Dynasty rookie board

The same career-value model that runs the big board, pointed at the only question a dynasty GM ever has: which rookies return fantasy production, and for how long. Quarterbacks, running backs, receivers and tight ends only — nobody starts a left tackle.

The 2022 class has been drafted — this board is the model’s pre-draft read, useful as a receipt against how the rookies actually landed.

1QB starts one quarterback, so even good QB prospects slide — QBs carry a 0.80 multiplier. Superflex starts two, which makes the position the scarcest asset in the format — QBs carry 1.30 instead. Nothing else changes between the two boards.

#PlayerPosCollegeDynasty scoreHow it's builtBustTierConsensus
1Garrett WilsonWROhio St.95.586.8 × 1.101%Elite12
2Jameson WilliamsWRAlabama93.985.4 × 1.100%Starter10
3Treylon BurksWRArkansas87.979.9 × 1.1030%Starter22
4Drake LondonWRUSC87.179.2 × 1.103%Starter13
5Jahan DotsonWRPenn St.78.271.1 × 1.1018%Starter32
6Chris OlaveWROhio St.76.069.1 × 1.104%Starter15
7Skyy MooreWRWestern Michigan74.767.9 × 1.1014%Starter38
8Christian WatsonWRNorth Dakota St.73.967.1 × 1.108%Starter51
9Breece HallRBIowa St.73.286.1 × 0.85 × 1.0013%Role player47
10John MetchieWRAlabama70.363.9 × 1.101%Role player71
11George PickensWRGeorgia66.260.2 × 1.104%Starter42
12Trey McBrideTEColorado St.64.367.7 × 0.9518%Starter58
13Kenneth Walker IIIRBMichigan St.62.173.0 × 0.85 × 1.006%Role player46
14Tyquan ThorntonWRBaylor59.854.3 × 1.1026%Role player129
15Kenny PickettQBPittsburgh58.372.9 × 0.8018%Starter34
16Desmond RidderQBCincinnati58.372.9 × 0.8015%Role player41
17Sam HowellQBNorth Carolina58.272.8 × 0.8011%Starter61
18Malik WillisQBLiberty58.072.5 × 0.8027%Role player28
19David BellWRPurdue57.752.5 × 1.1016%Role player76
20Calvin Austin IIIWRMemphis55.050.0 × 1.1021%Role player119
21Alec PierceWRCincinnati54.149.2 × 1.1018%Starter95
22Cade OttonTEWashington53.856.6 × 0.9518%Starter128
23Jalen TolbertWRSouth Alabama51.546.8 × 1.1014%Role player112
24Khalil ShakirWRBoise St.50.345.8 × 1.1030%Starter141
25Isaiah LikelyTECoastal Carolina49.452.0 × 0.9519%Starter145
26Isaiah SpillerRBTexas A&M49.357.9 × 0.85 × 1.0018%Role player67
27Chig OkonkwoTEMaryland48.951.5 × 0.9527%Role player150
28Matt CorralQBMississippi48.961.1 × 0.8024%Bust50
29Wan'Dale RobinsonWRKentucky43.939.9 × 1.1012%Starter154
30Brian Robinson Jr.RBAlabama43.554.1 × 0.85 × 0.9415%Role player98
31Daniel BellingerTESan Diego St.43.445.7 × 0.9526%Role player138
32Dareke YoungWRLenoir-Rhyne43.139.1 × 1.1020%Bust
33Greg DulcichTEUCLA40.843.0 × 0.9523%Role player86
34Jeremy RuckertTEOhio St.39.741.8 × 0.9528%Role player107
35Pierre StrongRBSouth Dakota St.39.049.2 × 0.85 × 0.9319%Role player121
36Rachaad WhiteRBArizona St.38.648.6 × 0.85 × 0.9410%Role player93
37Dameon PierceRBFlorida37.744.7 × 0.85 × 0.9920%Role player123
38Kyren WilliamsRBNotre Dame37.143.7 × 0.85 × 1.0074%Elite111
39Tyler AllgeierRBBYU36.843.3 × 0.85 × 1.006%Role player91
40Romeo DoubsWRNevada36.333.0 × 1.1012%Starter140
41Montrell WashingtonWRSamford36.132.8 × 1.1020%Role player
42Kyle PhilipsWRUCLA35.632.4 × 1.1039%Role player103
43Danny GrayWRSMU35.632.3 × 1.1022%Bust147
44Bo MeltonWRRutgers34.931.7 × 1.1032%Role player
