2026 Projections






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  • General Projections
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  • Overall - No Split
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  • Steamer (RoS):
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  • Context Neutral
    Steamer:
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  • Overall - No Split
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  • 3-Year
    Historical Projections
    Members Exclusive Data
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  • Splits - Steamer Projections
    Members Exclusive Data
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  • Steamer Ros:
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  • Overall - No Split
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  • Steamer
    (Context Neutral):
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  • Data Export [Members Only]
    #NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
    1Aaron JudgeNYY14563847108112917.9%24.6%.325.326.290.422.615.432182-1.560.0-6.27.7
    2Shohei OhtaniLAD149660461211092413.9%24.2%.315.313.283.386.598.4101671.253.2-14.06.3
    3Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    4Adley RutschmanBOS138559176464110.7%15.9%.159.273.251.334.410.325110-1.25.27.13.2
    5Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    6Kyle SchwarberPHI1506574197104614.8%28.1%.266.260.230.351.495.362132-1.923.2-17.32.8
    7Rafael DeversSFG150651308791212.7%24.6%.214.296.251.352.465.349124-1.517.3-12.02.8
    8Brent RookerATH15063932869569.4%25.4%.231.311.264.339.495.356129-0.921.3-16.62.7
    9Ben RiceNYY13254127747549.9%21.1%.225.268.247.331.473.346122-0.913.4-9.12.3
    10Mike TroutLAA118502246664313.6%29.8%.219.297.234.346.453.344120-1.011.0-5.82.3
    11George SpringerTOR1315682482711410.2%19.3%.198.285.260.342.458.3461230.716.4-13.52.3
    12Yandy DíazTBR14261619737329.3%15.0%.158.314.287.358.444.349124-1.716.0-14.52.3
    13Seiya SuzukiCHC133573247380710.7%25.2%.207.306.255.337.462.344121-1.013.2-10.82.2
    14Salvador PerezKCR14157727618805.1%20.5%.198.272.250.300.448.319104-2.8-0.42.52.2
    15Iván HerreraSTL12954317686799.9%20.1%.160.312.268.353.428.342120-0.212.6-10.42.1
    16Christian YelichMIL1355751974741611.0%23.9%.162.317.258.344.420.3331141.210.9-12.61.8
    17Hunter GoodmanCOL13755527698526.0%26.1%.226.296.252.303.478.333100-0.8-0.7-1.31.7
    18Carter JensenKCR10639812464749.6%25.0%.169.299.241.316.410.316102-0.20.52.11.6
    19Colt KeithDET12349315625538.8%20.9%.165.300.257.325.423.3241080.14.5-5.71.6
    20Kyle ManzardoCLE12852325607029.6%23.8%.216.268.238.318.454.331114-0.78.0-11.51.5
    21Jorge PolancoNYM12350721616859.2%20.5%.188.274.247.320.435.326109-0.84.5-7.61.4
    22Kerry CarpenterDET11547323616926.3%23.9%.221.287.252.308.473.333114-0.87.0-9.41.4
    23Gavin LuxTBR11144675044310.0%22.1%.114.317.252.328.366.30896-0.5-2.5-0.21.3
    24Samuel BasalloBAL10942716505637.8%24.4%.178.285.240.305.418.312101-0.50.1-3.01.2
    25Ryan O'HearnPIT12451114615939.2%18.8%.147.305.264.338.411.326107-1.23.2-8.91.2
    26Marcell Ozuna124520226268012.2%24.2%.187.286.242.338.429.334113-1.66.2-13.41.0
    27Chase DeLauterCLE10746013535039.0%18.7%.149.276.243.313.392.30898-0.7-1.8-4.31.0
    28Agustín RamírezMIA124519186265137.2%19.7%.176.272.244.305.420.31399-0.2-0.8-7.01.0
    29Josh BellMIN130540206064110.2%18.6%.171.272.247.328.417.325108-2.03.0-13.40.8
    30Trevor LarnachMIN10342613504939.9%23.5%.158.296.245.324.404.317102-0.80.4-8.00.7
    #NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
    1Aaron JudgeNYY14563847108112917.9%24.6%.325.326.290.422.615.432182-1.560.0-6.27.7
    2Shohei OhtaniLAD149660461211092413.9%24.2%.315.313.283.386.598.4101671.253.2-14.06.3
    3Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    4Adley RutschmanBOS138559176464110.7%15.9%.159.273.251.334.410.325110-1.25.27.13.2
    5Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    6Kyle SchwarberPHI1506574197104614.8%28.1%.266.260.230.351.495.362132-1.923.2-17.32.8
    7Rafael DeversSFG150651308791212.7%24.6%.214.296.251.352.465.349124-1.517.3-12.02.8
    8Brent RookerATH15063932869569.4%25.4%.231.311.264.339.495.356129-0.921.3-16.62.7
    9Ben RiceNYY13254127747549.9%21.1%.225.268.247.331.473.346122-0.913.4-9.12.3
    10Mike TroutLAA118502246664313.6%29.8%.219.297.234.346.453.344120-1.011.0-5.82.3
    11George SpringerTOR1315682482711410.2%19.3%.198.285.260.342.458.3461230.716.4-13.52.3
    12Yandy DíazTBR14261619737329.3%15.0%.158.314.287.358.444.349124-1.716.0-14.52.3
    13Seiya SuzukiCHC133573247380710.7%25.2%.207.306.255.337.462.344121-1.013.2-10.82.2
    14Salvador PerezKCR14157727618805.1%20.5%.198.272.250.300.448.319104-2.8-0.42.52.2
    15Iván HerreraSTL12954317686799.9%20.1%.160.312.268.353.428.342120-0.212.6-10.42.1
    16Christian YelichMIL1355751974741611.0%23.9%.162.317.258.344.420.3331141.210.9-12.61.8
    17Hunter GoodmanCOL13755527698526.0%26.1%.226.296.252.303.478.333100-0.8-0.7-1.31.7
    18Carter JensenKCR10639812464749.6%25.0%.169.299.241.316.410.316102-0.20.52.11.6
    19Colt KeithDET12349315625538.8%20.9%.165.300.257.325.423.3241080.14.5-5.71.6
    20Kyle ManzardoCLE12852325607029.6%23.8%.216.268.238.318.454.331114-0.78.0-11.51.5
    21Jorge PolancoNYM12350721616859.2%20.5%.188.274.247.320.435.326109-0.84.5-7.61.4
    22Kerry CarpenterDET11547323616926.3%23.9%.221.287.252.308.473.333114-0.87.0-9.41.4
    23Gavin LuxTBR11144675044310.0%22.1%.114.317.252.328.366.30896-0.5-2.5-0.21.3
    24Samuel BasalloBAL10942716505637.8%24.4%.178.285.240.305.418.312101-0.50.1-3.01.2
    25Ryan O'HearnPIT12451114615939.2%18.8%.147.305.264.338.411.326107-1.23.2-8.91.2
    26Marcell Ozuna124520226268012.2%24.2%.187.286.242.338.429.334113-1.66.2-13.41.0
    27Chase DeLauterCLE10746013535039.0%18.7%.149.276.243.313.392.30898-0.7-1.8-4.31.0
    28Agustín RamírezMIA124519186265137.2%19.7%.176.272.244.305.420.31399-0.2-0.8-7.01.0
    29Josh BellMIN130540206064110.2%18.6%.171.272.247.328.417.325108-2.03.0-13.40.8
    30Trevor LarnachMIN10342613504939.9%23.5%.158.296.245.324.404.317102-0.80.4-8.00.7
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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.