2026 Projections






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  • General Projections
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    Steamer:
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  • Overall - No Split
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  • Overall - No Split
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  • 600 PA / 200 IP
    3-Year
    Historical Projections
    Members Exclusive Data
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  • Splits - Steamer Projections
    Members Exclusive Data
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  • Overall - No Split
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  • Steamer Ros:
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  • Data Export [Members Only]
    #NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
    1Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    2Wyatt LangfordTEX1436132484742311.7%23.4%.198.296.251.343.449.3431221.317.2-2.83.6
    3Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    4Riley GreeneDET14863028828948.9%28.0%.207.322.257.327.464.340118-0.812.8-5.53.0
    5Steven KwanCLE14663897954178.9%9.1%.100.289.272.340.372.3151031.13.23.02.9
    6Jackson ChourioMIL145629238784246.2%20.3%.191.306.269.317.460.3321142.012.4-7.12.7
    7James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    8Taylor WardBAL148630278083510.3%24.4%.200.283.242.325.442.332115-1.49.6-7.52.4
    9Jose AltuveHOU138600207868128.4%17.1%.162.289.262.329.424.327110-0.46.8-3.82.4
    10Cody BellingerNYY138592227779118.3%15.0%.183.268.257.320.440.3261090.26.3-2.82.4
    11Tyler SoderstromATH14058524727858.6%23.5%.195.300.256.325.451.334114-0.79.2-5.92.4
    12Ian HappCHC144626218075812.5%23.6%.177.288.240.339.417.331112-0.78.3-6.62.3
    13Brandon NimmoTEX14361421787299.4%22.0%.173.294.250.330.423.328112-0.38.5-6.92.3
    14Kyle StowersMIA13054324657849.9%28.3%.208.304.243.326.451.335114-0.48.4-4.82.2
    15Randy ArozarenaSEA151651238673249.8%26.2%.175.289.233.329.408.324114-0.210.7-12.12.1
    16Jarren DuranBOS142615178270238.0%23.5%.178.321.260.327.438.3311083.19.2-9.92.1
    17Colton CowserBAL128513216462139.5%31.5%.189.300.227.310.417.3161040.62.8-0.32.1
    18Brandon MarshPHI121475135852119.7%28.1%.156.340.256.330.412.3231050.33.4-3.91.6
    19Ramón LaureanoSDP12551119666487.1%26.6%.184.304.245.311.429.321105-0.82.3-5.31.5
    20Lars NootbaarSTL116493155850611.8%20.2%.160.281.242.335.402.323107-0.23.7-6.61.4
    21Davis SchneiderTOR86327144039412.2%28.3%.195.273.220.322.415.323108-0.32.7-0.91.3
    22Harrison BaderSFG122482135750136.5%23.7%.141.297.243.303.384.301910.1-4.91.41.3
    23Alec BurlesonSTL13857520657366.9%14.3%.165.283.268.321.433.326109-1.24.7-11.61.3
    24Daylen LileWSN133551116659146.6%17.8%.150.309.266.320.416.3191040.63.4-9.21.3
    25Gavin LuxTBR11144675044310.0%22.1%.114.317.252.328.366.30896-0.5-2.5-0.21.3
    26Heliot RamosSFG14560921737167.4%24.2%.164.307.253.319.417.320104-0.92.2-11.61.1
    27Dylan BeaversBAL983921148411011.0%24.1%.159.292.238.324.397.3161040.11.8-5.11.0
    28Austin MartinMIN81311336241310.4%17.5%.094.303.252.340.346.308960.3-1.2-0.90.9
    29Isaac CollinsKCR108412849411311.2%22.5%.134.294.236.330.369.310980.2-0.8-4.60.9
    30Nathan LukesTOR813156373437.9%16.0%.124.291.257.319.381.30796-0.4-1.7-1.10.8
    #NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
    1Yordan AlvarezHOU131559297886312.6%16.9%.248.305.291.386.540.389153-1.533.7-13.94.0
    2Wyatt LangfordTEX1436132484742311.7%23.4%.198.296.251.343.449.3431221.317.2-2.83.6
    3Roman AnthonyBOS1416101985681112.3%25.2%.176.338.266.360.441.350121-0.115.2-6.83.0
    4Riley GreeneDET14863028828948.9%28.0%.207.322.257.327.464.340118-0.812.8-5.53.0
    5Steven KwanCLE14663897954178.9%9.1%.100.289.272.340.372.3151031.13.23.02.9
    6Jackson ChourioMIL145629238784246.2%20.3%.191.306.269.317.460.3321142.012.4-7.12.7
    7James WoodWSN1466302483811611.7%29.6%.195.344.258.347.453.3451220.216.7-12.82.5
    8Taylor WardBAL148630278083510.3%24.4%.200.283.242.325.442.332115-1.49.6-7.52.4
    9Jose AltuveHOU138600207868128.4%17.1%.162.289.262.329.424.327110-0.46.8-3.82.4
    10Cody BellingerNYY138592227779118.3%15.0%.183.268.257.320.440.3261090.26.3-2.82.4
    11Tyler SoderstromATH14058524727858.6%23.5%.195.300.256.325.451.334114-0.79.2-5.92.4
    12Ian HappCHC144626218075812.5%23.6%.177.288.240.339.417.331112-0.78.3-6.62.3
    13Brandon NimmoTEX14361421787299.4%22.0%.173.294.250.330.423.328112-0.38.5-6.92.3
    14Kyle StowersMIA13054324657849.9%28.3%.208.304.243.326.451.335114-0.48.4-4.82.2
    15Randy ArozarenaSEA151651238673249.8%26.2%.175.289.233.329.408.324114-0.210.7-12.12.1
    16Jarren DuranBOS142615178270238.0%23.5%.178.321.260.327.438.3311083.19.2-9.92.1
    17Colton CowserBAL128513216462139.5%31.5%.189.300.227.310.417.3161040.62.8-0.32.1
    18Brandon MarshPHI121475135852119.7%28.1%.156.340.256.330.412.3231050.33.4-3.91.6
    19Ramón LaureanoSDP12551119666487.1%26.6%.184.304.245.311.429.321105-0.82.3-5.31.5
    20Lars NootbaarSTL116493155850611.8%20.2%.160.281.242.335.402.323107-0.23.7-6.61.4
    21Davis SchneiderTOR86327144039412.2%28.3%.195.273.220.322.415.323108-0.32.7-0.91.3
    22Harrison BaderSFG122482135750136.5%23.7%.141.297.243.303.384.301910.1-4.91.41.3
    23Alec BurlesonSTL13857520657366.9%14.3%.165.283.268.321.433.326109-1.24.7-11.61.3
    24Daylen LileWSN133551116659146.6%17.8%.150.309.266.320.416.3191040.63.4-9.21.3
    25Gavin LuxTBR11144675044310.0%22.1%.114.317.252.328.366.30896-0.5-2.5-0.21.3
    26Heliot RamosSFG14560921737167.4%24.2%.164.307.253.319.417.320104-0.92.2-11.61.1
    27Dylan BeaversBAL983921148411011.0%24.1%.159.292.238.324.397.3161040.11.8-5.11.0
    28Austin MartinMIN81311336241310.4%17.5%.094.303.252.340.346.308960.3-1.2-0.90.9
    29Isaac CollinsKCR108412849411311.2%22.5%.134.294.236.330.369.310980.2-0.8-4.60.9
    30Nathan LukesTOR813156373437.9%16.0%.124.291.257.319.381.30796-0.4-1.7-1.10.8
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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, 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.