2026 Projections






ZiPS:
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  • Steamer:
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  • General Projections
    Steamer:
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer (RoS):
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Context Neutral
    Steamer:
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer (RoS)
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • 600 PA / 200 IP
    3-Year
    Historical Projections
    Members Exclusive Data
    ZiPS:
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  • 2025
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  • 2012
  • 2011
  • 2010
  • Steamer:
    Select
  • 2025
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  • Splits - Steamer Projections
    Members Exclusive Data
    Steamer:
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer Ros:
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer
    (Context Neutral):
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer RoS
    (Context Neutral):
    Select
  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
  • vs LHB
  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Data Export [Members Only]
    #NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
    1Tarik SkubalDET14702929188.010.901.830.88.28477.1%2.722.695.9
    2Paul SkenesPIT12803030185.010.772.230.74.29176.4%2.752.665.7
    3Garrett CrochetBOS14802929183.011.042.320.96.29476.2%3.052.935.1
    4Logan WebbSFG131003030195.18.612.040.70.31273.3%3.293.015.0
    5Cristopher SánchezPHI12802929188.08.742.200.78.29774.3%3.193.144.6
    6Chris SaleATL11602626157.011.062.360.95.30075.0%3.183.023.9
    7Yoshinobu YamamotoLAD12702626161.09.782.640.88.28273.9%3.253.213.8
    8Cole RagansKCR10802626155.211.173.190.97.29473.8%3.433.223.8
    9Jesús LuzardoPHI12902929176.09.912.761.10.29073.0%3.643.533.6
    10Max FriedNYY14802929180.18.542.640.83.28774.0%3.323.433.6
    11Framber ValdezDET131002929185.08.402.970.77.29172.8%3.493.503.6
    12Dylan CeaseTOR11902929169.010.653.431.09.29373.6%3.703.583.6
    13Hunter BrownHOU12902929179.09.752.920.99.29073.9%3.493.493.6
    14Sonny GrayBOS12902828168.09.512.341.11.30371.7%3.813.503.5
    15Jacob deGromTEX10702626158.010.132.051.23.27974.1%3.443.433.5
    16Bryan WooSEA13802929183.19.081.881.19.26673.3%3.413.573.4
    17Logan GilbertSEA11802828167.210.072.001.19.27973.4%3.433.383.3
    18Ranger SuarezBOS11902727164.28.112.550.92.29972.6%3.683.603.3
    19Joe RyanMIN111002828168.09.912.061.35.28571.8%3.823.723.2
    20George KirbySEA12902828166.08.811.701.10.29271.5%3.623.453.1
    21Kevin GausmanTOR111002929178.18.822.581.20.28771.7%3.913.833.1
    22Zack WheelerPHI9502121130.010.392.411.05.28674.6%3.303.283.0
    23Kyle BradishBAL8702525140.19.722.871.02.29172.8%3.613.533.0
    24Nathan EovaldiTEX11802525153.18.732.191.06.28271.1%3.663.582.9
    25Freddy PeraltaNYM12902929164.210.033.321.19.27873.3%3.763.872.9
    26Drew RasmussenTBR10802828152.28.232.301.01.28271.8%3.623.622.8
    27Michael KingSDP10802727156.19.393.161.17.28774.4%3.733.922.8
    28Chase BurnsCIN7702822124.210.632.831.20.29374.7%3.593.532.8
    29José SorianoLAA101002929171.08.363.870.77.29172.2%3.773.842.7
    30Brandon WoodruffMIL10702424141.29.542.431.28.27872.7%3.743.792.7
    #NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
    1Tarik SkubalDET14702929188.010.901.830.88.28477.1%2.722.695.9
    2Paul SkenesPIT12803030185.010.772.230.74.29176.4%2.752.665.7
    3Garrett CrochetBOS14802929183.011.042.320.96.29476.2%3.052.935.1
    4Logan WebbSFG131003030195.18.612.040.70.31273.3%3.293.015.0
    5Cristopher SánchezPHI12802929188.08.742.200.78.29774.3%3.193.144.6
    6Chris SaleATL11602626157.011.062.360.95.30075.0%3.183.023.9
    7Yoshinobu YamamotoLAD12702626161.09.782.640.88.28273.9%3.253.213.8
    8Cole RagansKCR10802626155.211.173.190.97.29473.8%3.433.223.8
    9Jesús LuzardoPHI12902929176.09.912.761.10.29073.0%3.643.533.6
    10Max FriedNYY14802929180.18.542.640.83.28774.0%3.323.433.6
    11Framber ValdezDET131002929185.08.402.970.77.29172.8%3.493.503.6
    12Dylan CeaseTOR11902929169.010.653.431.09.29373.6%3.703.583.6
    13Hunter BrownHOU12902929179.09.752.920.99.29073.9%3.493.493.6
    14Sonny GrayBOS12902828168.09.512.341.11.30371.7%3.813.503.5
    15Jacob deGromTEX10702626158.010.132.051.23.27974.1%3.443.433.5
    16Bryan WooSEA13802929183.19.081.881.19.26673.3%3.413.573.4
    17Logan GilbertSEA11802828167.210.072.001.19.27973.4%3.433.383.3
    18Ranger SuarezBOS11902727164.28.112.550.92.29972.6%3.683.603.3
    19Joe RyanMIN111002828168.09.912.061.35.28571.8%3.823.723.2
    20George KirbySEA12902828166.08.811.701.10.29271.5%3.623.453.1
    21Kevin GausmanTOR111002929178.18.822.581.20.28771.7%3.913.833.1
    22Zack WheelerPHI9502121130.010.392.411.05.28674.6%3.303.283.0
    23Kyle BradishBAL8702525140.19.722.871.02.29172.8%3.613.533.0
    24Nathan EovaldiTEX11802525153.18.732.191.06.28271.1%3.663.582.9
    25Freddy PeraltaNYM12902929164.210.033.321.19.27873.3%3.763.872.9
    26Drew RasmussenTBR10802828152.28.232.301.01.28271.8%3.623.622.8
    27Michael KingSDP10802727156.19.393.161.17.28774.4%3.733.922.8
    28Chase BurnsCIN7702822124.210.632.831.20.29374.7%3.593.532.8
    29José SorianoLAA101002929171.08.363.870.77.29172.2%3.773.842.7
    30Brandon WoodruffMIL10702424141.29.542.431.28.27872.7%3.743.792.7
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    • ZiPS:ZiPS Projections courtesy of Dan Szymborski
    • ZiPS DC:ZiPS Projections pro-rated to Depth Charts playing time
    • Steamer:Steamer Projections courtesy of steamerprojections.com
    • Depth Charts:FanGraphs Depth Chart projections are a combination of ZiPS and Steamer projections with playing time allocated by our staff.
    • ATC:ATC Projections courtesy of Ariel Cohen
    • THE BAT:THE BAT projections courtesy of Derek Carty. DFS version of THE BAT available at RotoGrinders. Sports betting version of THE BAT available at EV Analytics
    • THE BAT X:THE BAT X projections courtesy of Derek Carty. DFS version of THE BAT X available at RotoGrinders. Sports betting version of THE BAT X available at EV Analytics
    • OOPSY:OOPSY projections courtesy of Jordan Rosenblum with Depth Charts playing time, and OOPSYPeak use neutral playing time. Other flavors available at scoutthestatline.com. Stuff+ courtesy of Eno Sarris.

