Frictionless Intelligence Funding Report — Athlete Constellation
1) Frictionless Report Overview
Company Snapshot
Industry: SportsTech / AI data & performance analytics
Stage: Pre-Seed
Funding Ask: $350,000 (product completion, academy pilots, GTM)
Geography: San Antonio, Texas (U.S.)
Business Model: B2B2C — academy/team SaaS + premium consumer features
Key Strengths: domain-native team, early academy relationships, clear data capture loops for measurable athlete outcomes.
Challenge: pre-revenue with limited pilot data; needs outcome metrics and pricing clarity before seed syndication.
Market & Positioning
Primary Users: youth academies, school programs, club coaches, recruiters; secondary: athlete families.
Wedge: standardized testing + video/IoT inputs converted to coach-friendly, comparable metrics.
Expansion: sports beyond soccer, recruiter marketplace, NIL tooling.
2) Readiness Assessment
Overall Readiness Score: 63% (Early)
| Category | Impact Weight | Score | Notes |
|---|---|---|---|
| Formation | 10% | 60% | Company formed; basic ops in place. |
| Business Plan | 20% | 62% | Clear thesis; pricing & cohort economics in progress. |
| Pitch | 15% | 65% | Vision compelling; tighten KPI narrative. |
| Product | 15% | 62% | Beta live; needs pilot feedback cycles. |
| Technology | 15% | 64% | ML pipeline outlined; data capture plan maturing. |
| Go-to-Market | 25% | 58% | Pipeline forming; academy LOIs recommended. |
3) Frictionless Recommendations & Insights
Recommendations (Action • Area • Impact)
| Action | Area | Impact |
|---|---|---|
| Secure 2–3 academy pilots with written KPIs | GTM | Validates ARR & retention; unlocks seed |
| Define 5 “golden metrics” (placement, improvement) | Product/Analytics | Data moat & coach trust |
| Package video + testing into coach-ready report | UX | Higher conversion on demos |
| Publish outcomes case study in 90 days | Marketing | De-risks for angels/VCs |
| Map price tiers (academy/club/family) | Monetization | Improves ACV & payback |
Frictionless Insights
- Investor match improves 8–12 pts once first two pilots hit measurable outcomes.
- Keep B2B focus (academies, clubs) for seed; consumer features follow.
- Data standardization + benchmarking can become the defensible moat.
- Target sports VCs/accelerators for distribution & credibility.
4) Frictionless Investor Network Matches — Alamo Angels
| # | Investor | Profile & Focus | Stage Fit | Match % | Strategic Rationale / Notes |
|---|---|---|---|---|---|
| 1 | Alfonso (Poncho) García — 365 Retail / Tech Bloc | Cross-industry (AI, FinTech, CleanTech, Gaming, Marketplace) | Pre-Seed | 73% | AI + consumer-style engagement; wants pilot KPIs to lean in. |
| 2 | Mike Troy — Geekdom Fund | SaaS, AI, data/robotics; Texas focus | Pre-Seed → Seed | 70% | Sports analytics theme; needs academy pilot data. |
| 3 | Luis Martínez — Capital Factory | Texas accelerator; SaaS/AI | Pre-Seed | 67% | Program + mentor network fit; clarify monetization funnel. |
| 4 | Beto Altamirano — Irys | GovTech/ESG; civic impact | Pre-Seed | 64% | Youth opportunity & inclusion angle; wants social KPIs. |
| 5 | Mariano Gonzalez — MGV Capital | AI/automation; data infra | Pre-Seed | 61% | ML architecture support; asks for IP & data plan. |
| 6 | Will Conway — Tech Bloc | Ecosystem/DevOps; San Antonio | Pre-Seed | 59% | Visibility + mentorship; small checks, non-lead. |
5) Angel “View Details” (All 6)
Alfonso (Poncho) García — 73%
Sector: AI / FinTech / CleanTech / Marketplace
Stage: Pre-Seed
HQ: San Antonio, TX
Readiness Score: 63% (Early)
Fit Type: Excellent Fit
Why It Fits: Blends AI data and consumer engagement—exactly Alfonso’s cross-industry sweet spot.
