, if you will, that you’re an employee looking for a job. You’ve received three offers from three different employers for roughly the same position. Now imagine that those three employers talked amongst themselves, and decided to make you exactly the same offer for each position. And, to make things more interesting, imagine that they are also collaborating to set the salaries for the other candidates, too. You wouldn’t have much in the way of leverage to make salary demands. The three employers have set the market for your salary, and your ability to effectively counteroffer has been essentially rendered moot.

Now, you might point out that, unlike our job example, arbitration isn’t a free market. The Cubs can’t compete with the Nationals over Kyle Barraclough. But what the Cubs can do is agree with the Nationals on what a Kyle Barraclough is worth. Why do we care? Because arbitration is a comparisons-based system. The current system allows teams to, in essence, work together to set the prices for the comparables their own players will cite. The teams are coordinating amongst themselves to drive down prices for all players, because every player is a comparable for someone, and the teams have set prices for everyone.

The trouble is that agents have no way of knowing what those internal calculations are until after all of the arbs are finished in a given year. Another way to look at this is to consider that teams are building their own valuation tool in arbitration, one that is universal across teams, is position- and comparable-adjusted, and – most importantly – is internally consistent and predictable. Agents’ numbers don’t have that level of cohesion. So when teams enter arbitration with consistent numbers, and players don’t, it’s the players’ requests that appear out of step with the realities of the market. The either/or arbitration system facilitates that trend.

This knowledge gap creates a structural mismatch in favor of teams, a mismatch that shown itself in arbitration outcomes. Players who went to arbitration last winter did fairly well in their cases, and Passan cited an oft-used statistic that “[t]he league historically has won well more than 50 percent of cases.” But in reality it’s much more lopsided than that. In March of last year, attorney Christopher Deubert noted that:

[there] seems to be an increasing willingness of clubs to challenge a rise in player salaries by pursuing salary disputes through the conclusion of the arbitration process – albeit, in many instances, unsuccessfully – as reflected in the aggregate arbitration hearing records.  In 43 years of salary arbitration:

  • In 32 of those years (74.4%), clubs won the majority of salary arbitration hearings;
  • In 10 of those years (23.3%), players won the majority of salary arbitration hearings; and
  • In 1 year, all the cases settled.

That teams won a majority of cases in three-quarters of the years for which data is available is pretty remarkable, and demonstrates just how lopsided the present either/or system is when confronted with the knowledge gap created by team coordination. Teams are incentivized to offer lower numbers, knowing that, because all other teams are doing the same, they are likely to succeed. For a player, proceeding to arbitration has meant that they are more likely than not to be underpaid relative to the figure they submit to the arbitrator, which generally serves to incentivize settlements and drive down overall player earnings. Given that the likelihood of winning is priced into settlements, a midpoint between the two figures is no longer the ideal settlement posture; players are incentivized to accept a number closer to the team’s figure just to take every dollar they can. And players, their agents, and the MLBPA have far fewer resources at their disposal for hearings, a problem that is compounded as file-and-trial method results in more cases reaching arbitration. From Passan:

Going to trial can be pricey, particularly for smaller agencies that do not have in-house lawyers with enough expertise or experience to argue an arbitration case. Hiring outside counsel costs up to $55,000, an expense that falls on the agent. And when the spread, or the difference between the sides, is minimal and the 5 percent fee on the difference won’t come close to covering the attorney fees, the incentive is clearly to settle — a fact that teams know and leverage.

So why would eliminating the either/or system help? First, it incentivizes numbers closer to the player’s actual worth, rather than basing a result on resource allocation and structural features. Second, it allows arbitrators leeway to avoid outliers – the current system incentivizes extremes, whereas scrapping the either/or system allows arbitrators to push the parties towards compromise. And third, it creates organic salary movement as arbitrators begin to make their own determinations regarding player worth, requiring them to become more educated in baseball vernacular. And better educated arbitrators are good for everyone, a fact that will be the focus of my next piece.


MLB Payroll Probably Isn’t Going Back Up in 2019

In 2018, for the first time in more than a decade, player salaries went down from the previous year. At one point, there was some thought that this offseason’s free agent class might reverse the course charted last offseason. For years, we’ve been hearing about the monster class of free agents that would sign this winter. Here’s Jeff Passan back near the beginning of the 2017 season:

For those who have yet to hear about the free-agent class of 2018-19, here’s a sampling: Bryce HarperManny MachadoClayton KershawJosh Donaldson, Daniel Murphy, Dallas Keuchel, Charlie Blackmon, Andrew Miller, Zach Britton, Craig Kimbrel. There are dozens more. Teams will guarantee $3 billion to players that winter. The number could exceed $4 billion.

