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Transaction Oracle
— A Timely Look at Transactions as They Happen
— A Timely Look at Transactions as They Happen
Sunday, October 04, 2009
2010 ZiPS Projections - New York Yankees
Obviously, not a lot goes wrong wrong for teams that win 103 games. The Yankees were no exception, essentially improving offensively at every position from 2008. Even the things that did go wrong, like Joba Chamberlain not being a star this year, weren't exactly deal-breakers.The Yankees scored the most runs this season (unless the Angels score 37 more runs in their game against the A's, which they're losing 2-1 in the 3rd as I type this) and even though the offense is fairly old, they talent is simply too deep and the position that may need to be fixed soonest, catcher, is the position Montero plays, at least right now. Not that Posada stinks or anything, but backstops pushing 40 aren't exactly known for gentle declines.
The team has a few more questions with the pitching, most notably just what to do with Hughes long-term after his excellent stint in the bullpen and what to make of Wang. It's not a deep free agent pool for starters, so I wouldn't be surprised if the team mostly trolls for relievers after the big Sabathia/Burnett haul last winter.
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Offensive Projections Name P Age AVG OBP SLG G AB R H 2B 3B HR RBI BB K SB CS OPS+ Alex Rodriguez 3b 34 .281 .382 .526 128 477 86 134 25 1 30 93 69 106 15 3 141 Mark Teixeira# 1b 30 .280 .369 .505 147 574 95 161 38 2 29 112 75 111 1 0 132 Hideki Matsui* dh 36 .277 .360 .464 111 390 54 108 20 1 17 64 49 58 1 1 119 Derek Jeter ss 36 .303 .372 .424 139 571 88 173 29 2 12 60 56 86 19 5 114 Johnny Damon* lf 36 .272 .350 .436 130 507 87 138 29 3 16 61 60 83 18 3 110 Nick Swisher# rf 29 .243 .354 .446 147 511 87 124 30 1 24 81 85 137 1 1 113 Robinson Cano* 2b 27 .296 .334 .474 160 614 82 182 42 5 19 88 31 71 3 4 110 Shelley Duncan rf 30 .252 .328 .460 116 417 59 105 22 1 21 72 45 100 3 1 109 Xavier Nady rf 31 .275 .326 .454 85 313 51 86 18 1 12 61 19 67 1 1 107 Jorge Posada# c 38 .256 .336 .430 77 270 33 69 15 1 10 46 32 68 1 0 104 Eric Hinske* rf 32 .233 .324 .424 105 257 45 60 14 1 11 40 31 68 3 1 99 Jesus Montero c 20 .273 .315 .416 115 454 47 124 24 1 13 61 26 78 0 0 94 Melky Cabrera# cf 25 .266 .324 .393 155 519 63 138 27 3 11 64 43 67 11 3 92 Juan Miranda* 1b 27 .246 .317 .391 120 447 52 110 24 1 13 62 44 111 1 1 89 John Rodriguez* lf 32 .244 .321 .391 84 266 39 65 13 1 8 38 25 70 0 1 90 Brett Gardner* cf 26 .253 .328 .344 119 387 66 98 14 6 3 31 42 75 32 7 81 Cody Ransom 3b 34 .233 .304 .401 106 347 54 81 20 1 12 51 33 90 6 2 87 Jerry Hairston Jr. 3b 34 .252 .305 .378 101 294 53 74 17 1 6 37 21 45 8 3 82 Reegie Corona# 2b 23 .250 .313 .347 136 507 72 127 27 2 6 42 46 89 15 4 78 Chris Stewart c 28 .260 .323 .344 84 262 33 68 14 1 2 24 20 36 1 1 80 Kevin Russo 2b 25 .260 .312 .351 94 365 46 95 17 2 4 31 26 65 10 5 78 Francisco Cervelli c 24 .255 .307 .380 61 184 24 47 9 1 4 22 12 37 0 2 83 Freddy Guzman# cf 29 .251 .303 .330 119 446 75 112 16 5 3 33 33 64 44 11 70 Colin Curtis* lf 25 .244 .297 .347 132 513 61 125 23 3 8 50 36 101 5 3 73 Austin Jackson cf 23 .245 .296 .338 138 551 65 135 25 4 6 56 38 136 16 4 70 Austin Romine c 21 .247 .285 .367 118 458 53 113 24 2 9 52 23 92 5 3 74 P.J. Pilittere c 28 .262 .299 .336 83 301 33 79 14 1 2 31 14 34 0 1 70 Doug Bernier# ss 30 .224 .315 .313 96 281 40 63 12 2 3 30 33 75 2 1 70 Eric Duncan* 3b 25 .233 .280 .353 113 408 44 95 20 1 9 45 26 93 2 2 69 Ramiro Pena# ss 24 .249 .296 .332 93 382 47 95 17 3 3 32 25 78 5 5 69 Brian Peterson c 30 .237 .293 .320 