Updated Fielding Projections

As of this morning, we’ve updated our fielding projections.  There are two major changes in the way we’re cooking this year’s fielding projections.   First, these are now based on more data: 5 past years and the current season (in-season, that is) of UZR data instead of merely 2 past years of UZR’s and the current season.  Second, they are regressed towards Tom Tango’s Fan Scouting Reports instead of towards zero.  This ends up creating somewhat more aggressive projections.  Andrelton Simmons leads the way and is projected to be 21 runs better than the average shortstop.  The biggest gainer from the old system to the new system is Brett Gardner who goes from a projection of merely +1 all the way up to +14 (the 4th best UZR projection after Simmons, Machado and Arenado).  Check out the table below to see how fielders fared under each system.

UZR Projection Changes

steamerid
name
position
new
old
gain
9927Brett GardnerLF14.20.913.3
10847Andrelton SimmonsSS21.111.59.6
9368Evan Longoria3B13.76.47.3
11493Manny Machado3B16.99.97.0
639Adrian Beltre3B7.61.06.6
9777Nolan Arenado3B14.98.66.3
8370Dustin Pedroia2B9.53.65.9
9218Paul Goldschmidt1B7.11.25.9
1281Mark Teixeira1B7.82.15.7
1908Adrian Gonzalez1B10.34.55.7
3473Anthony Rizzo1B10.24.55.6
791Brandon Phillips2B9.23.75.6
8709Elvis AndrusSS8.32.85.5
10099Dustin AckleyLF2.6-2.75.3
5209Alex GordonLF8.43.64.8
1875Josh HamiltonLF0.4-4.24.6
2578Peter BourjosCF9.44.94.5
3882Chris YoungRF6.42.04.4
5222Justin UptonLF-1.1-5.24.1
5038Josh Donaldson3B9.15.14.1
4314Joey Votto1B5.61.64.0
10264Brandon Belt1B5.71.73.9
4556James Loney1B6.72.73.9
88Rafael Furcal2B2.4-1.53.8
6195Ian Kinsler2B1.5-2.33.8
4298Matt WietersC8.34.53.8
7007Yadier MolinaC9.55.93.7
1177Albert Pujols1B4.40.83.6
3361Gaby Sanchez1B3.50.03.5
8433Ike Davis1B2.7-0.73.4
3174Shin-Soo ChooLF-5.5-8.83.4
5247Brett Lawrie3B4.71.43.3
7435Ben Zobrist2B6.73.43.2
7304Salvador PerezC7.34.23.1
1679Chase Utley2B4.71.63.1
4903Matt Dominguez3B3.10.13.1
4220Ryan Zimmerman3B-1.2-4.23.1
7476Alex AvilaC0.6-2.32.9
5933Jean SeguraSS2.2-0.72.9
971Jimmy RollinsSS2.6-0.42.9
6073Brendan RyanSS3.91.02.9
9166Buster PoseyC5.93.02.9
3797J.J. HardySS5.22.42.8
2430Darwin Barney2B9.06.42.6
6310Alcides EscobarSS3.30.82.5
9241Starling MarteLF10.37.92.4
1904Adam LaRoche1B2.60.32.3
1857Joe Mauer1B2.0-0.22.3
3531Troy TulowitzkiSS4.01.82.2
4747Curtis GrandersonLF1.3-0.92.2
7870Jonathan LucroyC0.1-2.02.2
1201Carl CrawfordLF5.02.82.1
6589Sean Rodriguez1B2.40.32.1
4892Mike Moustakas3B6.03.92.1
5760Avisail GarciaRF-2.8-4.82.0
1930Casey Kotchman1B1.8-0.11.8
5343Brandon CrawfordSS4.42.61.8
9848Austin JacksonCF1.1-0.61.7
8631Cord Phelps1B1.7-0.11.7
1737Justin Morneau1B0.6-1.11.7
2073Jeff Baker1B1.4-0.31.7
4720Chase Headley3B4.52.81.6
1433Wilson RamosC1.0-0.61.6
4949Giancarlo StantonRF1.90.41.5
6012Didi GregoriusSS2.00.61.5
8259Kurt SuzukiC0.3-1.21.4
1825David DeJesusLF1.70.31.4
5254Robbie GrossmanLF-1.8-3.21.4
950Marlon ByrdRF1.50.11.4
1698Gerald LairdC1.80.51.4
3692Jack Hannahan3B1.1-0.31.4
9810Brian Dozier2B1.0-0.31.3
3364Miguel MonteroC4.83.51.3
3516Eric Hosmer1B0.2-1.11.3
3371Alejandro De AzaLF-0.6-1.81.3