Two years later, that free agent class isn’t quite as good as we expected. When we put up our Top-50 free agents, along with crowdsourced contract expectations, the expected outlay to 66 potential free agents didn’t come close the $3 billion assumed; the crowd predicted a number that was just about half of $4 billion that was thought possible about 20 months ago. Our readers provided estimates for 66 players, including Joe Mauer and Adrian Beltre, who have since retired. Of the remaining 64 players, 36 have signed contracts so far. Here’s how the contract totals compare to the crowdsourced average.

Free Agent Signings and Contract Predictions
Name Signing Team Proj WAR Crowd Average Contract Total Difference
Patrick Corbin WSN 3.5 102.3 M 140 M 37.7 M
Nathan Eovaldi BOS 2.7 44.5 M 68 M 23.5 M
Andrew McCutchen PHI 2.9 43.1 M 50 M 6.9 M
Zach Britton NYY 1.1 31.8 M 39 M 7.2 M
J.A. Happ NYY 2.8 32.6 M 34 M 1.4 M
Michael Brantley HOU 2.4 42.2 M 32 M -10.2 M
Charlie Morton TBR 2.8 32 M 30 M -2 M
Jeurys Familia NYM 1.0 33 M 30 M -3 M
Lance Lynn TEX 1.6 27.3 M 30 M 2.7 M
Andrew Miller STL 1.3 26 M 25 M -1 M
Joe Kelly LAD 1.0 16.1 M 25 M 8.9 M
DJ LeMahieu NYY 2.0 41 M 24 M -17 M
Daniel Murphy COL 1.9 29.6 M 24 M -5.6 M
Josh Donaldson ATL 4.2 57.8 M 23 M -34.8 M
David Robertson PHI 1.4 26.3 M 23 M -3.3 M
Jed Lowrie NYM 2.1 26.8 M 20 M -6.8 M
Wilson Ramos NYM 2.2 35.6 M 19 M -16.6 M
Anibal Sanchez WSN 1.7 11.8 M 19 M 7.2 M
Yasmani Grandal MIL 3.2 51.6 M 18.3 M -33.3 M
Kelvin Herrera CHW 0.4 24.8 M 18 M -6.8 M
Hyun-Jin Ryu LAD 2.0 35.6 M 17.9 M -17.7 M
Garrett Richards SDP 0.0 17.4 M 15 M -2.4 M
Joakim Soria OAK 0.9 14.8 M 15 M 0.2 M
Nelson Cruz MIN 3.2 28.2 M 14.3 M -13.9 M
Matt Harvey LAA 1.0 14.7 M 11 M -3.7 M
Kurt Suzuki WSN 1.3 10.1 M 10 M -0.1 M
Brian Dozier WSN 2.2 31.9 M 9 M -22.9 M
Trevor Cahill LAA 1.3 14.5 M 9 M -5.5 M
CC Sabathia NYY 1.2 10.7 M 8 M -2.7 M
Ian Kinsler SDP 1.7 11.8 M 8 M -3.8 M
Jesse Chavez TEX 0.6 7.5 M 8 M 0.5 M
Trevor Rosenthal WSN 1.2 9.9 M 7 M -2.9 M
Steve Pearce BOS 1.2 10.5 M 6.3 M -4.2 M
Jonathan Lucroy LAA 1.9 10.4 M 3.4 M -7 M
Lonnie Chisenhall PIT 0.9 10.4 M 2.8 M -7.6 M
Brian McCann ATL 1.0 10.1 M 2 M -8.1 M
TOTAL $984.7 M $838 M -$146.7 M

So far, free agents have signed for roughly 15% less combined than predicted. It’s interesting to note that the discount is being taken almost entirely in the length of the contracts, as both the predicted and actual AAV are around $12 million. Now that many players have reached agreements to avoided arbitration and a good number of players have signed free agent deals, we can take a look at where teams’ Opening Day payrolls stand at this point in the offseason. There are obviously a few huge contracts to go, with five of the top six contract projections still unsigned, so these numbers are nowhere near final. As of this writing, here’s what Opening Day payrolls look like for every team.

The Red Sox are well out in front of everybody, just like they were last season. The Cubs payroll is up compared to where it was. It might be somewhat difficult, if not impossible, to tell how every team’s payroll has moved based on the graph above. For reference, here’s a graph showing each team’s change in payroll from Opening Day last season.

