Atlantic Coast Conference
Power 4Women's Basketball · 18 teams · 219 scored athletes
Last updated: July 21, 2026 · refreshed daily
Avg NIL Score
Teams
North Carolina Tar Heels
14 scored players
68.7
Avg
Virginia Tech Hokies
11 scored players
67.3
Avg
Louisville Cardinals
14 scored players
65.8
Avg
Miami Hurricanes
13 scored players
63.7
Avg
NC State Wolfpack
11 scored players
62.9
Avg
Wake Forest Demon Deacons
12 scored players
62.5
Avg
Notre Dame Fighting Irish
10 scored players
62.1
Avg
Syracuse Orange
15 scored players
61.4
Avg
Duke Blue Devils
11 scored players
61.0
Avg
Georgia Tech Yellow Jackets
10 scored players
60.7
Avg
Florida State Seminoles
10 scored players
60.5
Avg
Clemson Tigers
12 scored players
59.8
Avg
Stanford Cardinal
15 scored players
59.7
Avg
Virginia Cavaliers
13 scored players
59.6
Avg
California Golden Bears
13 scored players
58.4
Avg
Boston College Eagles
10 scored players
55.3
Avg
SMU Mustangs
14 scored players
53.8
Avg
Pittsburgh Panthers
11 scored players
52.0
Avg
Top Athletes
Hannah Hidalgo
G · Notre Dame Fighting Irish
Kymora Johnson
G · Virginia Cavaliers
Mackenzie Nelson
G · Virginia Tech Hokies
Imari Berry
G · Louisville Cardinals
Taina Mair
G · Duke Blue Devils
Skylar Jones
G · Louisville Cardinals
Zoe Brooks
G · NC State Wolfpack
Zamareya Jones
G · NC State Wolfpack
Talayah Walker
G · Georgia Tech Yellow Jackets
Uche Izoje
C · Syracuse Orange
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 →