
Southwestern Athletic Conference
Women's Basketball · 12 teams · 177 scored athletes
Last updated: November 13, 2026 · refreshed daily
Avg NIL Score
Teams

Jackson State Lady Tigers
15 scored players
45.8
Avg

Arkansas-Pine Bluff Golden Lions
13 scored players
44.4
Avg

Grambling Lady Tigers
19 scored players
43.6
Avg

Alabama A&M Bulldogs
15 scored players
42.0
Avg

Southern Jaguars
16 scored players
41.7
Avg

Alabama State Lady Hornets
15 scored players
40.8
Avg

Alcorn State Lady Braves
14 scored players
40.5
Avg

Texas Southern Tigers
14 scored players
40.3
Avg

Bethune-Cookman Wildcats
12 scored players
40.2
Avg

Florida A&M Rattlers
17 scored players
39.6
Avg

Mississippi Valley State Devilettes
16 scored players
37.3
Avg

Prairie View A&M Lady Panthers
11 scored players
36.8
Avg
Top Athletes

Kiarra Henderson
G · Alcorn State Lady Braves

Nakia Cheatham
G · Alcorn State Lady Braves

Jailah Pelly
G · Arkansas-Pine Bluff Golden Lions

Indiya Bowen
G · Arkansas-Pine Bluff Golden Lions

Princess Anderson
G · Grambling Lady Tigers

Shaniah Nunn
SF · Grambling Lady Tigers

Tahnyjia Purifoy
PG · Florida A&M Rattlers

Khaniah Gardner
F · Arkansas-Pine Bluff Golden Lions

Jaileyah Cotton
G · Jackson State Lady Tigers

Caylee Simpson
G · Alabama State Lady Hornets
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). Scores recalculate daily after the overnight game sync, with a full data refresh every Sunday.
Methodology note: women's basketball calibration is developing as the dataset grows. Treat scores as directional while the sample expands.
Read the full methodology →