2024 Projections

Data Export [Members Only]
#NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
1José RamírezCLE1516562790942410.4%11.7%.215.276.276.354.490.3561301.825.22.45.0
2Andrés GiménezCLE144594167666275.6%19.1%.156.305.264.326.419.3231071.36.17.83.4
3Bo NaylorCLE118442165254711.9%25.3%.181.280.229.326.410.321106-0.32.67.42.5
4Steven KwanCLE14061657951179.5%10.2%.098.301.275.348.373.3181040.43.3-1.82.2
5Josh NaylorCLE13355821648587.4%14.5%.183.293.278.335.461.338118-1.610.2-12.41.7
6Lane ThomasCLE144615228171167.3%25.0%.180.303.249.310.429.31898-0.1-1.5-6.81.3
7Brayan RocchioCLE10543265143117.3%19.1%.114.293.245.307.359.29386-0.6-7.74.71.2
8Tyler FreemanCLE812854332976.0%15.2%.109.302.265.330.374.31198-0.2-0.70.61.0
9Will BrennanCLE893465363695.4%13.5%.106.303.271.317.377.30293-0.3-3.3-1.90.7
10Gabriel AriasCLE742677292737.5%28.9%.145.306.231.296.377.29487-0.5-4.71.50.6
11Estevan FlorialCLE8030983632129.5%32.8%.151.314.221.300.373.29587-0.1-4.7-0.70.5
12Kyle ManzardoCLE86356113841110.4%21.1%.171.264.230.314.402.31199-0.7-1.1-6.80.4
13Austin HedgesCLE541833151516.1%24.8%.091.245.195.254.286.24050-0.4-11.47.50.2
14Juan BritoCLE1443145010.1%18.7%.122.283.239.321.361.302920.0-0.40.20.1
15Myles StrawCLE2390010638.5%17.7%.057.304.248.313.306.279760.3-2.20.10.1
16David FryCLE521585171817.1%24.4%.158.287.234.301.392.30293-0.3-1.7-2.90.1
17Angel MartínezCLE124814506.6%22.1%.108.288.231.286.339.27574-0.1-1.60.30.0
18George ValeraCLE1345155010.3%28.4%.142.283.215.302.357.291850.0-0.8-0.50.0
19Johnathan RodriguezCLE62613307.0%31.4%.140.326.234.290.374.289830.0-0.6-0.20.0
20Jhonkensy NoelCLE123413406.5%27.2%.149.277.220.280.369.281780.0-0.9-0.40.0
21Chase DeLauterCLE311212131315.6%17.5%.107.292.253.299.360.28883-0.2-2.8-1.60.0
#NameTeamGPAHRRRBISBBB%K%ISOBABIPAVGOBPSLGwOBAwRC+BsROffDefWAR
1José RamírezCLE1516562790942410.4%11.7%.215.276.276.354.490.3561301.825.22.45.0
2Andrés GiménezCLE144594167666275.6%19.1%.156.305.264.326.419.3231071.36.17.83.4
3Bo NaylorCLE118442165254711.9%25.3%.181.280.229.326.410.321106-0.32.67.42.5
4Steven KwanCLE14061657951179.5%10.2%.098.301.275.348.373.3181040.43.3-1.82.2
5Josh NaylorCLE13355821648587.4%14.5%.183.293.278.335.461.338118-1.610.2-12.41.7
6Lane ThomasCLE144615228171167.3%25.0%.180.303.249.310.429.31898-0.1-1.5-6.81.3
7Brayan RocchioCLE10543265143117.3%19.1%.114.293.245.307.359.29386-0.6-7.74.71.2
8Tyler FreemanCLE812854332976.0%15.2%.109.302.265.330.374.31198-0.2-0.70.61.0
9Will BrennanCLE893465363695.4%13.5%.106.303.271.317.377.30293-0.3-3.3-1.90.7
10Gabriel AriasCLE742677292737.5%28.9%.145.306.231.296.377.29487-0.5-4.71.50.6
11Estevan FlorialCLE8030983632129.5%32.8%.151.314.221.300.373.29587-0.1-4.7-0.70.5
12Kyle ManzardoCLE86356113841110.4%21.1%.171.264.230.314.402.31199-0.7-1.1-6.80.4
13Austin HedgesCLE541833151516.1%24.8%.091.245.195.254.286.24050-0.4-11.47.50.2
14Juan BritoCLE1443145010.1%18.7%.122.283.239.321.361.302920.0-0.40.20.1
15Myles StrawCLE2390010638.5%17.7%.057.304.248.313.306.279760.3-2.20.10.1
16David FryCLE521585171817.1%24.4%.158.287.234.301.392.30293-0.3-1.7-2.90.1
17Angel MartínezCLE124814506.6%22.1%.108.288.231.286.339.27574-0.1-1.60.30.0
18George ValeraCLE1345155010.3%28.4%.142.283.215.302.357.291850.0-0.8-0.50.0
19Johnathan RodriguezCLE62613307.0%31.4%.140.326.234.290.374.289830.0-0.6-0.20.0
20Jhonkensy NoelCLE123413406.5%27.2%.149.277.220.280.369.281780.0-0.9-0.40.0
21Chase DeLauterCLE311212131315.6%17.5%.107.292.253.299.360.28883-0.2-2.8-1.60.0
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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.