Risks / Friction: Pre-revenue; requires outcome metrics to justify syndication.
- Sector Fit: 8/10
- Stage Fit: 8/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 8/10
What He Adds: Consumer UX + growth narrative; partner intros.
Next Step: 2 pilot LOIs + 90-day outcomes deck; schedule mentorship session.
Mike Troy — 70%
Sector: SaaS / AI / Data
Stage: Pre-Seed → Seed
HQ: San Antonio, TX
Readiness Score: 63% (Early)
Fit Type: Strong Fit
Why It Fits: AI analytics + repeatable B2B workflows match Geekdom Fund patterns.
Risks / Friction: Needs academy pilots and KPI traction.
- Sector Fit: 9/10
- Stage Fit: 7/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 8/10
What He Adds: Data strategy + follow-on syndicate readiness.
Next Step: Share pilot plan; technical diligence session.
Luis Martínez — 67%
Sector: AI / SaaS (Accelerator)
Stage: Pre-Seed
HQ: Austin–San Antonio, TX
Readiness Score: 63% (Early)
Fit Type: Program Fit
Why It Fits: Accelerator + mentor network perfect for early SportsTech AI.
Risks / Friction: Monetization & cohort metrics not yet proven.
- Sector Fit: 8/10
- Stage Fit: 8/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 8/10
What He Adds: Accelerator placement, mentors, investor prep.
Next Step: Submit pilots + KPIs for CF review.
Beto Altamirano — 64%
Sector: GovTech / ESG / Civic Innovation
Stage: Pre-Seed
HQ: San Antonio, TX
Readiness Score: 63% (Early)
Fit Type: Impact Fit
Why It Fits: Youth opportunity + community outcomes dovetail with Beto’s civic thesis.
Risks / Friction: Needs social KPIs & education partners.
- Sector Fit: 7/10
- Stage Fit: 7/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 9/10
What He Adds: Civic/education partnerships; impact storytelling.
Next Step: Frame academy pilot with city/NGO tie-in.
Mariano Gonzalez — 61%
Sector: AI / Automation / Data Infra
Stage: Pre-Seed
HQ: San Antonio, TX (US+LATAM)
Readiness Score: 63% (Early)
Fit Type: Technical Mentor Fit
Why It Fits: Can shape ML pipeline, metrics, and data moat strategy.
Risks / Friction: Wants IP defensibility & pipeline design first.
- Sector Fit: 8/10
- Stage Fit: 7/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 8/10
What He Adds: Architecture reviews, data governance, early GTM discipline.
Next Step: Tech workshop; define 5 “golden metrics”.
Will Conway — 59%
Sector: SaaS / Infrastructure / Ecosystem
Stage: Pre-Seed
HQ: San Antonio, TX
Readiness Score: 63% (Early)
Fit Type: Ecosystem Fit
Why It Fits: Visibility, DevOps mentorship, and network amplification.
Risks / Friction: Small checks; not a lead.
- Sector Fit: 7/10
- Stage Fit: 6/10
- Geography: 10/10
- Founder–Market Fit: 8/10
- Traction: 4/10
- Impact & Thesis Fit: 7/10
What He Adds: PR, events, regional intros.
Next Step: Tech Bloc demo + mentor alignment.
