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 2024 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
1Drake MayeQBNorth Carolina117.490.3 × 1.3017%Elite7
2Caleb WilliamsQBUSC111.485.7 × 1.3016%Starter6
3J.J. McCarthyQBMichigan110.084.6 × 1.3011%Starter22
4Jayden DanielsQBLSU109.684.3 × 1.308%Elite14
5Rome OdunzeWRWashington94.185.6 × 1.101%Starter2
6Ladd McConkeyWRGeorgia93.484.9 × 1.109%Starter31
7Brian ThomasWRLSU91.182.8 × 1.100%
class average
Starter21
8Marvin Harrison Jr.WROhio St.89.281.1 × 1.100%
class average
Starter5
9Malik NabersWRLSU89.181.0 × 1.100%
class average
Elite1
10Keon ColemanWRFlorida St.85.577.8 × 1.100%
class average
Starter32
11Bo NixQBOregon79.060.7 × 1.3012%Starter38
12Brock BowersTEGeorgia78.782.9 × 0.955%Elite4
13Adonai MitchellWRTexas78.070.9 × 1.100%
class average
Role player29
14Michael PenixQBWashington77.259.4 × 1.3016%Starter37
15Xavier WorthyWRTexas75.068.2 × 1.100%
class average
Role player36
16Xavier LegetteWRSouth Carolina71.965.4 × 1.109%Role player40
17Ricky PearsallWRFlorida71.965.4 × 1.1012%Starter41
18Ja'Lynn PolkWRWashington71.765.2 × 1.100%Role player54
19Malachi CorleyWRWestern Kentucky70.363.9 × 1.101%Role player69
20Troy FranklinWROregon67.261.1 × 1.101%Role player43
21Roman WilsonWRMichigan66.660.6 × 1.1013%Bust49
22Jonathon BrooksRBTexas64.876.2 × 0.85 × 1.0011%Role player58
23Devontez WalkerWRNorth Carolina62.556.8 × 1.1020%Bust105
24Jalen McMillanWRWashington61.155.6 × 1.109%Role player97
25Trey BensonRBFlorida St.59.970.4 × 0.85 × 1.0018%Role player61
26Michael PrattQBTulane59.145.4 × 1.3025%Starter119
27Jaylen WrightRBTennessee56.666.6 × 0.85 × 1.0015%Role player98
28Malik WashingtonWRVirginia53.949.0 × 1.1029%Role player102
29Audric EstimeRBNotre Dame53.663.1 × 0.85 × 1.0015%Role player95
30Jacob CowingWRArizona51.246.5 × 1.1035%Role player141
31Jermaine BurtonWRAlabama50.946.3 × 1.108%Role player112
32Tahj WashingtonWRUSC49.845.3 × 1.1016%Starter165
33Braelon AllenRBWisconsin49.057.7 × 0.85 × 1.0016%Role player109
34Ja'Tavion SandersTETexas48.250.7 × 0.9528%Starter72
35Blake CorumRBMichigan47.860.5 × 0.85 × 0.9324%Role player103
36Theo JohnsonTEPenn St.47.750.2 × 0.9524%Role player125
37Kimani VidalRBTroy46.756.8 × 0.85 × 0.979%Role player178
38Ben SinnottTEKansas St.46.548.9 × 0.9519%Bust93
39MarShawn LloydRBUSC46.458.4 × 0.85 × 0.9347%Role player124
40Spencer RattlerQBSouth Carolina46.035.4 × 1.3031%Role player82
41Jordan TravisQBFlorida St.45.334.8 × 1.3023%Starter198
42Johnny WilsonWRFlorida St.44.840.7 × 1.1016%Role player76
43Bub MeansWRPittsburgh43.939.9 × 1.1035%Role player202
44Javon BakerWRCentral Florida43.339.4 × 1.109%Bust96
45Brenden RiceWRUSC42.238.4 × 1.1038%Bust143
46Ryan FlournoyWRSE Missouri St.41.737.9 × 1.1047%Role player187
47Cornelius JohnsonWRMichigan38.835.2 × 1.1021%Starter170
48Jamari ThrashWRLouisville38.334.8 × 1.1047%Bust137
49Jared WileyTETCU38.040.0 × 0.9530%Bust106
50AJ BarnerTEMichigan37.239.1 × 0.9514%Starter162
51Jha'Quan JacksonWRTulane36.933.5 × 1.1029%Role player183
52Ray DavisRBKentucky36.549.0 × 0.85 × 0.8837%Starter139
53Isaiah DavisRBSouth Dakota St.35.141.6 × 0.85 × 0.9917%Role player156
54Bucky IrvingRBOregon34.640.7 × 0.85 × 1.0019%Starter120
55Luke McCaffreyWRRice33.530.5 × 1.1029%Starter135
56Will ShipleyRBClemson33.239.0 × 0.85 × 1.0026%Bust68
57Cade StoverTEOhio St.33.034.7 × 0.9527%Starter111
58Jordan WhittingtonWRTexas32.329.4 × 1.109%Role player189
59Tejhaun PalmerWRAla-Birmingham32.129.2 × 1.1026%Starter199
60Devaughn VeleWRUtah31.428.6 × 1.1038%Starter
61Anthony GouldWROregon St.30.527.7 × 1.1021%Bust203
62Erick AllTEIowa30.331.9 × 0.9525%Role player153
63Joe MiltonQBTennessee30.123.2 × 1.3034%Role player
64Isaac GuerendoRBLouisville29.938.8 × 0.85 × 0.9139%Role player160
65Jaheim BellTEFlorida St.29.831.3 × 0.9529%Bust154
66Casey WashingtonWRIllinois28.525.9 × 1.1023%Bust218
67Tip ReimanTEIllinois28.329.8 × 0.9521%Bust197
68Ainias SmithWRTexas A&M27.825.3 × 1.1023%Role player174
69Tanner McLachlanTEArizona27.428.9 × 0.9541%Bust191
70Dylan LaubeRBNew Hampshire27.436.6 × 0.85 × 0.8816%Bust142
71Tyrone Tracy Jr.RBPurdue26.435.3 × 0.85 × 0.8826%Role player146
72Devin LearyQBKentucky25.319.4 × 1.3038%
73Rasheen AliRBMarshall24.730.9 × 0.85 × 0.9419%Bust204
74Devin CulpTEWashington22.723.9 × 0.9530%Bust
75Jase McClellanRBAlabama22.226.2 × 0.85 × 1.0024%Bust
76Jawhar JordanRBLouisville20.928.4 × 0.85 × 0.8760%Bust
77Keilan RobinsonRBTexas13.818.2 × 0.85 × 0.8919%Bust

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.