45Andrew OgletreeTEYoungstown St.34.936.8 × 0.9527%Role player
46James CookRBGeorgia34.241.4 × 0.85 × 0.9734%Elite159
47Jalen NailorWRMichigan St.34.030.9 × 1.1038%Role player
48Kevin HarrisRBSouth Carolina33.739.7 × 0.85 × 1.0011%Role player
49Erik EzukanmaWRTexas Tech33.530.5 × 1.1036%Role player
50Zamir WhiteRBGeorgia32.839.9 × 0.85 × 0.9720%Role player99
51Keaontay IngramRBUSC32.339.0 × 0.85 × 0.9722%Role player
52Michael Woods IIWROklahoma30.928.1 × 1.1028%Role player
53Hassan HaskinsRBMichigan30.736.9 × 0.85 × 0.9810%Role player
54Tyler BadieRBMissouri30.436.2 × 0.85 × 0.9922%Role player
55Jerome FordRBCincinnati29.335.5 × 0.85 × 0.9714%Role player124
56Nick MuseTESouth Carolina29.030.6 × 0.9521%Bust
57Brock PurdyQBIowa St.29.036.2 × 0.8031%Elite
58Connor HeywardTEMichigan St.28.730.2 × 0.9517%Role player
59Isiah PachecoRBRutgers28.235.2 × 0.85 × 0.9452%Role player
60James MitchellTEVirginia Tech27.929.4 × 0.9526%Role player
61Bailey ZappeQBWestern Kentucky27.934.9 × 0.8025%Role player151
62Trestan EbnerRBBaylor27.835.1 × 0.85 × 0.9335%Bust
63Teagan QuitorianoTEOregon St.26.928.3 × 0.9521%Role player
64Jelani WoodsTEVirginia26.728.2 × 0.9523%Bust160
65Jake FergusonTEWisconsin26.327.7 × 0.9517%Elite164
66Charlie KolarTEIowa St.26.027.3 × 0.9522%Role player146
67Ko KieftTEMinnesota25.727.0 × 0.9521%Role player
68Tyrion Davis-PriceRBLSU25.630.1 × 0.85 × 1.0033%Role player
69Chris OladokunQBSouth Dakota St.24.730.9 × 0.8032%Role player
70Zander HorvathRBPurdue24.531.0 × 0.85 × 0.9334%Bust
71John FitzPatrickTEGeorgia23.524.8 × 0.9512%Bust
72Snoop ConnerRBMississippi22.226.1 × 0.85 × 1.0049%Bust
73Cole TurnerTENevada20.621.7 × 0.9535%Bust
74Brittain BrownRBUCLA20.427.5 × 0.85 × 0.8734%Bust
75Velus Jones Jr.WRTennessee20.318.4 × 1.1038%Role player143
76Grant CalcaterraTESMU18.419.3 × 0.9533%Role player
77Ty ChandlerRBNorth Carolina15.620.3 × 0.85 × 0.9040%Role player
78Skylar ThompsonQBKansas St.15.519.3 × 0.8031%Role player
79Samori ToureWRNebraska12.111.0 × 1.1011%Role player

How this differs from the big board

Built from the same models as the big board — here's exactly how the lens differs. The base is the served career-value model score, untouched. On top of it sit three documented adjustments, all shown per player in the table:

Dynasty score = model score × position multiplier (1QB) × RB age multiplier

  • Position: QB ×0.80, RB ×0.85, WR ×1.10, TE ×0.95 — QB scarcity by format, the RB career-length discount, the WR longevity premium, the TE slow-onboarding haircut.
  • RB age cliff: RB only: 1.00 at age 22 or younger at draft, minus 0.05 per year older, floor 0.85. When we don't have a player's age on file, no adjustment is applied and the row says so.
  • Tier reads: shown only where the tier model itself stands behind them; when it isn't confident, you get a dash, not a guess.

The multipliers are round numbers encoding dynasty-market convention (QB scarcity by format, the RB age cliff, WR longevity, the TE year-3 breakout) — convention, not Draftanomics research. The model signal is entirely in the base. College production volume (target and workload share) is already inside the base score as model features — it isn't double-counted here. See methodology for how the career-value model is fit and validated.