52 169 20 40 8 0 2 20 13 37 0 1 65 Jose Molina c 35 .227 .275 .308 68 185 20 42 9 0 2 17 11 39 1 0 57 Kevin Cash c 32 .208 .277 .309 53 149 14 31 6 0 3 18 14 47 0 0 57 Total .258 .324 .397 0 127 178 329 66 60 33 162 115 251 23 80 2936 Defensive Projections Name CThr 1b 2b 3b ss lf cf rf Rodriguez Av Teixeira# Av Matsui* Pr Jeter Av Damon* Vg Pr Swisher# Av Av Fr Av Cano* Fr Duncan Av Fr Fr Nady Av Av Posada# Pr Fr Hinske* Av Pr Av Av Montero Pr Cabrera# Vg Av Vg Miranda* Av Rodriguez* Fr Fr Gardner* Vg Vg Ransom Av Av Av Fr Hairston Fr Av Fr Av Av Av Corona# Vg Av Stewart Vg Russo Av Av Cervelli Av Guzman# Vg Vg Curtis* Av Av Av Jackson Av Av Romine Av Piliterre Fr Fr Bernier# Av Av Av Av Av Av Duncan* Fr Fr Fr Fr Pena# Vg Vg Vg Peterson Fr Molina Av Cash Av * - Bats Left # - Switch Hitter ODDIBE (Odds of Important Baseball Events) Name PO EX VG AV FR PO COMP 1 COMP 2 COMP 3 RodriguezAlex 3B 84% 12% 3% 1% 0% BoyerKen SchmidtMike MoraMelvin TeixeiraMark 1B 35% 44% 15% 6% 1% MurrayEddie DavisChili OrtizDavid JeterDerek SS 69% 21% 7% 2% 1% RizzutoPhil AparicioLuis ApplingLuke MatsuiHideki DH 13% 32% 27% 23% 4% FairlyRon BainesHarold MartinezTino CanoRobinson 2B 45% 19% 16% 13% 7% OrtaJorge VidroJose WalkerTodd DamonJohnny LF 20% 29% 21% 18% 12% GoslinGoose SlaughterEnos VeachBobby SwisherNick RF 14% 26% 24% 23% 14% EvansDwight BurroughsJeff DeerRob DuncanShelley RF 10% 23% 22% 25% 20% BrunanskyTom BuhnerJay DyeJermaine NadyXavier RF 5% 22% 25% 28% 20% GonzalezJuan YoungbloodJoel GuillenJose PosadaJorge C 25% 40% 21% 10% 3% SchangWally PiazzaMike HartnettGabby HinskeEric RF 4% 9% 16% 29% 43% NunnallyJon LowensteinJohn BrunanskyTom MonteroJesus C 4% 31% 37% 24% 3% RodriguezIvan CardonaJavier AlomarSandy CabreraMelky CF 5% 10% 28% 38% 20% AlmadaMel GallagherDave CrispCoco GardnerBrett CF 3% 13% 34% 37% 13% PrietoChris RobertsDave ButlerBrett MirandaJuan 1B 0% 1% 4% 27% 68% FalconeDave McAnultyPaul StahoviakScott RodriguezJohn LF 1% 3% 5% 15% 76% StahlLarry KempSteve HollandsworthTodd RansomCody 3B 1% 5% 14% 28% 51% BooneRay DurhamRay ClaytonRoyce HairstonJerry 3B 0% 3% 10% 26% 60% BarnesSkeeter O'RourkeFrank BrookensTom GuzmanFreddy CF 1% 3% 16% 38% 41% BrownAdrian RedmanTike RobertsDave CoronaReegie 2B 1% 3% 8% 25% 64% PhillipsTony ThomasDerrel CrespoFelipe StewartChris C 0% 3% 14% 44% 38% TaylorZack WilsonCraig KluttzClyde CervelliFrancisco C 0% 0% 4% 29% 67% TorrealbaYorvit DempseyPat BellorinEdwin RussoKevin 2B 0% 1% 4% 16% 78% BloomquistWillie BarfieldJosh LansingMike JacksonAustin CF 0% 0% 3% 18% 79% TerreroLuis RepkoJason MoranJavon RomineAustin C 0% 1% 5% 29% 66% HearnEd CotaHumberto MathisJeff CurtisColin LF 0% 0% 0% 1% 99% SwannPedro VazRoberto MillerDavid PilittereP.J. C 0% 0% 3% 24% 73% EncarnacionAngelo TaylorZack PhillipsPaul BernierDouglas SS 0% 0% 5% 20% 75% SchofieldDick RungePaul KoppeJoe DuncanEric 3B 0% 0% 0% 2% 97% HansonTravis FrostadEmerson BakerDave PenaRamiro SS 0% 0% 2% 12% 86% WeissWalt MeloJuan OlmedoRay PetersonBrian C 0% 0% 1% 6% 92% TillmanBob HundleyRandy MahoneyMike MolinaJose C 0% 0% 1% 4% 95% DifeliceMike KnorrRandy SantiagoBenito CashKevin C 0% 0% 1% 4% 95% KnorrRandy ChavezRaul DifeliceMike Name .300 BA .375 OBP .500 SLG 140 OPS+ 45 2B 10 3B 30 HR 30 SB RodriguezAlex 23% 61% 66% 47% 0% 0% 49% 0% TeixeiraMark 22% 41% 48% 27% 17% 0% 41% 0% JeterDerek 56% 44% 4% 8% 3% 0% 1% 6% MatsuiHideki 21% 27% 18% 11% 0% 0% 2% 0% CanoRobinson 49% 8% 29% 13% 40% 1% 11% 0% DamonJohnny 18% 20% 11% 8% 3% 1% 6% 4% SwisherNick 2% 19% 14% 9% 4% 0% 18% 0% DuncanShelley 4% 5% 19% 5% 0% 0% 9% 0% NadyXavier 20% 5% 14% 2% 0% 0% 0% 0% PosadaJorge 9% 11% 11% 4% 0% 0% 0% 0% HinskeEric 2% 5% 6% 2% 0% 0% 0% 