2025 Projections






ZiPS:
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  • Steamer:
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  • General Projections
    Steamer:
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  • Overall - No Split
  • as SP
  • as RP
  • vs RHB
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  • vs RHB as SP
  • vs LHB as SP
  • vs RHB as RP
  • vs LHB as RP
  • Steamer (RoS):
    Select
  • Overall - No Split
  • as SP
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  • vs RHB
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  • 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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  • 2024
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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
    1Kodai SengaNYM11702926150.29.923.941.06.28374.3%3.723.972.5
    2Clay HolmesNYM9704021139.28.503.490.70.29972.0%3.693.632.2
    3Sean ManaeaNYM9702525143.29.053.061.25.28070.4%4.114.111.9
    4David PetersonNYM9702724138.18.383.500.94.29973.2%3.853.981.8
    5Edwin DíazNYM533462063.213.153.230.87.29475.3%3.012.741.2
    6Tylor MegillNYM650371288.29.233.451.10.29871.6%4.083.981.0
    7Frankie MontasNYM660181789.08.533.471.23.29070.5%4.354.320.9
    8A.J. MinterNYM43253052.210.272.951.01.28675.0%3.363.430.6
    9Paul BlackburnNYM44034974.17.632.861.19.29970.1%4.364.260.6
    10Griffin CanningNYM5603316106.28.053.171.41.29070.5%4.534.590.6
    11Dedniel NúñezNYM32043048.010.263.310.98.29674.7%3.513.510.5
    12Reed GarrettNYM54257061.210.644.251.01.30774.2%3.853.810.5
    13José ButtóNYM43151162.09.033.791.04.27373.7%3.724.080.3
    14Brandon SproatNYM1105524.27.963.211.26.28770.1%4.384.430.2
    15Danny YoungNYM32048048.09.984.200.87.29771.6%3.913.910.2
    16Huascar BrazobánNYM22037041.29.084.360.91.29071.8%4.044.080.1
    17Kevin HergetNYM12024027.27.962.491.31.28971.5%4.164.240.1
    18Ryne StanekNYM43157056.010.094.221.19.28573.8%3.994.180.1
    19Brooks RaleyNYM11117016.29.013.541.13.28973.1%3.954.140.1
    20Sean Reid-FoleyNYM11023024.19.904.930.98.28773.5%3.964.130.1
    21Drew SmithNYM110909.09.353.761.29.28771.6%4.274.350.0
    22Justin HagenmanNYM11012012.08.332.861.28.29370.0%4.334.270.0
    23Grant HartwigNYM1009012.07.994.370.93.28467.6%4.554.560.0
    24Génesis CabreraNYM22039040.08.193.961.19.28572.0%4.274.520.0
    25Austin WarrenNYM00010010.27.703.601.12.29170.8%4.334.430.0
    26Chris DevenskiNYM11019020.28.483.171.54.28868.5%4.794.670.0
    27Max KranickNYM11029034.17.513.101.27.29270.3%4.454.50-0.1
    28Tyler ZuberNYM12028030.19.004.451.38.28869.2%4.854.76-0.1
    #NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
    1Kodai SengaNYM11702926150.29.923.941.06.28374.3%3.723.972.5
    2Clay HolmesNYM9704021139.28.503.490.70.29972.0%3.693.632.2
    3Sean ManaeaNYM9702525143.29.053.061.25.28070.4%4.114.111.9
    4David PetersonNYM9702724138.18.383.500.94.29973.2%3.853.981.8
    5Edwin DíazNYM533462063.213.153.230.87.29475.3%3.012.741.2
    6Tylor MegillNYM650371288.29.233.451.10.29871.6%4.083.981.0
    7Frankie MontasNYM660181789.08.533.471.23.29070.5%4.354.320.9
    8A.J. MinterNYM43253052.210.272.951.01.28675.0%3.363.430.6
    9Paul BlackburnNYM44034974.17.632.861.19.29970.1%4.364.260.6
    10Griffin CanningNYM5603316106.28.053.171.41.29070.5%4.534.590.6
    11Dedniel NúñezNYM32043048.010.263.310.98.29674.7%3.513.510.5
    12Reed GarrettNYM54257061.210.644.251.01.30774.2%3.853.810.5
    13José ButtóNYM43151162.09.033.791.04.27373.7%3.724.080.3
    14Brandon SproatNYM1105524.27.963.211.26.28770.1%4.384.430.2
    15Danny YoungNYM32048048.09.984.200.87.29771.6%3.913.910.2
    16Huascar BrazobánNYM22037041.29.084.360.91.29071.8%4.044.080.1
    17Kevin HergetNYM12024027.27.962.491.31.28971.5%4.164.240.1
    18Ryne StanekNYM43157056.010.094.221.19.28573.8%3.994.180.1
    19Brooks RaleyNYM11117016.29.013.541.13.28973.1%3.954.140.1
    20Sean Reid-FoleyNYM11023024.19.904.930.98.28773.5%3.964.130.1
    21Drew SmithNYM110909.09.353.761.29.28771.6%4.274.350.0
    22Justin HagenmanNYM11012012.08.332.861.28.29370.0%4.334.270.0
    23Grant HartwigNYM1009012.07.994.370.93.28467.6%4.554.560.0
    24Génesis CabreraNYM22039040.08.193.961.19.28572.0%4.274.520.0
    25Austin WarrenNYM00010010.27.703.601.12.29170.8%4.334.430.0
    26Chris DevenskiNYM11019020.28.483.171.54.28868.5%4.794.670.0
    27Max KranickNYM11029034.17.513.101.27.29270.3%4.454.50-0.1
    28Tyler ZuberNYM12028030.19.004.451.38.28869.2%4.854.76-0.1
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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.