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 2023 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
1Jaxon Smith-NjigbaWROhio St.109.199.1 × 1.101%Elite12
2Michael MayerTENotre Dame83.888.2 × 0.951%Starter16
3Jordan AddisonWRUSC83.575.9 × 1.107%Starter21
4Bijan RobinsonRBTexas82.597.1 × 0.85 × 1.0015%Elite5
5Quentin JohnstonWRTCU81.974.4 × 1.100%Starter22
6Zay FlowersWRBoston Col.76.469.4 × 1.104%Elite26
7Josh DownsWRNorth Carolina72.866.2 × 1.104%Elite41
8Bryce YoungQBAlabama72.891.0 × 0.8016%Elite1
9Dalton KincaidTEUtah72.376.1 × 0.9510%Starter25
10Anthony RichardsonQBFlorida71.589.3 × 0.8025%Starter9
11Darnell WashingtonTEGeorgia71.375.0 × 0.9515%Role player31
12Jalin HyattWRTennessee71.264.7 × 1.101%Role player43
13Luke MusgraveTEOregon St.70.073.7 × 0.9512%Starter52
14Jahmyr GibbsRBAlabama69.782.0 × 0.85 × 1.006%Elite23
15Tyler ScottWRCincinnati68.662.4 × 1.107%Role player70
16C.J. StroudQBOhio St.67.284.1 × 0.8022%Elite3
17Sam LaPortaTEIowa65.969.4 × 0.957%Elite56
18Rashee RiceWRSMU60.054.5 × 1.100%Starter81
19Xavier HutchinsonWRIowa St.58.853.5 × 1.1032%Role player90
20A.T. PerryWRWake Forest58.653.3 × 1.101%Role player112
21Will LevisQBKentucky57.171.4 × 0.8020%Starter24
22Marvin MimsWROklahoma56.851.6 × 1.100%Elite71
23Zach CharbonnetRBUCLA56.667.6 × 0.85 × 0.9822%Role player63
24Hendon HookerQBTennessee55.068.8 × 0.8015%Starter48
25Tucker KraftTESouth Dakota St.54.557.4 × 0.9525%Starter72
26Tyjae SpearsRBTulane53.462.8 × 0.85 × 1.0022%Role player75
27Tank DellWRHouston52.848.0 × 1.103%Starter89
28Roschon JohnsonRBTexas51.861.7 × 0.85 × 0.9919%Role player108
29Kendre MillerRBTCU51.560.6 × 0.85 × 1.006%Role player93
30Jayden ReedWRMichigan St.50.746.1 × 1.1029%Starter103
31Luke SchoonmakerTEMichigan49.251.8 × 0.9523%Role player137
32Tank BigsbyRBAuburn49.257.9 × 0.85 × 1.006%Role player122
33Dontayvion WicksWRVirginia47.242.9 × 1.1013%Role player139
34Jonathan MingoWRMississippi46.542.3 × 1.101%Role player69
35De'Von AchaneRBTexas A&M45.052.9 × 0.85 × 1.007%Starter68
36Cedric TillmanWRTennessee44.040.0 × 1.108%Role player62
37Andrei IosivasWRPrinceton42.838.9 × 1.1030%Starter167
38Kayshon BoutteWRLSU42.638.7 × 1.1023%Role player66
39Israel AbanikandaRBPittsburgh41.749.1 × 0.85 × 1.0010%Role player73
40Clayton TuneQBHouston39.949.8 × 0.8016%Role player154
41Puka NacuaWRBYU39.936.2 × 1.1050%Elite160
42Deuce VaughnRBKansas St.39.246.1 × 0.85 × 1.0023%Role player151
43Michael WilsonWRStanford38.835.3 × 1.1031%Starter113
44DeMario DouglasWRLiberty38.134.7 × 1.1048%Role player183
45DeWayne McBrideRBAla-Birmingham37.844.5 × 0.85 × 1.0022%Role player144
46Tre TuckerWRCincinnati37.434.0 × 1.1036%Starter177
47Ronnie BellWRMichigan35.432.2 × 1.1020%Role player187
48Parker WashingtonWRPenn St.34.631.4 × 1.104%Starter149
49Zach EvansRBMississippi34.640.8 × 0.85 × 1.0014%Bust91
50Zack KuntzTEOld Dominion34.436.2 × 0.9549%Bust156
51Justin ShorterWRFlorida34.431.3 × 1.1017%Bust173
52Grant DuboseWRCharlotte34.030.9 × 1.1021%Bust176
53Eric GrayRBOklahoma33.943.1 × 0.85 × 0.9327%Role player86
54Chase BrownRBIllinois33.942.2 × 0.85 × 0.9413%Role player153
55Brenton StrangeTEPenn St.33.635.3 × 0.9516%Role player130
56Chris RodriguezRBKentucky33.540.6 × 0.85 × 0.976%Role player198
57Charlie JonesWRPurdue31.128.2 × 1.1026%Bust138
58Davis AllenTEClemson30.832.4 × 0.9524%Role player78
59Jalen BrooksWRSouth Carolina30.627.8 × 1.1037%Bust
60Jaren HallQBBYU29.837.2 × 0.8031%Role player111
61Kenny McIntoshRBGeorgia28.635.8 × 0.85 × 0.9449%Bust95
62Lew NicholsRBCentral Michigan26.230.9 × 0.85 × 1.0063%Bust
63Jake HaenerQBFresno St.26.032.5 × 0.8026%Role player125
64Derius DavisWRTCU25.523.2 × 1.1019%Elite
65Will MalloryTEMiami (FL)25.426.7 × 0.9539%Role player127
66Antoine GreenWRNorth Carolina25.122.8 × 1.1035%Bust217
67Max DugganQBTCU24.530.6 × 0.8027%
68Dorian Thompson-RobinsonQBUCLA24.230.2 × 0.8028%Role player124
69Colton DowellWRUT Martin23.921.8 × 1.109%Bust
70Elijah HigginsWRStanford23.921.8 × 1.1030%Role player210
71Brayden WillisTEOklahoma23.725.0 × 0.9523%Bust
72Evan HullRBNorthwestern23.628.5 × 0.85 × 0.9714%Bust182
73Stetson BennettQBGeorgia23.028.7 × 0.8030%Bust101
74Cameron LatuTEAlabama21.923.1 × 0.9531%Starter186
75Sean CliffordQBPenn St.21.727.1 × 0.8025%Role player
76Payne DurhamTEPurdue21.722.9 × 0.9540%Role player196
77Trey PalmerWRNebraska20.418.5 × 1.1050%Role player76
78Josh WhyleTECincinnati17.518.4 × 0.9528%Role player190
79Tanner McKeeQBStanford17.121.4 × 0.8046%Bust141
80Aidan O'ConnellQBPurdue14.217.7 × 0.8032%Starter195

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.