0% MonteroJesus 12% 0% 1% 0% 0% 0% 0% 0% CabreraMelky 9% 2% 0% 0% 1% 1% 0% 0% GardnerBrett 4% 3% 0% 0% 0% 10% 0% 70% MirandaJuan 1% 0% 0% 0% 0% 0% 0% 0% RodriguezJohn 3% 4% 2% 1% 0% 0% 0% 0% RansomCody 1% 0% 3% 0% 0% 0% 0% 0% HairstonJerry 6% 1% 0% 0% 0% 0% 0% 0% GuzmanFreddy 3% 0% 0% 0% 0% 7% 0% 98% CoronaReegie 2% 1% 0% 0% 0% 0% 0% 0% StewartChris 9% 4% 0% 0% 0% 0% 0% 0% CervelliFrancisco 9% 2% 0% 0% 0% 0% 0% 0% RussoKevin 8% 1% 0% 0% 0% 0% 0% 0% JacksonAustin 1% 0% 0% 0% 0% 2% 0% 0% RomineAustin 1% 0% 0% 0% 0% 0% 0% 0% CurtisColin 0% 0% 0% 0% 0% 0% 0% 0% PilittereP.J. 10% 0% 0% 0% 0% 0% 0% 0% BernierDouglas 0% 2% 0% 0% 0% 0% 0% 0% DuncanEric 0% 0% 0% 0% 0% 0% 0% 0% PenaRamiro 2% 0% 0% 0% 0% 0% 0% 0% PetersonBrian 3% 1% 0% 0% 0% 0% 0% 0% MolinaJose 2% 0% 0% 0% 0% 0% 0% 0% CashKevin 1% 1% 0% 0% 0% 0% 0% 0% Extrapolated Career Statistics Name BA OBP SLG G AB R H 2B 3B HR RBI BB SO HP SB CS OPS+ RodriguezAlex .294 .384 .550 2960 11273 2184 3311 585 32 745 2207 1489 2426 222 379 88 141 TeixeiraMark .276 .365 .502 2293 8837 1417 2439 565 31 456 1650 1147 1800 134 26 3 126 PosadaJorge .273 .370 .471 1894 6425 955 1752 400 13 283 1142 955 1547 73 19 18 120 MatsuiHideki .282 .361 .469 1412 5099 768 1439 283 17 212 863 623 751 34 17 13 119 JeterDerek .310 .380 .444 2832 11532 1993 3578 578 68 276 1322 1155 1917 183 389 106 117 SwisherNick .238 .348 .438 1841 6374 1052 1514 360 15 295 950 1050 1687 74 17 17 109 CanoRobinson .291 .327 .457 2271 8888 1221 2582 589 45 267 1115 443 1034 63 54 67 106 DamonJohnny .283 .352 .434 2702 10644 1851 3009 573 109 272 1231 1119 1419 51 447 111 104 Pitching Statistics - Starters Name Age ERA W L G GS INN H ER HR BB K ERA+ C.C. Sabathia* 29 3.48 17 9 34 34 235.1 215 91 23 58 201 130 Joba Chamberlain 24 4.21 8 5 38 26 147.1 143 69 17 63 136 107 A.J. Burnett 33 4.46 12 11 31 31 195.2 186 97 25 86 178 102 Andy Pettitte* 38 4.48 11 11 31 31 190.2 200 95 21 67 130 101 Chad Gaudin 27 5.02 10 9 40 24 154.1 167 86 18 69 126 90 Sergio Mitre 29 5.07 6 5 23 19 108.1 129 61 14 31 64 89 Zachary McAllister 22 5.08 8 10 24 24 124.0 139 70 17 45 66 89 Chien-Ming Wang 30 5.13 6 8 19 17 105.1 114 60 10 40 58 88 Ian Kennedy 25 5.22 4 4 17 16 81.0 83 47 9 40 62 87 Ivan Nova 23 5.87 6 11 25 24 130.1 156 85 16 76 64 77 Josh Towers 33 6.11 5 8 25 16 101.2 130 69 19 29 50 74 Kei Igawa* 30 6.39 5 12 25 24 132.1 158 94 30 54 75 71 Pitching Statistics - Relievers Name Age ERA W L G GS INN H ER HR BB K ERA+ Mariano Rivera 40 3.18 5 2 62 0 62.1 53 22 7 13 64 141 David Robertson 25 3.84 6 3 54 0 70.1 59 30 5 38 81 120 Philip Hughes 24 4.32 7 5 40 12 93.2 93 45 11 33 84 105 Edwar Ramirez 29 4.62 3 3 52 0 64.1 60 33 8 38 68 97 Alfredo Aceves 27 4.70 5 4 62 0 76.2 81 40 10 23 55 95 Brian Bruney 28 4.72 3 2 52 2 47.2 47 25 5 29 40 94 Phil Coke* 27 4.91 4 3 79 0 69.2 72 38 9 29 57 93 Mark Melancon 25 4.96 4 3 47 0 74.1 78 41 9 29 55 92 Damaso Marte* 35 4.97 3 2 53 0 41.2 41 23 5 17 40 92 Jonathan Albaladejo 27 5.06 7 6 74 1 90.2 100 51 13 36 62 89 Humberto Sanchez 27 5.32 2 2 19 3 23.2 25 14 3 13 15 85 Michael Dunn* 25 5.50 4 4 44 0 75.1 76 46 10 57 69 83 Eric Wordekemper 26 5.61 2 3 40 0 51.1 60 32 8 26 28 80 Romulo Sanchez 26 5.69 3 6 39 6 68.0 72 43 10 39 44 80 Kevin Whelan 26 5.91 2 3 39 2 56.1 53 37 7 57 49 77 Zachary Kroenke* 26 6.14 2 5 42 1 58.2 64 40 10 41 37 74 Jose Valdez 27 6.16 3 5 42 2 57.0 62 39 10 40 40 74 Grant Duff 27 7.07 3 9 40 10 85.1 103 67 16 68 44 64 * - Throws Left ODDIBE (Odds of Important Baseball Events) Player PO TOP MID BOT COMP 1 COMP 2 COMP 3 RiveraMariano RP 75% 23% 2% JonesDoug HoffmanTrevor AndersenLarry SabathiaC.C. SP 89% 10% 0% KoufaxSandy GuidryRon CandelariaJohn RobertsonDavid RP 46% 36% 