2026 Projections






ZiPS:
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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
  • vs LHP
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  • Context Neutral
    Steamer:
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  • Overall - No Split
  • vs LHP
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  • Steamer (RoS)
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  • Overall - No Split
  • vs LHP
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  • 600 PA / 200 IP
    3-Year
    Historical Projections
    Members Exclusive Data
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  • Steamer:
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  • Splits - Steamer Projections
    Members Exclusive Data
    Steamer:
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  • Overall - No Split
  • vs LHP
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  • Steamer Ros:
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  • Overall - No Split
  • vs LHP
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  • Steamer
    (Context Neutral):
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  • Overall - No Split
  • vs LHP
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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
    3Julio RodríguezSEA152660309591287.2%22.1%.206.311.271.332.478.3461301.824.96.85.6
    4Fernando Tatis Jr.SDP1496482999802610.9%19.8%.209.301.270.355.479.3591311.925.9-1.54.7
    5Ronald Acuña Jr.ATL14462928105772513.6%21.4%.214.324.280.385.494.3791452.235.7-10.64.7
    6Kyle TuckerLAD1436203094882313.6%14.9%.230.274.269.371.498.3721401.130.5-6.54.6
    7Corbin CarrollARI1396062697773210.6%21.1%.234.291.257.344.491.3561294.725.4-3.24.3
    8Mookie BettsLAD143622218881910.7%11.6%.180.273.268.349.448.345122-0.615.54.74.2
    9Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    10Jackson MerrillSDP14360622778286.6%19.6%.190.304.269.320.459.3331140.510.36.33.8
    11Wyatt LangfordTEX1436132484742311.7%23.4%.198.296.251.343.449.3431221.317.2-2.83.6
    12Pete Crow-ArmstrongCHC147619238478335.6%24.2%.191.291.246.297.437.3141003.03.311.33.6
    13Bryce HarperPHI140607268284912.3%21.3%.214.312.272.365.486.364133-0.223.5-10.13.4
    14Byron BuxtonMIN122525277873157.8%27.2%.243.295.249.318.492.3441212.114.9-2.23.1
    15Brendan DonovanSEA13356913665858.5%13.9%.135.294.268.341.402.326116-1.19.40.73.0
    16Jazz Chisholm Jr.NYY140585288077329.6%26.6%.207.283.238.317.445.3291102.49.60.23.0
    17Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    18Riley GreeneDET14863028828948.9%28.0%.207.322.257.327.464.340118-0.812.8-5.53.0
    19Steven KwanCLE14663897954178.9%9.1%.100.289.272.340.372.3151031.13.23.02.9
    20Kyle SchwarberPHI1506574197104614.8%28.1%.266.260.230.351.495.362132-1.923.2-17.32.8
    21Andy PagesLAD140574227376106.1%22.1%.189.294.256.310.444.3251080.05.22.92.8
    22Brent RookerATH15063932869569.4%25.4%.231.311.264.339.495.356129-0.921.3-16.62.7
    23Jackson ChourioMIL145629238784246.2%20.3%.191.306.269.317.460.3321142.012.4-7.12.7
    24Michael Harris IIATL139574206971184.4%19.4%.172.299.265.301.438.3171021.32.74.02.6
    25James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    26Oneil CruzPIT1285382271652810.3%29.9%.197.307.237.319.433.3241062.76.50.12.5
    27Taylor WardBAL148630278083510.3%24.4%.200.283.242.325.442.332115-1.49.6-7.52.4
    28Cody BellingerNYY138592227779118.3%15.0%.183.268.257.320.440.3261090.26.3-2.82.4
    29Tyler SoderstromATH14058524727858.6%23.5%.195.300.256.325.451.334114-0.79.2-5.92.4
    30Ian HappCHC144626218075812.5%23.6%.177.288.240.339.417.331112-0.78.3-6.62.3
    #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
    3Julio RodríguezSEA152660309591287.2%22.1%.206.311.271.332.478.3461301.824.96.85.6
    4Fernando Tatis Jr.SDP1496482999802610.9%19.8%.209.301.270.355.479.3591311.925.9-1.54.7
    5Ronald Acuña Jr.ATL14462928105772513.6%21.4%.214.324.280.385.494.3791452.235.7-10.64.7
    6Kyle TuckerLAD1436203094882313.6%14.9%.230.274.269.371.498.3721401.130.5-6.54.6
    7Corbin CarrollARI1396062697773210.6%21.1%.234.291.257.344.491.3561294.725.4-3.24.3
    8Mookie BettsLAD143622218881910.7%11.6%.180.273.268.349.448.345122-0.615.54.74.2
    9Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    10Jackson MerrillSDP14360622778286.6%19.6%.190.304.269.320.459.3331140.510.36.33.8
    11Wyatt LangfordTEX1436132484742311.7%23.4%.198.296.251.343.449.3431221.317.2-2.83.6
    12Pete Crow-ArmstrongCHC147619238478335.6%24.2%.191.291.246.297.437.3141003.03.311.33.6
    13Bryce HarperPHI140607268284912.3%21.3%.214.312.272.365.486.364133-0.223.5-10.13.4
    14Byron BuxtonMIN122525277873157.8%27.2%.243.295.249.318.492.3441212.114.9-2.23.1
    15Brendan DonovanSEA13356913665858.5%13.9%.135.294.268.341.402.326116-1.19.40.73.0
    16Jazz Chisholm Jr.NYY140585288077329.6%26.6%.207.283.238.317.445.3291102.49.60.23.0
    17Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    18Riley GreeneDET14863028828948.9%28.0%.207.322.257.327.464.340118-0.812.8-5.53.0
    19Steven KwanCLE14663897954178.9%9.1%.100.289.272.340.372.3151031.13.23.02.9
    20Kyle SchwarberPHI1506574197104614.8%28.1%.266.260.230.351.495.362132-1.923.2-17.32.8
    21Andy PagesLAD140574227376106.1%22.1%.189.294.256.310.444.3251080.05.22.92.8
    22Brent RookerATH15063932869569.4%25.4%.231.311.264.339.495.356129-0.921.3-16.62.7
    23Jackson ChourioMIL145629238784246.2%20.3%.191.306.269.317.460.3321142.012.4-7.12.7
    24Michael Harris IIATL139574206971184.4%19.4%.172.299.265.301.438.3171021.32.74.02.6
    25James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    26Oneil CruzPIT1285382271652810.3%29.9%.197.307.237.319.433.3241062.76.50.12.5
    27Taylor WardBAL148630278083510.3%24.4%.200.283.242.325.442.332115-1.49.6-7.52.4
    28Cody BellingerNYY138592227779118.3%15.0%.183.268.257.320.440.3261090.26.3-2.82.4
    29Tyler SoderstromATH14058524727858.6%23.5%.195.300.256.325.451.334114-0.79.2-5.92.4
    30Ian HappCHC144626218075812.5%23.6%.177.288.240.339.417.331112-0.78.3-6.62.3
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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.