Big Ten Conference
Power 4Women's Basketball · 18 teams · 201 scored athletes
Last updated: September 4, 2026 · refreshed daily
Avg NIL Score
Teams
Michigan State Spartans
11 scored players
67.4
Avg
Nebraska Cornhuskers
11 scored players
66.0
Avg
Minnesota Golden Gophers
9 scored players
64.6
Avg
Ohio State Buckeyes
10 scored players
63.8
Avg
Iowa Hawkeyes
13 scored players
63.6
Avg
Maryland Terrapins
11 scored players
61.5
Avg
Michigan Wolverines
13 scored players
61.0
Avg
USC Trojans
13 scored players
60.8
Avg
Oregon Ducks
13 scored players
60.1
Avg
Purdue Boilermakers
11 scored players
59.0
Avg
Penn State Lady Lions
12 scored players
57.6
Avg
Indiana Hoosiers
12 scored players
57.5
Avg
Illinois Fighting Illini
13 scored players
57.3
Avg
Northwestern Wildcats
10 scored players
57.3
Avg
Washington Huskies
12 scored players
56.1
Avg
UCLA Bruins
3 scored players
55.2
Avg
Wisconsin Badgers
13 scored players
53.9
Avg
Rutgers Scarlet Knights
11 scored players
49.4
Avg
Top Athletes
Jaloni Cambridge
G · Ohio State Buckeyes
JuJu Watkins
G · USC Trojans
Shay Ciezki
G · Indiana Hoosiers
Oluchi Okananwa
G · Maryland Terrapins
Ava Heiden
C · Iowa Hawkeyes
Jazzy Davidson
G · USC Trojans
Olivia Olson
G · Michigan Wolverines
Britt Prince
G · Nebraska Cornhuskers
Sayvia Sellers
G · Washington Huskies
Kiyomi McMiller
G · Penn State Lady Lions
Methodology
The NILmetrics Score is a 0-100 rating that combines on-court performance, market demand signals, social presence, and risk assessment 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. Risk Assessment rewards durable availability over a full sample (a higher score means lower risk). 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 →