18% ManteiMatt WilliamsonScott LittellMark ChamberlainJoba SP 46% 41% 12% MaloneyJim RijoJose PizarroJuan HughesPhilip RP 24% 46% 30% BantaJack CaudillBill AyalaBobby BurnettA.J. SP 30% 58% 12% NomoHideo ClemensRoger StottlemyreTodd PettitteAndy SP 23% 64% 13% RogersKenny KeyJimmy ReussJerry RamirezEdwar RP 22% 36% 42% NelsonJoe SimpsonAllan DohmannScott AcevesAlfredo RP 19% 30% 50% FoulkeKeith DeLeonLuis BellHeath BruneyBrian RP 24% 28% 49% BalfourGrant NelsonJoe LaCorteFrank CokePhil RP 15% 31% 54% LittlefieldDick AyalaBobby GuardadoEddie MelanconMark RP 17% 30% 53% AyalaBobby YorkJim BeardDave MarteDamaso RP 20% 32% 48% PercivalTroy DotelOctavio GordonTom GaudinChad SP 19% 40% 41% DarwinDanny WeaverJim GreggHal AlbaladejoJonathan RP 12% 28% 59% BelindaStan LaddPete JohnsonKen MitreSergio SP 20% 35% 46% PavanoCarl EthertonSeth EatonAdam McAllisterZachary SP 7% 54% 39% LittellMark BurdickStacey JohnsonJoe WangChien-Ming SP 9% 47% 44% WojnaEd KisonBruce HamiltonJoey KennedyIan SP 20% 30% 50% HardenRich D'AcquistoJohn FoppertJesse SanchezHumberto RP 16% 29% 54% JonesGordon CoombsDanny DeanPaul DunnMichael RP 10% 21% 69% WilliamsMitch MeyerJack BruneyBrian SanchezRomulo RP 1% 15% 84% MarquezJeff ValdezCarlos PattersonJeff WordekemperEric RP 1% 19% 80% CrowDean RoehlScott HinesCarlos NovaIvan SP 0% 15% 85% WellsJared RitzKevin MoehlerBrian WhelanKevin RP 1% 12% 87% BowlesBrian VoylesBrad van BurenJermaine TowersJosh SP 5% 18% 77% SchmidtDave SmithsonMike TollbergBrian KroenkeZachary RP 0% 7% 93% FeshSean NanceShane DuquetteBryan ValdezJose RP 0% 6% 94% WilmetPaul MiadichBart VillanoMike IgawaKei SP 0% 6% 94% OlinSteve YoungCurt LorraineAndrew DuffGrant RP 0% 0% 100% SextonJeff NelsonJeff HolmanBrad Player 130 ERA+ 100 ERA+ K/9 >8 BB/9 <2 HR/9 <1 RiveraMariano 68% 96% 85% 62% 62% SabathiaC.C. 55% 97% 34% 32% 73% RobertsonDavid 41% 75% 85% 1% 85% ChamberlainJoba 17% 69% 57% 1% 52% HughesPhilip 18% 59% 51% 8% 61% BurnettA.J. 6% 59% 57% 0% 38% PettitteAndy 3% 54% 2% 1% 52% RamirezEdwar 19% 50% 71% 1% 51% AcevesAlfredo 17% 43% 17% 27% 51% BruneyBrian 19% 46% 40% 1% 61% CokePhil 13% 42% 34% 5% 53% MelanconMark 15% 40% 21% 8% 47% MarteDamaso 20% 46% 59% 8% 59% GaudinChad 4% 35% 32% 0% 56% AlbaladejoJonathan 10% 36% 13% 9% 41% MitreSergio 6% 33% 1% 27% 47% McAllisterZachary 1% 23% 0% 1% 31% WangChien-Ming 1% 24% 0% 1% 76% KennedyIan 8% 32% 20% 1% 62% SanchezHumberto 16% 46% 13% 11% 52% DunnMichael 9% 28% 53% 0% 44% SanchezRomulo 1% 10% 0% 0% 28% WordekemperEric 1% 15% 0% 0% 30% NovaIvan 0% 3% 0% 0% 46% WhelanKevin 0% 10% 35% 0% 52% TowersJosh 1% 11% 1% 24% 22% KroenkeZachary 0% 5% 0% 0% 18% ValdezJose 0% 3% 2% 0% 15% IgawaKei 0% 1% 1% 0% 1% DuffGrant 0% 0% 0% 0% 7% Extrapolated Career Statistics Player W L S ERA G GS IP H ER HR BB SO ERA+ BurnettA.J. 158 142 0 4.22 408 404 2580.0 2411 1209 277 1089 2372 105 PettitteAndy 268 172 0 4.07 573 563 3557.7 3762 1609 319 1146 2592 115 RiveraMariano 82 56 608 2.37 1038 10 1206.0 955 317 72 282 1132 190 SabathiaC.C. 290 181 0 3.80 618 618 4113.3 3985 1735 410 1167 3380 120 All figures in % based on projection playing time 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 2009. ZiPS is projecting equivalent production - a .240 ZiPS projection may end up being .280 in AAA or .300 in AA, for example. Whether or not a player will play is one of many non-statistical factors one has to take into account when predicting the future. Players are listed with their most recent teams unless Dan has made a mistake. This is very possible as a lot of minor-league signings are generally unreported in the offseason. ZiPS is projecting based on the AL having a 4.46 ERA and the NL having a 4.41 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. Positional offense is ranked by RC/27 and divided into quintiles based on what the most frequent starting players at each position did in 2007-2009. Excellent is the top quintile, Very Good the 2nd quintile and so on.