2579Carlos RuizC2.81.51.2
1744Miguel Cabrera1B-2.4-3.61.2
9892Jay BruceRF3.82.61.2
9627Yan GomesC4.83.61.2
4229Howie Kendrick2B1.70.51.1
2530Yonder Alonso1B2.21.11.1
9272Chris Davis1B0.8-0.31.1
1609Omar Infante2B1.20.21.0
9054Justin Smoak1B0.5-0.51.0
3892Josh ReddickRF10.49.50.9
8722Jason CastroC-1.8-2.80.9
10306Oswaldo ArciaRF-4.6-5.50.9
6387Michael BournCF3.62.80.9
1677Shane VictorinoRF8.87.90.9
1066Willie Bloomquist3B0.4-0.50.9
731Torii HunterRF0.2-0.60.9
2411Jose TabataRF-2.0-2.90.9
7620Justin RuggianoCF-2.1-2.90.8
14225Yasiel PuigRF4.43.60.8
1327Jayson WerthRF-4.0-4.80.8
4616Russell MartinC5.04.20.8
1176Placido Polanco3B0.6-0.20.8
5133Alexei RamirezSS1.60.90.8
2216Don KellyLF-0.4-1.10.7
11579Bryce HarperLF4.33.60.7
4952Ryan HaniganC5.85.00.7
3231Brayan PenaC-0.2-0.90.7
6677J.B. ShuckLF-1.6-2.30.7
11038Kevin KiermaierCF0.70.00.7
9981Michael SaundersCF-2.0-2.70.7
4719Brian BogusevicLF1.10.50.7
3256Welington CastilloC0.2-0.50.6
5409Pablo Sandoval3B-1.0-1.70.6
9015Gordon Beckham2B-0.4-1.00.6
6656L.J. HoesRF-2.5-3.10.6
9571Craig GentryCF6.76.10.6
5384Juan LagaresCF9.18.50.6
8267Chris IannettaC-0.6-1.20.6
5677A.J. EllisC5.75.20.6
6184J.D. MartinezLF-0.6-1.20.6
5497Marwin GonzalezSS0.3-0.30.5
7739Travis d'ArnaudC-0.2-0.70.5
4106Michael BrantleyLF-0.6-1.10.5
13075Norichika AokiRF0.60.10.5
3269Robinson Cano2B2.72.20.5
4599Nick Swisher1B1.10.60.5
9874DJ LeMahieu2B2.11.60.5
10299Eury PerezLF0.50.00.5
4672Junior LakeLF3.12.60.5
656Cesar IzturisSS0.60.10.5
9187Jordan DanksRF0.50.10.5
1873Matt HollidayLF-4.3-4.80.5
49Kyle BlanksDH0.1-0.30.5
3057Mike Napoli1B2.92.50.5
6777Tyler PastornickyDH-0.5-0.90.4
11368Yasmani GrandalC-1.8-2.20.4
5930Nick MarkakisRF-5.0-5.40.4
81Henry BlancoC0.2-0.20.4
6887Martin MaldonadoC0.90.50.4
3086Mitch MorelandDH0.90.40.4
5000Stephen VogtC0.40.00.4
4082Erick AybarSS-2.2-2.60.4
7244Tyler Moore1B-0.4-0.80.4
25Jose MolinaC1.10.70.4
9312Ryan Wheeler1B0.3-0.10.4
6201Nate SchierholtzRF-1.0-1.40.4
3878Chris StewartC0.2-0.10.4
10459Adeiny HechavarriaSS-3.9-4.20.4
9219Danny Espinosa2B1.61.30.4
5361Freddie Freeman1B1.10.80.4
9423Chris McGuiness1B0.40.00.3
6827Justin MaxwellCF0.0-0.30.3
589Carlos BeltranRF-3.1-3.40.3
4866Jarrod DysonCF2.42.10.3
8027Adam Lind1B-0.1-0.40.3
5450Daniel NavaLF-4.7-5.00.3
7250Collin CowgillRF0.70.40.3
5098Jordany Valdespin2B-0.2-0.40.3
11167Josh Rutledge2B-0.9-1.20.3
1443Mark Ellis2B1.31.10.3
4034Mat Gamel1B0.30.00.3
3735Zach LutzDH0.30.00.3
6104Aaron Hill2B-1.3-1.60.2
5928Daric Barton1B1.21.00.2
6262Tommy FieldSS-0.4-0.60.2
9929Darin RufDH-1.6-1.80.2
5297Aaron HicksCF-0.9-1.10.2
10166Nick FranklinDH-0.1-0.30.2
1101Ichiro SuzukiRF3.02.80.2
5422Steve Lombardozzi3B-0.1-0.30.2
8254Sam FuldLF0.20.00.2
2437Maicer Izturis2B-4.1-4.30.2
13265Mike ZuninoC-0.7-0.80.2
11477Christian YelichLF-1.1-1.30.2
1551David RossC1.91.70.2
9853Sean Halton1B0.10.00.2
2434Nelson CruzDH-0.2-0.40.2
6848Eduardo Nunez3B-1.9-2.00.2
10199Billy HamiltonCF0.90.70.2
2502Lucas DudaDH-0.9-1.00.2
4403Erik KratzC1.31.10.1
3411Drew ButeraC0.10.00.1
7436Brandon AllenDH0.10.00.1
193Carlos Triunfel3B0.10.00.1
2830Travis SniderDH-0.2-0.30.1
6962Ryan KalishRF0.1-0.10.1
9854Efren Navarro1B0.10.00.1
