2024 Projections

Data Export [Members Only]
#NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
1Spencer StriderATL15603030178.212.642.981.03.29873.0%3.393.055.0
2Zack WheelerPHI13803030186.29.352.141.01.29272.1%3.523.484.8
3Logan WebbSFG121003030193.08.041.910.75.30171.6%3.423.314.6
4Kevin GausmanTOR12903030181.110.492.371.10.30575.1%3.443.384.4
5Aaron NolaPHI12903030186.09.431.961.26.29370.4%3.893.734.1
6Pablo LópezMIN12803030184.29.952.381.05.29773.2%3.523.504.0
7Gerrit ColeNYY14803131195.010.092.261.26.28374.6%3.513.683.9
8George KirbySEA12903030181.18.481.341.09.29571.6%3.573.513.8
9Tarik SkubalDET10902828154.210.292.380.99.29872.9%3.443.303.8
10Zach EflinTBR12802828165.18.731.591.10.29571.4%3.633.543.7
11Framber ValdezHOU14802929186.28.952.930.79.29373.4%3.403.553.7
12Corbin BurnesBAL12803030188.09.672.741.02.28372.7%3.533.633.6
13Zac GallenARI13903030188.09.242.431.06.29072.0%3.663.673.6
14Yoshinobu YamamotoLAD12802727167.29.532.401.10.28771.1%3.713.633.5
15Max FriedATL13702828163.28.642.210.91.29974.0%3.403.493.5
16Luis CastilloSEA13903030188.19.552.731.10.28373.9%3.543.743.5
17Freddy PeraltaMIL11902828161.210.823.101.19.28172.7%3.703.743.4
18Justin SteeleCHC11802929170.28.742.660.95.30172.8%3.673.703.3
19Jesús LuzardoMIA11902929168.010.222.951.16.29973.2%3.783.763.2
20Sonny GraySTL10802828163.08.582.930.90.29272.4%3.643.763.1
21Tyler GlasnowLAD12602424138.111.382.801.18.29074.2%3.503.433.1
22Blake Snell11802828159.111.414.381.00.29076.0%3.493.703.1
23Logan GilbertSEA12903131184.08.962.051.26.28871.9%3.823.863.0
24Mitch KellerPIT101003030180.28.673.021.05.30370.1%4.164.033.0
25Jordan Montgomery10802828168.28.172.431.11.29572.2%3.863.953.0
26Shane BieberCLE10802626166.08.502.331.11.29672.2%3.793.832.8
27Bobby MillerLAD12702626152.08.842.501.06.28269.8%3.833.802.8
28Grayson RodriguezBAL11802828158.29.682.991.07.28972.2%3.743.742.8
29Dylan CeaseCHW10903030173.210.403.881.17.29672.9%4.014.062.8
30Tanner BibeeCLE11802828164.19.192.741.16.29173.4%3.763.922.7
#NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
1Spencer StriderATL15603030178.212.642.981.03.29873.0%3.393.055.0
2Zack WheelerPHI13803030186.29.352.141.01.29272.1%3.523.484.8
3Logan WebbSFG121003030193.08.041.910.75.30171.6%3.423.314.6
4Kevin GausmanTOR12903030181.110.492.371.10.30575.1%3.443.384.4
5Aaron NolaPHI12903030186.09.431.961.26.29370.4%3.893.734.1
6Pablo LópezMIN12803030184.29.952.381.05.29773.2%3.523.504.0
7Gerrit ColeNYY14803131195.010.092.261.26.28374.6%3.513.683.9
8George KirbySEA12903030181.18.481.341.09.29571.6%3.573.513.8
9Tarik SkubalDET10902828154.210.292.380.99.29872.9%3.443.303.8
10Zach EflinTBR12802828165.18.731.591.10.29571.4%3.633.543.7
11Framber ValdezHOU14802929186.28.952.930.79.29373.4%3.403.553.7
12Corbin BurnesBAL12803030188.09.672.741.02.28372.7%3.533.633.6
13Zac GallenARI13903030188.09.242.431.06.29072.0%3.663.673.6
14Yoshinobu YamamotoLAD12802727167.29.532.401.10.28771.1%3.713.633.5
15Max FriedATL13702828163.28.642.210.91.29974.0%3.403.493.5
16Luis CastilloSEA13903030188.19.552.731.10.28373.9%3.543.743.5
17Freddy PeraltaMIL11902828161.210.823.101.19.28172.7%3.703.743.4
18Justin SteeleCHC11802929170.28.742.660.95.30172.8%3.673.703.3
19Jesús LuzardoMIA11902929168.010.222.951.16.29973.2%3.783.763.2
20Sonny GraySTL10802828163.08.582.930.90.29272.4%3.643.763.1
21Tyler GlasnowLAD12602424138.111.382.801.18.29074.2%3.503.433.1
22Blake Snell11802828159.111.414.381.00.29076.0%3.493.703.1
23Logan GilbertSEA12903131184.08.962.051.26.28871.9%3.823.863.0
24Mitch KellerPIT101003030180.28.673.021.05.30370.1%4.164.033.0
25Jordan Montgomery10802828168.28.172.431.11.29572.2%3.863.953.0
26Shane BieberCLE10802626166.08.502.331.11.29672.2%3.793.832.8
27Bobby MillerLAD12702626152.08.842.501.06.28269.8%3.833.802.8
28Grayson RodriguezBAL11802828158.29.682.991.07.28972.2%3.743.742.8
29Dylan CeaseCHW10903030173.210.403.881.17.29672.9%4.014.062.8
30Tanner BibeeCLE11802828164.19.192.741.16.29173.4%3.763.922.7
of 31
Page Size:
1 - 30 of 908 results
of 31
Page Size:
1 - 30 of 908 results
  • 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

  • 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.