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Doug Jones, Age 40 - 80.1 IP, 2.02 ERA, 231 ERA+.
That line looks pretty much like a standard Rivera season, and there aren't many 40 year old closers to compare against.
What's the avg OPS+ for catchers? Romine projecting to 74 already seems very encouraging.
Can't wait for the Red Sox 2010 DIPS that shows Buchholz with a 1.03 ERA.
What would you expect when 8 of 9 position player starters had a 2009 OPS+ above their career averages?
Can't wait for the Red Sox 2010 DIPS that shows Buchholz with a 1.03 ERA.
An Oriole fan writing a projection system with a pro-Red Sox bias would be a rather odd choice, I imagine.
Dan, I have what I'm sure is going to be shocking news for you. There are a certain number of Yankee fans who have used the last eight years of no title to fuel one of the most intensely wacky persecution complexes in the history of psychiatry.
I think that Jeter projection is pretty good, 114 OPS+ and average defense at SS. That's a very valuable season. Of course I'd love him to hit .330 again but I would take that projection.
I'm surprised about Melancon's awful projection. I know he's struggled in Major League time but his minor league numbers are really good. Are his control issues in the Majors bad enough to make his projection that low or are his minor league numbers not as impressive as I think they are? To some extent my question also applies to Robertson.
I like the rotation depth the Yanks are going to have. Gaudin, Mitre, Wang, Kennedy and MCAllister are all reasonable enough 5th starters and some of them have some pretty high upside for 6th-10th starters.
I hope this year is the beginning of a more consistent stretch for Cano and that in 10 years we look back and wonder what the hell happened to him in 08.
Can't wait for the Red Sox 2010 DIPS that shows Buchholz with a 1.03 ERA.
This is annoying because it's exactly what the posters here expect from Yankee fans and it's just going to lead to more anti-Yankee posts and distract from actually talking about the team.
Buhner, though, was a middle of the order threat on a playoff team, and remained such for a couple years. Why does he come up as a comp for a minor league slugger?
Give that man the 10,000 dollars!
tex: 907 + 948
a-rod: 941 - 933
jete: 791 + 871
posada: 836 + 885
cano: 808 + 871
damon: 800 + 854
matsui: 844 + 874
cabrera: 703 + 752
swisher: 835 + 869
absent a-rod, every starting player is 30-80 points better than their projections. is this all park-related?
Well, if that's all it is, I take back every nasty thing I've ever said about the park, including its financing. But though their .858 / .821 home / road OPS+ split shows that their new Stadium didn't hurt, what does a similar .859 / .753 split for the Red Sox say about the way that Boston hitters use Fenway as a crutch?
First, the baseline for Duncan is a little higher - ZiPS/zMLE have Duncan as a 114 OPS+ in a neutral context.
There's also the issues that I calculated exact component park factors (rather than generalized ones) and with a slightly easier league, Buhner's 126 OPS+ over those years in question drops to 120 OPS+ in a neutral context.
Even that 6 is actually one of the bigger differences among the comps - remember, shape matters, too. If I have a 115 OPS+ hulking slugger, I want a group that contains 110 and 120 OPS+ hulking sluggers, not Carl Crawford!