7752David Cooper1B0.10.00.1
906Eric ChavezDH0.0-0.10.1
7287Carlos GonzalezLF0.90.80.1
7937Logan SchaferLF1.41.30.1
5223Cameron MaybinCF-0.2-0.30.1
10815Jurickson Profar2B0.80.70.1
2225Jason BourgeoisRF0.10.00.1
8879Ryan LavarnwayC-0.2-0.30.1
6178Tony SanchezC0.0-0.10.1
1926Scott HairstonDH-0.7-0.80.1
4293Brett HayesC0.0-0.10.1
4984Brock Peterson1B0.10.00.1
11200Kole CalhounRF-0.7-0.80.1
7577Marc KraussRF-1.3-1.40.1
13051Chris Colabello1B0.10.00.1
5386Andrew LamboDH-0.2-0.30.1
11287Bryan HoladayC-0.6-0.60.1
5887John JasoDH-0.2-0.30.1
6582Nate Freiman1B0.30.20.1
2554Chris Parmelee1B0.00.00.1
7423Shane Peterson1B0.00.00.1
2802Tony CruzC0.40.40.1
10289Hector SanchezC-0.5-0.60.1
2677Jordan PachecoDH-0.3-0.30.1
5273Sandy LeonDH-0.1-0.10.0
8434Brett Wallace1B-0.1-0.10.0
4918Engel BeltreCF0.60.50.0
9628Michael McKenryC-0.3-0.40.0
6788Josh SatinDH0.10.10.0
342Corky MillerC0.00.00.0
4792Jeff FrancoeurRF-0.7-0.70.0
11147Tommy MedicaDH0.00.00.0
9542Steve ClevengerC-0.2-0.20.0
5212Alfredo MarteRF-0.1-0.10.0
3867Kelly ShoppachC0.20.10.0
6050Moises Sierra1B-0.3-0.40.0
7515Jhonatan SolanoC0.0-0.10.0
5199Taylor TeagardenC0.00.00.0
5491Austin RomineC-0.1-0.10.0
11265Jonathan Schoop2B-0.4-0.40.0
9689Josh TholeC-0.3-0.30.0
10593Joey TerdoslavichRF0.00.00.0
629Brandon BarnesCF1.11.10.0
10556Cesar HernandezCF-0.3-0.30.0
6402Brent Morel3B0.00.00.0
13047Munenori Kawasaki2B0.20.20.0
9911Chris CarterDH-0.2-0.20.0
7324Chris GimenezC0.00.00.0
7002Will Middlebrooks3B-1.0-1.00.0
3298Charlie Culberson2B-0.1-0.10.0
10059Max StassiC0.00.00.0
7168Juan CentenoC0.00.00.0
113Steven LerudC0.00.00.0
11146Cameron RuppC0.00.00.0
9632Brett JacksonCF0.00.00.0
4365Scott Van SlykeDH0.10.10.0
2783Johnny MonellC0.00.00.0
3376Nick HundleyC0.20.20.0
9176Audry PerezC0.00.00.0
sa454748Charlie CutlerC0.00.00.0
sa548088Miguel Sano3B0.00.00.0
745David OrtizDH0.00.00.0
sa503033Ramon CabreraC0.00.00.0
sa657862Addison RussellSS0.00.00.0
sa501596Jon Singleton1B0.00.00.0
1573Travis HafnerDH0.00.00.0
7765Jermaine Curtis3B0.00.00.0
sa501667Kyle JensenLF0.00.00.0
8610Kendrys MoralesDH0.00.00.0
sa510223Daniel ButlerC0.00.00.0
15676Jose Abreu1B0.00.00.0
sa454887A.J. JimenezC0.00.00.0
sa507194Gregory PolancoRF0.00.00.0
sa455288Dean Anna2B0.00.00.0
sa549634Cheslor Cuthbert3B0.00.00.0
sa549555Gary SanchezC0.00.00.0
sa392355Alfredo SilverioCF0.00.00.0
sa549529Maikel Franco3B0.00.00.0
sa503297Francisco MartinezSS0.00.00.0
sa503874Danny Santana2B0.00.00.0
5306Kristopher NegronSS0.00.00.0
sa501959Brent KeysCF0.00.00.0
sa327116Jared MitchellCF0.00.00.0
sa506574Oscar TaverasCF0.00.00.0
sa455610Jordan Lennerton1B0.00.00.0
sa526553Hunter Morris1B0.00.00.0
sa500820Trayce ThompsonRF0.00.00.0
9591Jeff Kobernus3B0.00.00.0
sa454552Greg MiclatSS0.00.00.0
sa549081Joc PedersonCF0.00.00.0
sa502010Dustin GarneauC0.00.00.0
sa597754Francisco LindorSS0.00.00.0
1861Wilson BetemitDH0.00.00.0
sa501717Lane AdamsRF0.00.00.0
sa503875Kennys Vargas1B0.00.00.0
sa502052Brady ShoemakerRF0.00.00.0
sa392232Bryce BrentzRF0.00.00.0
sa508601Japhet Amador1B0.00.00.0
sa390641Christian ColonSS0.00.00.0
sa526397Ryan LamarreCF0.00.00.0
15670Alexander Guerrero2B0.00.00.0
sa455627Miguel Rojas2B0.00.00.0
