The matching engine that learned from 40,000 deals.
RightMatch™ ranks every athlete for every brief and every brief for every athlete. Built with brand recruiters and audited quarterly for fairness.
Four stages, one ranking
- 1
Candidate generation
Filter the full corpus (10K+ athletes) by hard constraints from the brief: sport, division, geography, restricted categories.
- 2
Feature scoring
12 signals per candidate (Sportfolio Score™ inputs, brand-fit history, audience match, price band).
- 3
Re-ranking
Learned re-ranker (gradient-boosted on outcome data) optimizes for closed-deal probability, not impressions.
- 4
Explain
Every surfaced athlete comes with a Match Card listing the top 3 reasons. No black box.
The 12 features the model sees
Hard fit (sport, division, state)
Hard filter. A campaign for D1 football in Texas never sees a JUCO swimmer in Oregon.
Sportfolio Score™ tier
Composite athletic + audience + reliability signal.
Audience overlap
Geographic and demographic overlap with the brand's stated target audience.
Brand-fit history
Past deals in same category + post-campaign sentiment lift.
Content velocity
Recent posting cadence and engagement trend (rising vs. flat).
Why we built our own ranker
Surfaces the same 50 athletes for every brief. Acceptance rate: 18%. Repeat-deal rate: 9%.
Surfaces 10 highly-targeted athletes per brief, refreshed nightly. Acceptance rate: 52%. Repeat-deal rate: 41%.
Guardrails we publish
No protected attributes
Model never sees race, religion, political affiliation, sexual orientation, or anything we cannot measure cleanly.
Fairness audits, quarterly
We benchmark surface-rate parity across women's sports, HBCUs, and non-revenue programs. Reports public on /trust-safety.
Athletes can opt out
Any athlete can turn off RightMatch™ surfacing for any category. Brands still see you in search; you just won't auto-rank.
Common questions
See RightMatch™ on a real brief.
20-minute live walkthrough — we run a real brief from your category and show the top 10 surfaced athletes.