Some more on Duncan's list:
Jason Lane
Kurt Airoso
Bubba Trammell
Richie Zisk
Carmelo Martinez
Aaron Guiel
Jeff Burroughs
Wally Post
Ed Spiezio
Jerry Morales
Dusty Baker
other Brian Giles
Rob Deer
Andy Kosco
Joe Vitiello
Sam Mele
Willie Kirkland
Dick Gernert
Gene Schall
Chuck Workman
Vic Wertz
Greg Vaughn
Johnny Rizzo
Mike Lum
Cecil Fielder
Ron Kittle
Jack Voigt
Ozzie Timmons
Deron Johnson
Ryan Radmanovich
Ruben Sierra
Richard Hidalgo
Jerry Martin
Dan Ford
Dave Kingman
Ernie Young
Dustan Mohr
Steve Balboni
Babe Dahlgren
And on and on.
absent a-rod, every starting player is 30-80 points better than their projections. is this all park-related?
Part park, part league scoring more than I thought, part everybody but ARod playing better than usual. As noted above, considering that 8 of the 9 regulars played above their career averages, I should have hit low.
He's less than 600 hits away at this point and if he just has 2 full seasons of 150 hits, (a weak total for him and he's coming off the best year of his career), he can probably limp the rest of the way as a part-timer if he has to. He's played 140 games for 14 years in a row now, so he's really been able to pile up the hits.
Assuming Biggio gets in, if he makes it to 3000, Damon will probably be the first 3K player not to make the Hall, with the exception of those not in for other reasons (Rose of course and very likely Palmeiro).
Assuming Biggio gets in, if he makes it to 3000, Damon will probably be the first 3K player not to make the Hall, with the exception of those not in for other reasons (Rose of course and very likely Palmeiro).
If Damon gets 3000 hits, I think he gets into the hof, he'll be compared to Brock enough and his superior defense (even with the weak arm) will be enough to get him in.
Maybe eventually, but it's a tough crowd now. When Tim Raines gets 1/3 of what he needs for qualification, there ain't going to be many who are going to "feel" like Johnny Damon is a HOFer.
20 years ago, sure. It's pretty scary that it only took some knee problems to keep Buckner from getting 3000 hits. Now that would be comical.
I think he has it harder than Brock did, but he is going to get compared to Brock, no matter whether that is the best comparison, and in almost every aspect in that comparison Damon is going to grade out better.
cairo: 855
chone: 847
hbt: 860
marcel: 856
pecota: 822
zips: 883
So don't pick on Szym too much. If anything, pick on PECOTA.
Will he? It's getting late out here, so I won't take a deep look at the numbers, but Brock retired with the single-season and career records for stolen bases. For "Fame", that counts for a lot.
I'd say Brock is perhaps the one guy who is non-Hall of Meriter, Hall of Famer, with both groups getting it right.
I don't see Damon having much of a chance of making it (absent more seasons like this one), 3,000 hits or not. Of course, I'm perhaps the most disbelieving of automatic markers here.
The Book Blog thread explaining Regression
Important reading.
Please, please, please don't let Michael Kay find out the Scooter is Jeter's #1 comp.
The Robinson Cano comps have me thinking Jorge Cantu must be #4. :-)
2001 OPS+: 109
10/14/01: Breaks leg
2002 OPS+: 110
2003 OPS+: 38
Yes, he was - better than his contemporary Ray Schalk, who is in the HOF on the strength of being one of the "Clean Sox" of 1919 (and Warren Brown's pushing).
-- MWE
Important reading.
From that link:
The first highlighted passage is mgl's. The second highlighted passage is one that needs more elaboration. In this case, I'm specifically wondering how those numbers for Phil Hughes were arrived at, numbers which seem to imply a mean that's a lot worse than what his performance this year (his first full injury free year) might project. What assumptions were you making about injuries and managerial schizophrenia when you project him to a 4.32 ERA, with 12 starts in 40 games?
I'm obviously missing something here, but what is it? How do you take the sum of Phil Hughes's minor league and Major League career and come up with a line like that? Seems to me that it has to be based on an expectation of another major injury somewhere along the road.
ZiPS assumes that a player's recent usage reflects his future usage. A human can better guess how Hughes will in fact be used.
As for the 4.32 ERA, that reflects ZiPS not knowing about the rib and muscle issues. I found back when I started doing this that removing seasons in which a pitcher spent at least 30 days on the DL made projections significantly less accurate - the injury performance doesn't go away because players with an injury have an increased risk of being injured. So what amounts to a 6.27 ERA in 2008 is part of his record.
Ugh. This again. MGL should never write about statistics. Let's break it down:
1. any "player will always regress" WRONG. The notion of "always" is antithetical to statistics.
2. "on the average" CONTRADICTS ANY MEANING OF THE WORD "ALWAYS"
3. "towards average" WRONG
....... really, that first sentence is just horrible.