sa547735Alen HansonSS0.00.00.0
sa502062Alex HassanRF0.00.00.0
sa598119Thomas La Stella2B0.00.00.0
945Bobby AbreuDH0.00.00.0
11737Nick Castellanos3B0.00.00.0
sa502536Hak-Ju LeeSS0.00.00.0
sa501940Andrew SusacC0.00.00.0
sa502196Justin Bour1B0.00.00.0
sa455056Christian VazquezC0.00.00.0
sa526414George SpringerCF0.00.00.0
10028Christian BethancourtDH0.00.00.0
sa455994Michael Almanzar3B0.00.00.0
sa548308Garin Cecchini3B0.00.00.0
9328Drew StubbsCF0.10.10.0
3353Matt JoyceDH-0.2-0.20.0
8385Pedro FlorimonSS0.80.80.0
10953Phil Gosselin2B0.00.00.0
8418Ehire AdrianzaSS0.00.00.0
7945Jaff DeckerLF0.00.00.0
10154Donald LutzLF0.00.00.0
10655Rob BrantlyC-0.2-0.20.0
5751Hernan Perez2B0.00.00.0
3648Rene RiveraC0.20.20.0
454Juan Uribe3B10.410.50.0
5942Jesus SucreC0.00.00.0
4606Chris SnyderC0.00.00.0
4875Logan Watkins2B0.10.10.0
10339Scooter Gennett2B0.50.50.0
4623Josh Vitters3B-0.10.00.0
4464Mike BaxterDH0.00.00.0
8145Eric FryerC0.00.10.0
1617Lyle Overbay1B0.00.00.0
10346J.R. MurphyC0.00.10.0
9461Thomas NealLF-0.10.00.0
6019Cody ClarkC0.10.10.0
10416Wilfredo TovarSS0.10.10.0
7226Matt Davidson3B-0.2-0.20.0
2616Zack CozartSS4.24.20.0
1824Humberto QuinteroC0.10.10.0
9957Steve PearceLF-0.1-0.10.0
9871Bryan AndersonC-0.1-0.10.0
6880Nick NoonanDH0.00.00.0
10542Derek Dietrich2B-0.2-0.10.0
8029Rob JohnsonC-0.1-0.10.0
877John HesterC-0.1-0.10.0
3142Robinson ChirinosC0.00.00.0
11385Matt den DekkerCF0.00.00.0
6732Zoilo AlmonteLF0.20.20.0
9063Alexi AmaristaDH-0.8-0.70.0
10329Reymond FuentesCF0.00.00.0
5277Tuffy GosewischC0.00.10.0
3118Jesus Guzman1B-0.10.00.0
4756John BakerC-0.10.00.0
7619Mark Reynolds1B-0.2-0.10.0
4940Jason HeywardRF10.810.90.0
6448Jim AdduciLF0.00.0-0.1
6746Josh PrinceLF-0.10.0-0.1
6635Kevin MattisonLF-0.10.0-0.1
6165Irving Falu2B-0.10.0-0.1
10622Zach WaltersSS-0.10.0-0.1
7480Mike Carp1B-0.9-0.8-0.1
4900Robert Andino2B-0.2-0.1-0.1
14441Henry UrrutiaDH-0.10.0-0.1
8609Tim FederowiczC0.00.1-0.1
4063Anthony ReckerC-0.2-0.1-0.1
1830Clint BarmesSS0.50.6-0.1
3390John MayberryDH-2.0-1.9-0.1
5225Roger KieschnickLF0.00.0-0.1
8587Steve SusdorfLF-0.10.0-0.1
6341Luis Jimenez3B0.10.2-0.1
4191Yunel EscobarSS3.13.2-0.1
12532Kolten Wong2B0.50.6-0.1
6596Ryan Jackson3B-0.10.0-0.1
7412Paul JanishSS0.00.1-0.1
6609Freddy GalvisDH0.20.3-0.1
4964Tony CampanaCF-0.2-0.1-0.1
11205Adam EatonCF-7.1-7.0-0.1
2158Greg Dobbs1B-0.10.0-0.1
319Adam DunnDH-0.10.0-0.1
11997Cody Asche3B-1.4-1.3-0.1
6835Nick BussCF-0.10.0-0.1
3388Chris Getz2B-0.10.0-0.1
9060James DarnellLF-0.20.0-0.1
8380Reid BrignacSS-0.10.0-0.1
9284Chris HerrmannC-0.10.0-0.1
4243Jose LobatonC-1.3-1.2-0.1
2539Pete KozmaSS0.00.2-0.2
847Alfonso SorianoRF0.10.3-0.2
7358Michael Martinez2B-0.20.0-0.2
10030Chris OwingsSS-0.10.1-0.2
5653Ryan Roberts2B-0.3-0.1-0.2
393Victor MartinezDH-0.10.0-0.2
2178Brendan Harris2B-0.2-0.1-0.2
8392Daniel Descalso3B-1.9-1.8-0.2
7290Jake ElmoreSS-0.3-0.1-0.2
6867Derek NorrisC-0.4-0.2-0.2
12434Kevin PillarLF1.01.1-0.2
5486Abraham AlmonteCF-2.2-2.0-0.2
3179Dioner NavarroC-1.0-0.8-0.2
6978Juan Francisco1B-2.8-2.6-0.2
5053Tony AbreuDH-0.5-0.3-0.2
5587Carlos CorporanC-0.3-0.1-0.2
2161Jason KubelDH-0.8-0.6-0.2
8272Matt Tuiasosopo3B-0.20.0-0.2
818Jason Giambi1B-0.20.0-0.2
