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
1Bryce YoungQBAlabama118.391.0 × 1.3016%Elite1
2Anthony RichardsonQBFlorida116.189.3 × 1.3025%Starter9
3C.J. StroudQBOhio St.109.384.1 × 1.3022%Elite3
4Jaxon Smith-NjigbaWROhio St.109.199.1 × 1.101%Elite12
5Will LevisQBKentucky92.871.4 × 1.3020%Starter24
6Hendon HookerQBTennessee89.468.8 × 1.3015%Starter48
7Michael MayerTENotre Dame83.888.2 × 0.951%Starter16
8Jordan AddisonWRUSC83.575.9 × 1.107%Starter21
9Bijan RobinsonRBTexas82.597.1 × 0.85 × 1.0015%Elite5
10Quentin JohnstonWRTCU81.974.4 × 1.100%Starter22
11Zay FlowersWRBoston Col.76.469.4 × 1.104%Elite26
12Josh DownsWRNorth Carolina72.866.2 × 1.104%Elite41
13Dalton KincaidTEUtah72.376.1 × 0.9510%Starter25
14Darnell WashingtonTEGeorgia71.375.0 × 0.9515%Role player31
15Jalin HyattWRTennessee71.264.7 × 1.101%Role player43
16Luke MusgraveTEOregon St.70.073.7 × 0.9512%Starter52
17Jahmyr GibbsRBAlabama69.782.0 × 0.85 × 1.006%Elite23
18Tyler ScottWRCincinnati68.662.4 × 1.107%Role player70
19Sam LaPortaTEIowa65.969.4 × 0.957%Elite56
20Clayton TuneQBHouston64.849.8 × 1.3016%Role player154
21Rashee RiceWRSMU60.054.5 × 1.100%Starter81
22Xavier HutchinsonWRIowa St.58.853.5 × 1.1032%Role player90
23A.T. PerryWRWake Forest58.653.3 × 1.101%Role player112
24Marvin MimsWROklahoma56.851.6 × 1.100%Elite71
25Zach CharbonnetRBUCLA56.667.6 × 0.85 × 0.9822%Role player63
26Tucker KraftTESouth Dakota St.54.557.4 × 0.9525%Starter72
27Tyjae SpearsRBTulane53.462.8 × 0.85 × 1.0022%Role player75
28Tank DellWRHouston52.848.0 × 1.103%Starter89
29Roschon JohnsonRBTexas51.861.7 × 0.85 × 0.9919%Role player108
30Kendre MillerRBTCU51.560.6 × 0.85 × 1.006%Role player93
31Jayden ReedWRMichigan St.50.746.1 × 1.1029%Starter103
32Luke SchoonmakerTEMichigan49.251.8 × 0.9523%Role player137
33Tank BigsbyRBAuburn49.257.9 × 0.85 × 1.006%Role player122
34Jaren HallQBBYU48.437.2 × 1.3031%Role player111
35Dontayvion WicksWRVirginia47.242.9 × 1.1013%Role player139
36Jonathan MingoWRMississippi46.542.3 × 1.101%Role player69
37De'Von AchaneRBTexas A&M45.052.9 × 0.85 × 1.007%Starter68
38Cedric TillmanWRTennessee44.040.0 × 1.108%Role player62
39Andrei IosivasWRPrinceton42.838.9 × 1.1030%Starter167
40Kayshon BoutteWRLSU42.638.7 × 1.1023%Role player66
41Jake HaenerQBFresno St.42.332.5 × 1.3026%Role player125
42Israel AbanikandaRBPittsburgh41.749.1 × 0.85 × 1.0010%Role player73
43Puka NacuaWRBYU39.936.2 × 1.1050%Elite160
44Max DugganQBTCU39.730.6 × 1.3027%
45Dorian Thompson-RobinsonQBUCLA39.330.2 × 1.3028%Role player124
46Deuce VaughnRBKansas St.39.246.1 × 0.85 × 1.0023%Role player151
47Michael WilsonWRStanford38.835.3 × 1.1031%Starter113
48DeMario DouglasWRLiberty38.134.7 × 1.1048%Role player183
49DeWayne McBrideRBAla-Birmingham37.844.5 × 0.85 × 1.0022%Role player144
50Tre TuckerWRCincinnati37.434.0 × 1.1036%Starter177
51Stetson BennettQBGeorgia37.328.7 × 1.3030%Bust101
52Ronnie BellWRMichigan35.432.2 × 1.1020%Role player187
53Sean CliffordQBPenn St.35.227.1 × 1.3025%Role player
54Parker WashingtonWRPenn St.34.631.4 × 1.104%Starter149
55Zach EvansRBMississippi34.640.8 × 0.85 × 1.0014%Bust91
56Zack KuntzTEOld Dominion34.436.2 × 0.9549%Bust156
57Justin ShorterWRFlorida34.431.3 × 1.1017%Bust173
58Grant DuboseWRCharlotte34.030.9 × 1.1021%Bust176
59Eric GrayRBOklahoma33.943.1 × 0.85 × 0.9327%Role player86
60Chase BrownRBIllinois33.942.2 × 0.85 × 0.9413%Role player153
61Brenton StrangeTEPenn St.33.635.3 × 0.9516%Role player130
62Chris RodriguezRBKentucky33.540.6 × 0.85 × 0.976%Role player198
63Charlie JonesWRPurdue31.128.2 × 1.1026%Bust138
64Davis AllenTEClemson30.832.4 × 0.9524%Role player78
65Jalen BrooksWRSouth Carolina30.627.8 × 1.1037%Bust
66Kenny McIntoshRBGeorgia28.635.8 × 0.85 × 0.9449%Bust95
67Tanner McKeeQBStanford27.821.4 × 1.3046%Bust141
68Lew NicholsRBCentral Michigan26.230.9 × 0.85 × 1.0063%Bust
69Derius DavisWRTCU25.523.2 × 1.1019%Elite
70Will MalloryTEMiami (FL)25.426.7 × 0.9539%Role player127
71Antoine GreenWRNorth Carolina25.122.8 × 1.1035%Bust217
72Colton DowellWRUT Martin23.921.8 × 1.109%Bust
73Elijah HigginsWRStanford23.921.8 × 1.1030%Role player210
74Brayden WillisTEOklahoma23.725.0 × 0.9523%Bust
75Evan HullRBNorthwestern23.628.5 × 0.85 × 0.9714%Bust182
76Aidan O'ConnellQBPurdue23.017.7 × 1.3032%Starter195
77Cameron LatuTEAlabama21.923.1 × 0.9531%Starter186
78Payne DurhamTEPurdue21.722.9 × 0.9540%Role player196
79Trey PalmerWRNebraska20.418.5 × 1.1050%Role player76
80Josh WhyleTECincinnati17.518.4 × 0.9528%Role player190

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 (Superflex) × RB age multiplier

  • Position: QB ×1.30, 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.