4. "the chances of him being worse is <sic> greater than the chances of him being better" CONTRADICTS #1 AND #3 which clearly state he has no chance of being better (unless he was below average)
5. "ALWAYS" -- wrong (antithetical) but hard to know what this is referring to given the preceding series of conflicting statements
6. "as long as we properly define the mean for the player" -- AYE, THERE'S THE RUB
We don't know what Chipper Jones' "true" mean is. But if we did know Chipper's "true" mean, he would be every bit as likely to over-perform as under-perform that mean (give or take a bit of skew in the true distribution -- i.e. it's not necessarily normal but it's probably close enough).
As to the rest ... a player whose true mean is a 110 OPS+ but who has hit for a 100 OPS+ the last 3 years will, OF COURSE!! ALWAYS!! CHANCES ARE!!, regress towards his true 110 OPS+ -- i.e. he will regress away from average. The projection systems will, on average, under-predict such a player because the projection systems won't know he's a true 110 OPS+ player. That's not a big deal but it's a fact.
Regression towards the mean is really the wrong concept because we don't know what the mean is for any individual player. In a sense, we don't even predict a mean for the individual player. A better explanation of how projection systems work would be something like the following (admittedly short on specifics):
From 2007-2009, 8 players with at least 1200 PA had an OPS+ of 140 or better. However, from 1901-2009, only 57 hitters have had a 140 OPS+ for their careers (minimum PA of 4000). Even if you limit it to ages 25-32 (at least 3000 PA), you only get 90 hitters. Therefore we assume/estimate that it would be incredibly unlikely that the unknown distribution of true talent levels would produce 8 true 140 OPS+ hitters at one time. Therefore some members of this group must be over-performing their (unknown) true talent so we estimate that, on average, members of this group will regress towards an OPS+ of 100. That is, as a group they averaged something like a 150 OPS+ but we'll project the group to average about a 135-140 OPS+ in 2010. We really haven't a clue what any individual member of that group will do (even Teixeira has a 1% chance of being "poor" next year) and we know that it is highly likely that, if only by chance, one or more of them will hit better in 2010 than they did from 2007-2009.
Of course the projections adjust for other predictive factors (most importantly age) and they don't use OPS+, but that's the gist of it.
Or if you prefer ... regression towards the mean would be the correct concept if every player in baseball had the same mean -- i.e. came from the same population. We are very highly confident however that Albert Pujols is not in the same population as Aaron Miles. So the task becomes figuring out which population a given player most likely belongs to. The main drivers behind our guess are the player's recent performance and age. Unfortunately, even using 3-4 seasons, these are small samples to detect small differences in performance that are so important in baseball. So we have a great deal of uncertainty around a mean estimated purely by recent performance and age.
The question which follows is -- do we think that performance-age mean estimate is an unbiased predictor of the player's true mean? That's not an easy question to answer but history and earlier statistical analysis do quite clearly show that such a mean is biased. It is biased upwards for those who performed above average (in the overall league sense) and biased downward for those below average.
But let's be clear about what's "really" going on. There are a group of players who come from the "true 120 OPS+" population. Over the last few years, a few of these guys (say 20%) had an OPS+ between 90 and 110 -- they will all be projected to about a 100 OPS+. 30% were between 110 and 120 and they'll be projected to about a 110 OPS+ (everybody loses 5 points towards average). 30% were between 120 and 130 and they'll be projected to about a 120 OPS+. And 20% were between 130 and 150 and they'll be projected to about a 130-135 OPS+. You'll notice the projection system has correctly projected only 30% of that population to their true mean.
In the subsequent year, those "true 120" guys who were projected to a 100 will be considered "true 100" flukes after putting up a 120 season and will get projected to about a 105 OPS+ even though, were we omnisicient, we'd have no reason to expect them not to repeat their performance.
This is one thing about projection systems -- they can't identify "true 120" players who have underperformed the last 3-4 years. They see them as guys who have put up a 100ish OPS+ and will remain at a 100ish OPS+. They handle average or better players who overperformed their true means fine (by regressing them towards 100); they handle average or worse players who underperformed their true means fine (by regressing them towards 100). But above-average players who underperformed their true means and below-average players who overperformed are not being projected correctly because they can't be identified.
That's not a fault of the projection systems necessarily. But the key is that they treat the mean estimate (after regression) as "true" rather than incorporating the substantial amount of uncertainty that there is in that projection. Unfortunately, the uncertainty introduced into a projection just from basic randomness of a season's worth of PAs is already huge (again, see Teixeira ... or Swisher who could basically perform at any level) and adding in the uncertainty of our mean estimate only shows how little we do know about individual players. And I don't know what can be done to try to improve that mean estimate other than possibly incorporating some biometric data (e.g. eye tracking ability), maybe psychometric, or things like pitch f/x (and what comes after).
By the way, how many of you knew that Matt Holliday is one of the 8 players in that 140+ group? The full list, in order: Pujols, AROD, Mauer, Teixeira, Berkman, Holliday, Pena, Gonzalez. I would have guessed at least those last 3 would have been in the 130s.
ZiPS assumes that a player's recent usage reflects his future usage. A human can better guess how Hughes will in fact be used.
As for the 4.32 ERA, that reflects ZiPS not knowing about the rib and muscle issues. I found back when I started doing this that removing seasons in which a pitcher spent at least 30 days on the DL made projections significantly less accurate - the injury performance doesn't go away because players with an injury have an increased risk of being injured. So what amounts to a 6.27 ERA in 2008 is part of his record.