607Raul IbanezDH-0.7-0.5-0.2
1556Wil NievesC-0.20.0-0.2
9393Matt Adams1B-0.5-0.2-0.2
10053Grant Green2B-0.5-0.3-0.2
9147Taylor Green3B-0.20.0-0.2
6400Kirk NieuwenhuisCF-0.20.1-0.2
12533Marcus Semien3B-0.20.1-0.2
8002Wilin RosarioC-2.5-2.2-0.2
10071Jonathan VillarSS-4.4-4.1-0.3
3395Cliff PenningtonDH0.10.4-0.3
7389David Adams3B-0.20.1-0.3
11339Jake MarisnickCF0.50.8-0.3
5002Caleb GindlRF-0.6-0.3-0.3
3433Allen CraigRF-2.9-2.6-0.3
1572Coco CrispCF-2.8-2.6-0.3
4704Donnie Murphy3B-3.1-2.8-0.3
13510Jose RamirezSS-0.30.0-0.3
8090Matt Carpenter3B-1.8-1.5-0.3
7215David LoughLF5.05.3-0.3
7927Eric Sogard2B0.40.7-0.3
6740Johnny Giavotella2B-0.5-0.2-0.3
5913Leury Garcia2B-0.5-0.2-0.3
2636Brandon GuyerLF-0.30.0-0.3
3123Gregor BlancoLF2.93.2-0.3
3373Clete ThomasCF-0.30.0-0.3
7399Billy ButlerDH-0.30.0-0.3
1502Nick Green2B-0.40.0-0.3
4249Shane RobinsonCF0.60.9-0.3
9785Kyle Seager3B-0.9-0.6-0.3
7571Lonnie Chisenhall3B-2.4-2.0-0.3
9856Brandon Snyder3B-0.40.0-0.3
6806Josmil PintoC0.40.7-0.3
5666Devin MesoracoC0.10.5-0.4
13110Yoenis CespedesLF1.21.5-0.4
4450Steve Tolleson2B-0.40.0-0.4
11376Michael ChoiceCF-0.30.0-0.4
9134Tyler FlowersC-0.5-0.1-0.4
4885Nyjer MorganCF-0.30.1-0.4
10822Todd CunninghamRF-0.40.0-0.4
1849Rickie Weeks2B-3.4-3.0-0.4
8252Hunter PenceRF-1.0-0.6-0.4
1165Chris Valaika2B-0.7-0.3-0.4
4390Scott Moore3B-0.5-0.1-0.4
3707Geovany SotoC0.10.6-0.4
9807Ryan Goins2B1.11.5-0.4
242Paul Konerko1B-0.9-0.4-0.5
5503Corban Joseph2B-0.50.0-0.5
3448Jeff MathisC0.61.1-0.5
12861Anthony Rendon2B-1.1-0.6-0.5
1580Chone Figgins3B-0.40.1-0.5
9700Casper WellsCF-0.30.2-0.5
4400Chris DenorfiaRF1.52.0-0.5
5278Pedro Ciriaco2B-2.3-1.8-0.5
2113Ryan DoumitDH-1.6-1.1-0.5
3817Joaquin AriasDH-0.50.0-0.5
2218Ryan RaburnLF0.00.5-0.5
8203Dee Gordon2B-1.6-1.1-0.5
2041John BuckC-0.20.3-0.5
6153Eduardo EscobarSS-1.3-0.7-0.5
7331Seth SmithDH-0.7-0.2-0.5
211Will VenableRF-3.6-3.0-0.5
8841Ramiro Pena3B-0.50.1-0.5
9308Josh PhegleyC-1.3-0.8-0.5
1260Ryan LudwickLF-5.0-4.5-0.5
2090Alex RiosRF-0.7-0.1-0.6
10047Wil MyersRF0.00.6-0.6
8219Jason BartlettSS-0.6-0.1-0.6
4881Carlos GomezCF11.311.9-0.6
7185Logan ForsytheLF-1.2-0.5-0.6
4969Luis ValbuenaDH0.10.8-0.6
5371Juan PerezCF0.81.4-0.6
9205Logan MorrisonDH-1.4-0.7-0.6
11003Evan GattisC0.10.8-0.6
1736Jose ReyesSS-4.3-3.6-0.6
9112Khris DavisLF-3.1-2.4-0.6
1429Nick Punto2B-0.10.6-0.7
4182Jonathan Herrera2B-0.60.1-0.7
1766Laynce NixLF-0.30.3-0.7
6035David MurphyRF1.82.5-0.7
5275Francisco CervelliC-0.60.1-0.7
3708Rajai DavisLF-2.8-2.1-0.7
2591Michael TaylorLF-0.8-0.1-0.7
10231Jose IglesiasSS1.82.5-0.7
2650Corey BrownCF-0.80.0-0.7
3917Dayan ViciedoLF-2.9-2.2-0.7
5506George KottarasC-1.8-1.1-0.8
1159Andrew Romine3B-0.70.1-0.8
4251Stephen DrewSS-1.4-0.6-0.8
3441Nolan ReimoldLF-1.2-0.4-0.8
7859Charlie BlackmonDH-1.2-0.4-0.8
1555Marco Scutaro2B-2.4-1.6-0.8
5097Anthony GoseCF-1.4-0.6-0.8
5496Francisco PegueroRF-0.60.2-0.8
3190Nate McLouthDH-1.8-0.9-0.8
3035Michael MorseLF-9.5-8.6-0.8
7316Darin MastroianniRF-0.10.8-0.8
3837Andrew BrownRF-1.0-0.1-0.8
9256A.J. PollockCF6.97.8-0.9
443Juan PierreLF-2.2-1.4-0.9
9883Jordan SchaferCF-1.3-0.4-0.9
8623Donovan Solano2B-1.1-0.2-0.9
2151Edwin Encarnacion1B-2.3-1.3-0.9
