
Atlantic Coast Conference
Power 4Women's Basketball · 18 teams · 144 scored athletes
Last updated: June 6, 2026
Avg NIL Score
Teams

Louisville Cardinals
7 scored players
74.1
Avg

NC State Wolfpack
5 scored players
73.0
Avg

Virginia Tech Hokies
7 scored players
70.5
Avg

North Carolina Tar Heels
7 scored players
69.6
Avg

Duke Blue Devils
7 scored players
69.5
Avg

Syracuse Orange
5 scored players
67.4
Avg

Stanford Cardinal
9 scored players
64.8
Avg

Wake Forest Demon Deacons
9 scored players
63.5
Avg

California Golden Bears
7 scored players
63.3
Avg

Virginia Cavaliers
10 scored players
63.1
Avg

Notre Dame Fighting Irish
8 scored players
62.6
Avg

Miami Hurricanes
9 scored players
62.5
Avg

Florida State Seminoles
8 scored players
61.5
Avg

Clemson Tigers
9 scored players
61.3
Avg

Georgia Tech Yellow Jackets
9 scored players
59.9
Avg

Boston College Eagles
8 scored players
57.0
Avg

SMU Mustangs
11 scored players
53.9
Avg

Pittsburgh Panthers
9 scored players
53.0
Avg
Top Athletes

Hannah Hidalgo
G · Notre Dame Fighting Irish

Kymora Johnson
G · Virginia Cavaliers

Taina Mair
G · Duke Blue Devils

Zoe Brooks
G · NC State Wolfpack

Mackenzie Nelson
G · Virginia Tech Hokies

Imari Berry
G · Louisville Cardinals

Skylar Jones
G · Louisville Cardinals

Zamareya Jones
G · NC State Wolfpack

Toby Fournier
F · Duke Blue Devils

Khamil Pierre
F · NC State Wolfpack
Methodology
The NILmetrics Score is a 0-100 rating that combines on-court performance, market demand signals, social presence, and reliability into a single number. Like Elo for chess or KenPom for basketball, it measures a player's relative standing in the college basketball NIL market — not a guaranteed dollar value or future deal price.
Performance weighs recent box-score production. Market reflects program tier, conference visibility, and demand signals. Social captures verified follower scale. Reliability rewards healthy minutes over a full sample. We update scores after every Sunday's full sync.
Methodology note: women's basketball calibration is developing as the dataset grows. Treat scores as directional while the sample expands.
Read the full methodology →