IOW it's an educated guess that derives from a large database of past cases rather than an educated but non-provable belief (based on how he's performed since his recovery) that the injury was of a flukish sort. That's reasonable, and thanks for the explanation, even if I'd still bet on Hughes far outperforming that projection.
One thing I noticed, Teixeira shows as projecting to "AV" D at first, but has a 79% chance of being VG or EX according to ODDIBE . . . that doesn't make sense to me . . .
Tell me ZiPS - are you by chance a ... pleasure model?
You missed Chipper, Hanley, Manny, and perhaps a few others too. But it doesn't change the main point.
I like to refer to the "classic" case of regression to the mean, at least the one I recall most in introductions to the subject, characteristics of offspring. Tall fathers tend to have tall sons (compared to average height), and short fathers tend to have short sons. But tall fathers tend to have sons who are shorter than them, and short fathers have sons who are taller than them.
It is other words, the second random variable (son's height, or year X+1 OPS), conditioned on the first (father's height, or composite of OPS up to year X) will tend to regress part way toward the overall mean.
A couple key things: when projecting, you're conditioning on a random variable, not a known constant. "Tend to" doesn't mean "always." Even if we conditioned on true talent, we'd see some hitters not regressing toward that true talent. And, of course, players get better or worse all the time, so true talent is not constant across time.
I have Teixeira's defense as AV/66 at 1B for 2010 (I projected him at +6 runs range-wise in 2009 for a VG and +3 runs in 2010 for an AV)
The 79% EX or VG are his odds to be in the top 40% of starters in offense in 2010.
Pretty much! I'd certainly do better with a time machine, but if I had a time machine, I'd probably be executing sweet Back to the Future II scams, not tweaking projections!
I think the 79% is the likelihood of his offensive production being among the top 40% of first basemen.
Edit: Only 12 minutes late! Is that like, a 12 pack of cokes?
Sabathia's top comp might be scarier, given the contract length.
In fact, everyone's going to have some scary comps simply because bad things happen a lot to great pitchers, good pitchers, average pitchers, and bad pitchers. That ZiPS sees 154 wins left in Sabathia is a pretty good thing!
This would have to be quite the Madoff-like catastrophe.
(raises hand)
The guy that did surprise me in that group was Carlos Pena.
-- MWE
The top cutoff for the POOR 1B group is roughly an OPS+ of 94.
Pretty much everyone, no matter how good, has some established chance at having a terrible year. A-Rod's 0% there is actually 1-in-243, so it certainly could happen.
2007 really pulls that multi year average up.
FWIW BBREf PI says there are 14 playesr with 1200+ PAS, and OPS+ over 140 from 2007-09:
Cnt Player OPS+ PA From To+----+-----------------+----+-----+----+----+
1 Albert Pujols 178 2020 2007 2009
2 Alex Rodriguez 158 1837 2007 2009
3 Chipper Jones 152 1730 2007 2009
4 Prince Fielder 150 2094 2007 2009
5 Mark Teixeira 149 1967 2007 2009
6 Hanley Ramirez 147 2051 2007 2009
7 Manny Ramirez 147 1654 2007 2009
8 Joe Mauer 145 1704 2007 2009
9 Matt Holliday 144 2006 2007 2009
10 Lance Berkman 143 1895 2007 2009
11 Carlos Pena 142 1789 2007 2009
12 Ryan Braun 141 1863 2007 2009
13 Adrian Gonzalez 141 2101 2007 2009
14 Miguel Cabrera 140 2043 2007 2009
Of the 14, Pena is alone in that he has just 1 year over 130, that would seem to indicate that 140 is likely not his true talent level.
Hmmm ... I used PI too ... I must have accidentally set a criterion I didn't mean to set.
Anyway, the explanation in #51 is clear and simple. The key point I want to add regarding projection systems is that they are regression towards the mean conditional on past performance (usually a weighted function of the last 3-4 years) plus age and a few other variables. And that's fine -- I don't mean to question their utility as standard sorts of models. The problem comes in when they are interpreted as regression towards the player-specific mean.
Dan's presentation (and PECOTA's and I suppose most of them) are good in that they are displaying at least some of the uncertainty. What I'm not clear about is how that uncertainty is being estimated (feel free to link me if you want Dan).
I treat aging and regression toward the mean separately.
He's an old players skills type (low BA, lots of walks, lots of Ks), so maybe ZiPs sees him petering out soon.
Also, this was just another (good) season for him - it's certainly not clearly his best.
That's certainly a possibility and the only reason that I can think of to explain the projection.
Also, this was just another (good) season for him - it's certainly not clearly his best.
His wOBA this year was .375, his next highest was .368 back in 2006. Maybe 'clearly' was too strong, but this is the best offensive season that he's had in his career. His defense was a bit better in 2006, but that shouldn't affect the offensive projections.
not kewl!
I note that his top comp is Dwight Evans, and Evans had his best offensive seasons from 29-32 and was productive for a long time after that. Burroughs, too, had a couple of productive seasons between 29-31, although he fell off much more rapidly than did Evans. It's very possible that Swisher could be very productive over the next year or two and then fall away fairly quickly.
-- MWE
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