9958Jeff BianchiDH0.61.6-0.9
2881Scott Sizemore3B-1.1-0.2-1.0
5557Jarrod SaltalamacchiaC-3.7-2.7-1.0
10155Mike TroutCF4.05.0-1.0
6876Mark TrumboLF-3.0-2.0-1.0
3410Ryan BraunRF-3.2-2.2-1.0
9414Quintin BerryCF-1.3-0.2-1.0
9345Brock HoltSS-1.6-0.5-1.1
1534Michael CuddyerRF-7.6-6.5-1.1
5519Ruben TejadaSS0.11.2-1.1
4810Brian McCannC-0.50.6-1.1
332Austin KearnsRF-0.70.5-1.1
9009Conor Gillaspie3B-1.6-0.5-1.2
6453Andy DirksLF0.51.7-1.2
8760Carlos PegueroCF-1.20.0-1.2
4712Ben RevereCF-0.80.4-1.2
10816Jedd Gyorko2B-0.60.7-1.2
5227Jon JayCF-1.8-0.5-1.2
5663Neftali Soto3B-1.30.0-1.3
3978Chris HeiseyDH-0.40.9-1.3
5015B.J. UptonCF-1.5-0.1-1.3
9682Adam Rosales3B-1.6-0.3-1.4
166Brian Roberts2B-1.9-0.5-1.4
6364Danny Valencia3B-1.9-0.5-1.4
8347Denard SpanCF1.52.9-1.4
6352Ryan SweeneyLF-0.31.1-1.4
9776Jason Kipnis2B-2.7-1.2-1.5
7158Eric YoungDH-2.2-0.7-1.5
746A.J. PierzynskiC0.01.6-1.6
11846Leonys MartinCF1.83.5-1.6
4022Melky CabreraLF-4.8-3.1-1.7
3856Jeff Keppinger2B-2.1-0.4-1.7
3704Skip SchumakerDH-4.3-2.6-1.7
4727Jacoby EllsburyCF2.84.5-1.7
6547Jordy MercerSS-2.6-0.9-1.7
2498Jemile Weeks2B-2.7-1.0-1.7
5235Justin Turner2B-2.2-0.5-1.7
2197Grady SizemoreCF-1.70.0-1.7
5986Mike AvilesSS-2.4-0.7-1.7
10698Mike Olt3B-1.70.0-1.7
4467Brandon Moss1B-3.4-1.7-1.7
3154Domonic BrownLF-7.7-6.0-1.7
1867Darnell McDonaldDH-1.50.3-1.7
7888Ryan Flaherty2B-0.71.1-1.8
5827Wilmer Flores2B-1.90.0-1.9
12775Brad MillerSS-1.50.4-1.9
2505Hank CongerC-2.4-0.5-1.9
2714Garrett Jones1B-3.3-1.4-1.9
7532Ed LucasSS-2.2-0.3-1.9
12984Jackie BradleyCF-3.2-1.2-2.1
1771Matt DiazLF-2.9-0.8-2.1
1845Jonny GomesLF-3.8-1.7-2.1
7528Kevin Frandsen3B-2.2-0.1-2.1
3312Martin Prado3B-0.21.9-2.1
697J.P. ArencibiaC-3.0-0.9-2.1
2918Angel PaganCF-5.9-3.8-2.1
8553Gerardo ParraCF6.38.4-2.1
5631Matt KempCF-10.6-8.5-2.1
9549David Freese3B-7.7-5.5-2.2
10762Corey DickersonCF-2.4-0.1-2.3
785Todd Frazier3B2.34.6-2.3
5417Jose Altuve2B-4.5-2.1-2.4
2103Josh WillinghamLF-7.9-5.5-2.4
3336Alberto Callaspo2B-4.5-1.8-2.6
2495Pedro Alvarez3B-4.0-1.2-2.8
6265Andre EthierCF-5.6-2.8-2.9
2234Kelly Johnson3B-3.2-0.3-2.9
9847Andrew McCutchenCF-1.71.3-3.0
9077Lorenzo CainCF5.68.7-3.1
8202Josh HarrisonSS-3.20.0-3.1
1887Jose BautistaRF-1.71.4-3.1
2154Ryan Howard1B-4.0-0.9-3.1
6885Ian DesmondSS-1.81.5-3.2
1965Desmond JenningsCF-4.5-1.3-3.2
1760Cody RossRF1.04.2-3.2
2396Carlos SantanaC-4.1-0.8-3.3
7539Neil Walker2B-3.00.4-3.4
6368Adam JonesCF-7.5-4.0-3.4
4613Prince Fielder1B-4.6-1.1-3.5
10324Marcell OzunaCF0.03.7-3.7
3787David Wright3B0.24.0-3.9
8155Everth CabreraSS-5.1-1.0-4.1
1491Ty Wigginton3B-4.8-0.6-4.1
4062Dexter FowlerCF-8.0-3.9-4.1
1002Aramis Ramirez3B-5.4-1.1-4.3
826Derek JeterSS-8.2-3.9-4.3
12161Xander BogaertsSS-3.90.7-4.6
1945Corey HartRF-5.2-0.5-4.8
6274Carlos QuentinLF-9.0-4.2-4.8
9893Colby RasmusCF-1.04.1-5.1
4418Jed LowrieSS-7.2-2.1-5.1
7462Trevor Plouffe3B-9.2-4.1-5.1
1738Jhonny PeraltaSS-2.92.4-5.3
4962Asdrubal CabreraSS-10.5-5.1-5.4
5305Alex PresleyCF-7.5-1.9-5.6
4579Starlin CastroSS-4.31.4-5.7
4316Daniel Murphy2B-9.0-3.1-5.9
6086Casey McGehee3B-5.50.5-6.0
8001Hanley RamirezSS-8.7-2.5-6.2
3442Dan Uggla2B-9.7-2.5-7.2
1191Chris Johnson3B-10.8-3.3-7.5
5310Tyler ColvinCF-11.9-4.3-7.6

