2024 Projections

Data Export [Members Only]
#NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
1Edwin DíazNYM333461062.114.053.130.83.31078.4%2.702.551.4
2Emmanuel ClaseCLE443466067.18.812.100.65.29974.3%3.013.031.0
3Tanner ScottSDP432566066.211.794.020.70.31175.1%3.253.171.0
4A.J. MinterATL43563061.211.132.960.97.30674.0%3.453.261.0
5Pete FairbanksTBR332953053.212.463.460.96.28779.4%2.853.170.9
6Andrés MuñozSEA442957059.211.963.300.81.30077.2%2.943.050.9
7Trevor MegillMIL32652056.211.593.411.05.32572.3%3.913.460.9
8José AlvaradoPHI332360061.112.264.240.94.30479.3%3.103.430.9
9David BednarPIT333059062.010.672.951.10.29778.1%3.243.570.8
10Ryan PresslyHOU43561062.19.992.440.93.29673.0%3.393.270.8
11Hunter HarveyKCR331358060.210.002.561.06.29473.3%3.543.520.8
12Aaron BummerATL43160060.210.724.140.64.31870.6%3.813.360.8
13Ryan HelsleySTL432658062.011.733.871.06.27978.8%3.133.570.8
14Caleb FergusonHOU43159156.110.273.630.83.30674.6%3.473.600.8
15Clay HolmesNYY432963064.19.423.350.68.29773.8%3.313.470.7
16Camilo DovalSFG543362064.010.963.730.89.29676.5%3.253.550.7
17Chris MartinBOS43460059.28.851.671.01.31474.7%3.433.390.7
18Joe KellyLAD33255055.111.594.050.77.31471.2%3.753.310.7
19Aroldis ChapmanPIT44758057.213.405.430.90.30377.7%3.333.510.7
20Bryan AbreuHOU32359063.112.074.000.94.29679.2%3.043.440.7
21Sixto SánchezMIA32041151.28.802.620.83.29970.2%3.713.500.7
22Andrew NardiMIA53662065.210.943.801.03.30174.3%3.673.740.7
23Pierce JohnsonATL34159057.111.973.901.05.32674.0%3.823.500.7
24Jason AdamSDP331057057.010.813.011.05.27574.7%3.303.650.7
25Yuki MatsuiSDP32858060.211.593.911.05.29376.4%3.423.620.7
26Josh SborzTEX44256059.010.863.541.15.30669.8%4.213.770.7
27Matt StrahmPHI54159167.010.582.581.39.28475.5%3.633.920.7
28Shawn Armstrong22155161.09.322.581.09.29573.6%3.633.780.7
29Ian HamiltonNYY43253061.210.163.970.82.29273.4%3.563.640.6
30Matt BrashSEA43749050.212.374.070.84.31576.1%3.293.240.6
#NameTeamWLSVGGSIPK/9BB/9HR/9BABIPLOB%GB%ERAFIPWAR
1Edwin DíazNYM333461062.114.053.130.83.31078.4%2.702.551.4
2Emmanuel ClaseCLE443466067.18.812.100.65.29974.3%3.013.031.0
3Tanner ScottSDP432566066.211.794.020.70.31175.1%3.253.171.0
4A.J. MinterATL43563061.211.132.960.97.30674.0%3.453.261.0
5Pete FairbanksTBR332953053.212.463.460.96.28779.4%2.853.170.9
6Andrés MuñozSEA442957059.211.963.300.81.30077.2%2.943.050.9
7Trevor MegillMIL32652056.211.593.411.05.32572.3%3.913.460.9
8José AlvaradoPHI332360061.112.264.240.94.30479.3%3.103.430.9
9David BednarPIT333059062.010.672.951.10.29778.1%3.243.570.8
10Ryan PresslyHOU43561062.19.992.440.93.29673.0%3.393.270.8
11Hunter HarveyKCR331358060.210.002.561.06.29473.3%3.543.520.8
12Aaron BummerATL43160060.210.724.140.64.31870.6%3.813.360.8
13Ryan HelsleySTL432658062.011.733.871.06.27978.8%3.133.570.8
14Caleb FergusonHOU43159156.110.273.630.83.30674.6%3.473.600.8
15Clay HolmesNYY432963064.19.423.350.68.29773.8%3.313.470.7
16Camilo DovalSFG543362064.010.963.730.89.29676.5%3.253.550.7
17Chris MartinBOS43460059.28.851.671.01.31474.7%3.433.390.7
18Joe KellyLAD33255055.111.594.050.77.31471.2%3.753.310.7
19Aroldis ChapmanPIT44758057.213.405.430.90.30377.7%3.333.510.7
20Bryan AbreuHOU32359063.112.074.000.94.29679.2%3.043.440.7
21Sixto SánchezMIA32041151.28.802.620.83.29970.2%3.713.500.7
22Andrew NardiMIA53662065.210.943.801.03.30174.3%3.673.740.7
23Pierce JohnsonATL34159057.111.973.901.05.32674.0%3.823.500.7
24Jason AdamSDP331057057.010.813.011.05.27574.7%3.303.650.7
25Yuki MatsuiSDP32858060.211.593.911.05.29376.4%3.423.620.7
26Josh SborzTEX44256059.010.863.541.15.30669.8%4.213.770.7
27Matt StrahmPHI54159167.010.582.581.39.28475.5%3.633.920.7
28Shawn Armstrong22155161.09.322.581.09.29573.6%3.633.780.7
29Ian HamiltonNYY43253061.210.163.970.82.29273.4%3.563.640.6
30Matt BrashSEA43749050.212.374.070.84.31576.1%3.293.240.6
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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

  • 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.