Some of this will change in the coming months, though a lot of those teams at the bottom aren’t really expected to move the needle much. Last year’s Opening Day payroll average came in at around $136 million. Keep in mind, these numbers don’t include benefits or players on the 40-man roster. Last year, the end-of-season payroll average including those numbers went up to around $152 million. Right now, the average 2019 Opening Day payroll comes in at close to $128 million. In total, the difference between last year’s combined Opening Day payrolls and payrolls right now is $243 million. If we take a look at how much spending is left to be done, we might be able to approximate payroll for next season.

Here are the remaining crowdsourced free agents, their projected total salaries and the average annual value of each deal.

Remaining Free Agent and Contract Predictions
Name Proj WAR Crowd Avg Years Crowd Avg Total ($M) Crowd AAV ($M)
Bryce Harper 4.9 9.1 $300.0 $33.0
Manny Machado 5 8.6 $272.9 $31.7
Dallas Keuchel 3.3 4.2 $81.0 $19.4
Craig Kimbrel 2.1 3.9 $62.2 $16.1
A.J. Pollock 3.1 3.7 $58.8 $16.0
Mike Moustakas 2.8 2.8 $34.3 $12.2
Adam Ottavino 0.8 2.6 $27.0 $10.3
Marwin Gonzalez 1.8 2.9 $29.5 $10.1
Jose Iglesias 1.7 2.8 $25.6 $9.1
Gio Gonzalez 0.8 2.3 $26.4 $11.6
Nick Markakis 1.1 1.9 $19.9 $10.8
Adam Jones 1.2 1.9 $18.7 $9.9
Asdrubal Cabrera 2 2.1 $20.4 $9.6
Cody Allen 0.5 2.3 $20.5 $9.0
Wade Miley 1.1 1.9 $15.9 $8.5
Josh Harrison 1.2 1.9 $14.5 $7.5
Freddy Galvis 0.3 2.1 $15.1 $7.2
Brad Brach 0.1 2.0 $15.0 $7.5
Justin Wilson 0.1 2.1 $12.2 $6.0
Martin Maldonado 1 1.8 $10.7 $5.9
Carlos Gonzalez 1.3 1.5 $10.9 $7.3
Ryan Madson 0.1 1.2 $6.9 $5.8
Clay Buchholz 1 1.4 $9.3 $6.6
Jeremy Hellickson 0.4 1.5 $9.2 $6.2
Greg Holland 0 1.3 $7.4 $5.8
Tony Sipp 0 1.2 $5.9 $4.8
Shawn Kelley 0 1.2 $5.4 $4.5
Zach Duke 0 1.2 $4.7 $3.9
TOTAL 73.3 $1140.3 M $296.1 M

If the crowd is correct and $296 million in salaries are added to the 2019 season, we’ll be looking at around a $50 million increase over last season. That probably won’t happen, though. Last season when I did this same exercise, I was overly generous in my estimates, giving the players the crowdsourced money, and adding in another $60 million for all the other players for whom we did collect estimates. By the time Opening Day rolled around, I was more than $100 million off, and instead of a potential 1% increase in payroll, teams moved down 1% from the previous year.

If the rest of this offseason is anything like the remainder of last offseason, we are going to be looking at flat Opening Day payrolls. Even more troublesome for the players is that while Opening Day payroll was down about one percent from 2017 to 2018, when the end of season numbers were calculated, that figure was closer to 2.5%. While not definitive yet, there seems to be a pretty good possibility that major league payrolls will go down for the second consecutive season.


Effectively Wild Episode 1322: Read All About It

EWFI
Ben Lindbergh and Jeff Sullivan banter about Ben and Travis Sawchik finishing their new book, The MVP Machine, Willians Astudillo’s MVP voting results, the Kyler Murray baseball/football decision and why it’s unusual for players in his situation to have leverage, Manny Machado rumors, and Yasmani Grandal’s comments about the contract he didn’t sign, then answer listener emails about why baseball players don’t hold out and Rickey Henderson’s base-stealing today, plus Stat Blasts about the biggest gaps between best and second-best years and the giant New York Yankees.

Audio intro: The Decemberists, "Midlist Author"
Audio outro: Cake, "Open Book"

Link to preorder The MVP Machine
Link to Astudillo results
Link to Jeff’s Garcia post
Link to hitter height data
Link to Grant’s Little League WAR article
Link to article about baseball terms in Spanish

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