2025 Projections






ZiPS:
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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
    New! Members Exclusive Data
    ZiPS:
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  • Steamer:
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  • Splits - Steamer Projections
    New! 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
    1Paul SkenesPIT12602929173.111.202.440.75.29376.2%2.782.695.3
    2Tarik SkubalDET14702929183.110.461.920.85.28575.2%2.882.825.2
    3Zack WheelerPHI14803030192.19.632.250.99.27973.6%3.283.334.6
    4Logan WebbSFG131003030196.07.672.030.69.29871.5%3.423.234.3
    5Garrett CrochetBOS11702929153.011.712.610.93.29974.1%3.202.894.2
    6Cole RagansKCR11902929176.110.353.320.92.29073.9%3.463.403.7
    7Chris SaleATL12702626153.010.802.330.91.29774.7%3.153.023.6
    8Framber ValdezHOU14802929185.18.792.810.78.28773.5%3.333.423.5
    9Dylan CeaseSDP12903131182.110.603.351.02.28173.5%3.513.503.5
    10Logan GilbertSEA12903131193.29.261.871.15.27172.7%3.423.483.5
    11Pablo LópezMIN12903030185.19.482.211.13.29871.8%3.753.563.4
    12Jacob deGromTEX7402020110.111.391.861.04.28574.7%3.042.863.4
    13Yoshinobu YamamotoLAD11602525150.19.552.280.95.28872.2%3.423.263.3
    14Corbin BurnesARI12902929183.28.852.570.97.28672.7%3.553.573.3
    15Blake SnellLAD11602828156.211.733.941.00.27675.5%3.333.393.3
    16Justin SteeleCHC11802929167.18.742.460.91.29272.8%3.493.463.2
    17Michael KingSDP12802929169.29.863.151.01.29174.3%3.543.623.2
    18Tyler GlasnowLAD9602222134.111.162.641.06.28372.5%3.423.153.2
    19Sonny GraySTL10802626156.19.522.540.97.29572.1%3.593.413.1
    20Joe RyanMIN10802727159.09.901.971.35.28371.2%3.823.703.1
    21Cristopher SánchezPHI10802828167.07.782.390.87.29672.7%3.583.593.1
    22Max FriedNYY12802727166.18.482.590.86.28773.3%3.413.503.1
    23Tanner BibeeCLE11902929168.29.502.411.19.28773.2%3.683.713.1
    24George KirbySEA11802626152.28.411.251.10.29172.1%3.503.413.1
    25Spencer SchwellenbachATL12802828166.08.782.011.05.29171.0%3.693.533.0
    26Aaron NolaPHI12903030185.08.892.151.28.29072.1%3.893.853.0
    27Sandy AlcantaraMIA9902727172.17.812.290.97.29371.6%3.733.713.0
    28Hunter BrownHOU12902929168.19.573.151.01.29973.0%3.753.673.0
    29Spencer StriderATL10602222123.112.063.081.03.29773.0%3.473.112.9
    30Kevin GausmanTOR121003030180.18.992.591.16.29872.5%3.883.782.8
    #NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
    1Paul SkenesPIT12602929173.111.202.440.75.29376.2%2.782.695.3
    2Tarik SkubalDET14702929183.110.461.920.85.28575.2%2.882.825.2
    3Zack WheelerPHI14803030192.19.632.250.99.27973.6%3.283.334.6
    4Logan WebbSFG131003030196.07.672.030.69.29871.5%3.423.234.3
    5Garrett CrochetBOS11702929153.011.712.610.93.29974.1%3.202.894.2
    6Cole RagansKCR11902929176.110.353.320.92.29073.9%3.463.403.7
    7Chris SaleATL12702626153.010.802.330.91.29774.7%3.153.023.6
    8Framber ValdezHOU14802929185.18.792.810.78.28773.5%3.333.423.5
    9Dylan CeaseSDP12903131182.110.603.351.02.28173.5%3.513.503.5
    10Logan GilbertSEA12903131193.29.261.871.15.27172.7%3.423.483.5
    11Pablo LópezMIN12903030185.19.482.211.13.29871.8%3.753.563.4
    12Jacob deGromTEX7402020110.111.391.861.04.28574.7%3.042.863.4
    13Yoshinobu YamamotoLAD11602525150.19.552.280.95.28872.2%3.423.263.3
    14Corbin BurnesARI12902929183.28.852.570.97.28672.7%3.553.573.3
    15Blake SnellLAD11602828156.211.733.941.00.27675.5%3.333.393.3
    16Justin SteeleCHC11802929167.18.742.460.91.29272.8%3.493.463.2
    17Michael KingSDP12802929169.29.863.151.01.29174.3%3.543.623.2
    18Tyler GlasnowLAD9602222134.111.162.641.06.28372.5%3.423.153.2
    19Sonny GraySTL10802626156.19.522.540.97.29572.1%3.593.413.1
    20Joe RyanMIN10802727159.09.901.971.35.28371.2%3.823.703.1
    21Cristopher SánchezPHI10802828167.07.782.390.87.29672.7%3.583.593.1
    22Max FriedNYY12802727166.18.482.590.86.28773.3%3.413.503.1
    23Tanner BibeeCLE11902929168.29.502.411.19.28773.2%3.683.713.1
    24George KirbySEA11802626152.28.411.251.10.29172.1%3.503.413.1
    25Spencer SchwellenbachATL12802828166.08.782.011.05.29171.0%3.693.533.0
    26Aaron NolaPHI12903030185.08.892.151.28.29072.1%3.893.853.0
    27Sandy AlcantaraMIA9902727172.17.812.290.97.29371.6%3.733.713.0
    28Hunter BrownHOU12902929168.19.573.151.01.29973.0%3.753.673.0
    29Spencer StriderATL10602222123.112.063.081.03.29773.0%3.473.112.9
    30Kevin GausmanTOR121003030180.18.992.591.16.29872.5%3.883.782.8
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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. Peak version and 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.