2026 Projections






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  • General Projections
    Steamer:
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  • Overall - No Split
  • vs LHP
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  • Steamer (RoS):
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  • Overall - No Split
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  • Context Neutral
    Steamer:
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  • Overall - No Split
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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
    Steamer:
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  • Overall - No Split
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  • Steamer Ros:
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  • Overall - No Split
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  • Steamer
    (Context Neutral):
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  • Steamer RoS
    (Context Neutral):
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  • Overall - No Split
  • vs LHP
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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
    2Juan SotoNYM15567839109992118.4%17.9%.262.284.273.409.535.402161-0.248.6-13.95.9
    3Fernando Tatis Jr.SDP1496482999802610.9%19.8%.209.301.270.355.479.3591311.925.9-1.54.7
    4Ronald Acuña Jr.ATL14462928105772513.6%21.4%.214.324.280.385.494.3791452.235.7-10.64.7
    5Kyle TuckerLAD1436203094882313.6%14.9%.230.274.269.371.498.3721401.130.5-6.54.6
    6Corbin CarrollARI1396062697773210.6%21.1%.234.291.257.344.491.3561294.725.4-3.24.3
    7Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    8James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    9Brandon NimmoTEX14361421787299.4%22.0%.173.294.250.330.423.328112-0.38.5-6.92.3
    10Seiya SuzukiCHC133573247380710.7%25.2%.207.306.255.337.462.344121-1.013.2-10.82.2
    11Kyle StowersMIA13054324657849.9%28.3%.208.304.243.326.451.335114-0.48.4-4.82.2
    12Wilyer AbreuBOS125505226470810.1%24.8%.206.289.245.324.451.333110-0.35.4-2.82.0
    13Lawrence ButlerATH134553217164188.5%26.1%.183.294.240.306.423.3161021.32.5-1.32.0
    14Matt WallnerMIN117481246063510.6%30.7%.234.283.226.326.460.340118-0.89.2-8.21.8
    15Teoscar HernándezLAD13255426718366.3%26.3%.205.311.259.310.464.332113-1.07.2-9.21.7
    16Sal FrelickMIL13154786653198.1%14.2%.107.294.261.327.368.307970.8-1.2-1.31.6
    17Bryan ReynoldsPIT14663218737458.7%24.1%.160.314.253.326.413.322104-0.72.4-8.41.5
    18Ramón LaureanoSDP12551119666487.1%26.6%.184.304.245.311.429.321105-0.82.3-5.31.5
    19Kerry CarpenterDET11547323616926.3%23.9%.221.287.252.308.473.333114-0.87.0-9.41.4
    20Addison BargerTOR11547017566048.1%24.3%.183.285.239.305.422.314101-0.50.2-3.01.4
    21Jung Hoo LeeSFG1365678665497.6%11.2%.126.289.266.327.392.3151010.41.0-7.21.3
    22Daylen LileWSN133551116659146.6%17.8%.150.309.266.320.416.3191040.63.4-9.21.3
    23Jo AdellCLE14056830688296.8%27.4%.222.271.233.296.456.322105-0.53.1-10.21.3
    24Dylan CrewsWSN107440125246207.0%22.2%.151.280.236.300.387.301911.0-3.5-0.51.1
    25Jesús SánchezTOR10341314504898.4%23.9%.172.292.244.311.416.3161030.11.4-5.31.1
    26Dylan BeaversBAL983921148411011.0%24.1%.159.292.238.324.397.3161040.11.8-5.11.0
    27Tyler O'NeillBAL93383184849510.0%27.8%.205.277.231.315.436.324109-0.24.0-7.41.0
    28Mike YastrzemskiATL115462165651511.0%23.0%.183.268.229.318.411.317102-0.50.9-6.61.0
    29Chase DeLauterCLE10746013535039.0%18.7%.149.276.243.313.392.30898-0.7-1.8-4.31.0
    30Adolis GarcíaPHI135562246878116.6%25.9%.190.275.234.290.423.307940.1-3.8-6.10.9
    #NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
    1Aaron JudgeNYY14563847108112917.9%24.6%.325.326.290.422.615.432182-1.560.0-6.27.7
    2Juan SotoNYM15567839109992118.4%17.9%.262.284.273.409.535.402161-0.248.6-13.95.9
    3Fernando Tatis Jr.SDP1496482999802610.9%19.8%.209.301.270.355.479.3591311.925.9-1.54.7
    4Ronald Acuña Jr.ATL14462928105772513.6%21.4%.214.324.280.385.494.3791452.235.7-10.64.7
    5Kyle TuckerLAD1436203094882313.6%14.9%.230.274.269.371.498.3721401.130.5-6.54.6
    6Corbin CarrollARI1396062697773210.6%21.1%.234.291.257.344.491.3561294.725.4-3.24.3
    7Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    8James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    9Brandon NimmoTEX14361421787299.4%22.0%.173.294.250.330.423.328112-0.38.5-6.92.3
    10Seiya SuzukiCHC133573247380710.7%25.2%.207.306.255.337.462.344121-1.013.2-10.82.2
    11Kyle StowersMIA13054324657849.9%28.3%.208.304.243.326.451.335114-0.48.4-4.82.2
    12Wilyer AbreuBOS125505226470810.1%24.8%.206.289.245.324.451.333110-0.35.4-2.82.0
    13Lawrence ButlerATH134553217164188.5%26.1%.183.294.240.306.423.3161021.32.5-1.32.0
    14Matt WallnerMIN117481246063510.6%30.7%.234.283.226.326.460.340118-0.89.2-8.21.8
    15Teoscar HernándezLAD13255426718366.3%26.3%.205.311.259.310.464.332113-1.07.2-9.21.7
    16Sal FrelickMIL13154786653198.1%14.2%.107.294.261.327.368.307970.8-1.2-1.31.6
    17Bryan ReynoldsPIT14663218737458.7%24.1%.160.314.253.326.413.322104-0.72.4-8.41.5
    18Ramón LaureanoSDP12551119666487.1%26.6%.184.304.245.311.429.321105-0.82.3-5.31.5
    19Kerry CarpenterDET11547323616926.3%23.9%.221.287.252.308.473.333114-0.87.0-9.41.4
    20Addison BargerTOR11547017566048.1%24.3%.183.285.239.305.422.314101-0.50.2-3.01.4
    21Jung Hoo LeeSFG1365678665497.6%11.2%.126.289.266.327.392.3151010.41.0-7.21.3
    22Daylen LileWSN133551116659146.6%17.8%.150.309.266.320.416.3191040.63.4-9.21.3
    23Jo AdellCLE14056830688296.8%27.4%.222.271.233.296.456.322105-0.53.1-10.21.3
    24Dylan CrewsWSN107440125246207.0%22.2%.151.280.236.300.387.301911.0-3.5-0.51.1
    25Jesús SánchezTOR10341314504898.4%23.9%.172.292.244.311.416.3161030.11.4-5.31.1
    26Dylan BeaversBAL983921148411011.0%24.1%.159.292.238.324.397.3161040.11.8-5.11.0
    27Tyler O'NeillBAL93383184849510.0%27.8%.205.277.231.315.436.324109-0.24.0-7.41.0
    28Mike YastrzemskiATL115462165651511.0%23.0%.183.268.229.318.411.317102-0.50.9-6.61.0
    29Chase DeLauterCLE10746013535039.0%18.7%.149.276.243.313.392.30898-0.7-1.8-4.31.0
    30Adolis GarcíaPHI135562246878116.6%25.9%.190.275.234.290.423.307940.1-3.8-6.10.9
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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.