6) Other Investor Matches — External Funds (U.S.-led)
| # | Fund | Profile & Focus | Match % | Strategic Rationale / Notes |
|---|---|---|---|---|
| 1 | Sapphire Sport | Sports/media/culture tech; league LPs | 74% | Prime thematic fit; needs paid pilots. |
| 2 | Courtside Ventures | Sports • Lifestyle • Gaming | 72% | Category specialist; wants user KPIs. |
| 3 | Elysian Park Ventures | Future of sports; Dodgers-backed | 70% | Deep team/league access; require data/IP clarity. |
| 4 | KB Partners | Sports/media/streaming; early | 68% | Good for analytics & infra; wants club customers. |
| 5 | Will Ventures | Sports & human performance | 67% | Outcome-driven; ask for measurable improvements. |
| 6 | Causeway Media Partners | Growth-stage sports media/tech | 60% | Later; track for Series A readiness. |
| 7 | Stadia Ventures | Sports accelerator + VC | 65% | Great accelerator path for pilots. |
| 8 | Techstars Sports | Sports innovation accelerator | 63% | Program check; build LOIs before applying. |
| 9 | HYPE Sports Innovation | Global sports accelerator/fund | 62% | Useful for pilots & exposure. |
| 10 | Initialized Capital | Generalist seed; automation/SaaS | 58% | Not sports-only; needs TAM proof beyond niche. |
7) External Funds — “View Details” (All 10)
1) Sapphire Sport — 74%
Website: sapphireventures.com/sapphire-sport
Focus: Sports / media / culture tech (strategic LPs from leagues)
Check Size: Typically seed–A
Fit: Strong thematic alignment; distribution via LPs
What’s Missing: Paid pilots and outcome KPIs
2) Courtside Ventures — 72%
Website: courtsidevc.com
Focus: Sports • lifestyle • gaming; seed specialist
Check Size: Seed
Fit: Pure category match; playbooks for sports data startups
What’s Missing: Engagement KPIs; academy contracts
3) Elysian Park Ventures — 70%
Website: elysianpark.ventures
Focus: Future of sports: health, culture, commerce, tech
Check Size: Seed–A
Fit: Team & league connections for distribution
What’s Missing: IP defensibility & scalable data model
4) KB Partners — 68%
Website: kbpartners.com
Focus: Sports, media, data infrastructure
Check Size: Seed
Fit: Infrastructure & analytics experience
What’s Missing: First club/academy paying customers
5) Will Ventures — 67%
Website: willventures.com
Focus: Sports & human performance; seed
Check Size: Seed
Fit: Outcome-centric; aligns to athlete improvement
What’s Missing: Quantified outcomes and retention data
6) Causeway Media Partners — 60%
Website: causewaymp.com
Focus: Growth-stage sports media/tech
Check Size: Later-stage checks
Fit: Future scale partner post-revenue
What’s Missing: Revenue scale and repeatable sales motion
7) Stadia Ventures — 65%
Website: stadiaventures.com
Focus: Sports & esports accelerator + VC
Check Size: Accelerator + seed
Fit: High-leverage path for pilots & mentors
What’s Missing: LOIs and clear ROI metrics
8) Techstars Sports — 63%
Website: techstars.com/accelerators/sports
Focus: Sports innovation accelerator (partners vary)
Check Size: Accelerator investment
Fit: Curriculum + partner access
What’s Missing: Confirm active program; secure pilot LOIs first
9) HYPE Sports Innovation — 62%
Website: hypesportsinnovation.com
Focus: Global sports innovation & accelerators
Check Size: Accelerator / venture studio
Fit: Rapid pilots with clubs & federations
What’s Missing: Signed trial partners; coach endorsements
10) Initialized Capital — 58%
Website: initialized.com
Focus: Generalist seed with automation/SaaS strength
Check Size: $200k–$1M seed
Fit: Good for scalable data/SaaS if TAM broadens
What’s Missing: Broader market proof beyond niche academies
8) Investor Outreach & Funding Range
Recommended Initial Targets: Sapphire Sport, Courtside, Elysian Park, KB Partners, Will Ventures (externals) + Alfonso, Mike Troy, Luis (angels).
Expected Round Composition: $350k–$500k pre-seed = lead angel/accelerator ($100k–$150k) + 3–5 angels ($25k–$75k each) + potential program check.
Prerequisites: 2 signed pilots, outcomes dashboard, clear academy pricing, and a 90-day case study.