 

Comments (11)

  1. MP

    Does Steamer either explicitly or implicitly use strength of schedule as a factor in its projections? The question occurred to me after reading this: http://www.fangraphs.com/blogs/2014-strengths-of-schedule-projected/. I suppose things would get recursive in a hurry since the SOS calcs. were done using Steamer as an input, but wondering if your team adjustments already include some adjustments for historical schedule disparities?

    Reply
    1. J. Cross (Post author)

      We don’t. It’s definitely a good idea for the future and I think we could probably get away with one recursion since the adjustment is unlikely to be that dramatic, I’d think.

      In Sullivan’s analysis, I’m wondering if AL teams are getting a bump simply b/c of the DH spot is one more place to get WAR and if you removed the DH WAR the leagues would look more similar.

      Reply
      1. MP

        Thinking a little more about it, I suppose by definition you are already including some weighted average of past schedule difficulty when you make your projections. That is, a pitcher in the AL East will usually have worse peripherals than his “true skill” — even in a hypothetically neutral park — simply because the AL East plays so tough on average. So, in theory, any projection system ignoring SOS will tend to under-rate players moving from a reliably tough division (over past 3 years) to an easier division, and vice versa. For players remaining in the same division (i.e., most players), the current system of not adjusting for schedule effects is probably fine.