    • On-Pace - Every Game Played:Please note, these are not projections. They represent a player's current seasons stats pro-rated for the remaining games in the season if they were to play in every single remaining game*. This is not how a player will actually perform the rest of the season, and should not be used for anything other than your own personal amusement. (*Starters pitch every 4.5 days and relievers pitch every 2.5 days.)
    • On-Pace - Games Played %:Please note, these are not projections. They represent a player's current seasons stats prorated for the remaining games in the season if they were to play the same percentage of total games they have already played this season. This is not how a player will actually perform the rest of the season, and should not be used for anything other than your own personal amusement.
    • RoS:Rest of Season
    • Update:Updated In-Season

    • ADP:ADP data provided courtesy of National Fantasy Baseball Championship
    • Inter-Projection Standard Deviation (InterSD):The standard deviation of the underlying projections surrounding the ATC average auction value. InterSD describes how much the projections disagree about the value of a player. The larger the InterSD, the more projections differ.
    • Inter-Projection Skewness (InterSK):The skewness of the underlying projections surrounding the ATC average auction value. InterSK describes the symmetry of the underlying projections. A positive InterSK means that a player’s mean is being pulled to the upside; the majority of projections are lower than the ATC average. A negative InterSK means that a player’s mean is being pulled to the downside; the majority of projections are higher than the ATC average.
    • Intra-Projection Standard Deviation (IntraSD):The standard deviation of a player’s categorical Z-Scores. IntraSD is a measure of the dimension of a player’s statistical profile. The smaller the IntraSD, the more balanced the individual player’s category contributions are. The larger the IntraSD, the more unbalanced the player’s category contributions are.