        Reply
        1. MP

          The ideal methodology would probably be to calc. a player’s weighted avg. past schedule difficulty (across multiple teams, if necessary), compare to his projected current season SOS, and adjust his projection accordingly.

          What would *really* be interesting (and challenging) would be to calc. a player’s past SOS by looking at the actual batters or pitchers he faced (weighted by PA of course), to come up with schedule-neutral stats, which would be used to arrive at a schedule-neutral stat projection. Then apply current-season projected SOS to get the actual projection. I honestly feel like that might be the next big step for projection systems.

          Reply
          1. J. Cross (Post author)

            That would be cool. Definitely something to work towards in future years. The first thing we want to neutralize for, I think, is catcher framer skill, but we have some work to do in order to get there.

  2. Ben

    In terms of placing proper value on pitching rate stats in my league (K/9, K/BB, ERA, WHIP, BAA), I’m trying to weight my Z-values for those by innings pitched, so that somebody with more IP gets more credit, since he affects more games. My formula for this has been to multiply the initial Z-value by (Individual IP/201), since 201 was the maximum IP in the Steamer projections (Wainwright). For example,

    However, when I do this, top relievers like Kimbrel/Chapman/Jansen take massive hits in value; Kimbrel goes from being valued just outside the top 10 overall to being behind the likes of Hyun-Jin Ryu, Ian Kennedy and Matt Garza, while Jansen slips behind Scott Kazmir. I feel like this is too big a drop, but I’m not sure how to mitigate this without overbalancing. Thoughts?

    Reply
    1. Adam

      What sample are you getting the mean and standard deviation from for your z-scores?

      If you’re only using the top 100-150 pitchers or so to calculate z-scores, then elite relievers like Kimbrel should have astronomical z-scores on ERA* (z = 3.55 in my data; 1.86 projected ERA). Elite starters like Strasburg are much lower (z = 1.03; 2.94 projected ERA). So, even when you weight the standardized score by IP (and I just multiply directly), Kimbrel with his 70 or so IP (70 x 3.55 = 248.5) still comes out ahead of Strasburg and his 180 IP (180 x 1.03 = 185.4). I then recalculate z-scores for the weighted ratios so they can be aggregated with other categories.

      *This assumes that you’ve reverse scored raw ERA.

      Reply
      1. Ben

        Adam:

        The problem isn’t that Kimbrel’s ERA value isn’t still high; it’s that his OVERALL value (sum of all categories) dips dramatically. My league has these pitching categories:

        IP, W, ERA, SO, SV, HD, QS, BAA, WHIP, K/9, K/BB.

        As you can see, with five rate stats, the impact over more IP is huge. Am I twisting data to fit conclusions, or am I right to think Kimbrel is significantly more valuable in my league than Ryu/Kennedy/Garza?

        Reply
        1. DLS

          So the way I see it:

          Neutral Categories: ERA, BAA, WHIP, K/9, K/BB
          Starter Categories: IP,W,QS,SO
          Reliever Categories: SV,HD

          The reason SO is a starter category is simply due to having more opportunities to striking people out, so top flight starters have necessarily better SO numbers than top flight closers.

          The thing about Kimbrel (and other top flight closers) though is that their SO numbers are actually competitive with mid tier and even 2nd tier pitching, logging 100+Ks over the season. Kimbrel is also dominant in the neutral categories, performing better than most starters, even when accounting for lower IP contribution. This is why it feels like Kimbrel should at least be valued within the top pitching tiers, because his contribution feels strong.

          Using the maximum IP to modify rate stats will effectively give starters an extra category worth of weight, since closers will likely fall below the mean. What I’d suggest is limiting your population of available players for scoring and then creating a mean of IP, and use that to normalize your rate stats. The reason you have to limit the population is that there is a large number of tracked pitchers with very low IP, so it ends up skewing the mean. A good way to limit the population is to use the population provided by your fantasy league provider. This type of normalization will tend to exaggerate extreme outliers a bit, but you can probably make a case that the best of the best should be exaggerated.

          It’s also worth noting that if you’re using your Z-Scores to effectively measure pitchers against batters, you should apply the same methodology to batting rate scores (AVG, OBP, SLG, OPS), else batters will be flatter with respect to the pitchers.

          Reply
  3. Adam

    Gosh, I play in a league with some niche stats (Fielding Percentage, Quality Starts, Holds). It would be awesome to get your projections for FPCT. Do you calculate and share those?

    Great, great work, by the way.

    Reply
    1. J. Cross (Post author)

      Thanks. We don’t do any of those 3 right now, I’m afraid. We were thinking about adding QS and holds in the future. I’d never even considered FPCT though — I’m actually hoping that disappears from the stat landscape.

      Reply

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