Hardest Part

Normalizing fragmented, inconsistently formatted high school athletic data and weighting it accurately against the hidden recruiting preferences of individual college coaches.

Min Viable Scope

Focus exclusively on one objective, statistics-driven sport for Division 2 and Division 3 recruiting. Deliberately exclude subjective team sports, Division 1 programs, and name-image-likeness compliance tools.

Cold Start Problem

Coaches ignore the platform without a critical mass of verified recruits, and athletes abandon it without active coach engagement. Break this by launching exclusively in an objective sport, seeding the database with scraped public meet results to guarantee initial athlete density.

Time To First Value

Same-day generation of a realistic algorithmic target school list for athletes upon profile completion

Data Moat Available

true

Technical Difficulty

Moderate

Wedge

The beachhead targets high school baseball and softball players seeking Division III and NAIA placements. These sports feature standardized, quantifiable metrics (exit velocity, pitch speed) that algorithms easily verify, while D3 coaches face acute roster shortages with zero scouting budgets. Expansion proceeds into court sports like volleyball, and finally moves up-market into Division I preferred walk-on placements.

Timing

Computer vision now extracts verified athletic metrics from standard smartphone video, eliminating the need for expensive human scouting. Concurrently, LLMs map these extracted metrics and student transcripts against the historical recruiting profiles of college programs, enabling automated matching at scale.

Why This ICP

Parents of high school athletes act as highly motivated, timeline-constrained buyers who already exhibit extreme willingness to pay for club teams and private coaching. They represent an ideal early-mover segment because securing college admission or athletic scholarships carries immense financial and emotional stakes.

Size Of Prize

~400,000 high school student-athletes actively seeking college placement annually × $1,200 average annual spend on recruiting platforms and showcases = ~$480M addressable prize.

Gap Narrative

High school athletes and lower-division college coaches rely on fragmented highlight emails and expensive showcase events to find roster matches. College programs lack the bandwidth to evaluate thousands of marginal prospects, while athletes lack visibility into actual roster gaps and academic fit requirements. The opportunity provides an algorithmic clearinghouse that evaluates verified athletic metrics and academic profiles, matching them directly to the open roster slots of specific programs.

Defensibility

Proprietary data accumulation forms the primary moat. The platform captures the exact athletic and academic metric combinations that result in verified offers from specific coaches. This closed-loop success data continually refines the matching algorithm, creating a two-sided network effect where better recommendations attract more coaches, which in turn attracts more paying athletes.

Why This Thesis

A Service-as-Software approach aligns with this buyer because parents do not want another DIY software dashboard; they want the concrete outcome of a matched college offer. An agent that automatically ingests player data, identifies matching programs, and executes personalized outreach directly replaces the workflow of a $5,000 human recruiting consultant.

SAM

~$40M-60M focused on NCAA Division I and high-budget Division II programs actively managing transfer portal turnover

SOM

~$4M-8M

TAM

~2,500 North American collegiate athletic departments × ~$50k/yr average spend per department ≈ ~$125M

Growth Rate

~18-25%/yr, driven by accelerating athlete mobility in the NCAA Transfer Portal and the immediate need to fill roster gaps caused by NIL-driven departures

Paid Comparable Spend

~$60k-120k/yr per department spent on legacy scouting video platforms, recruiting databases, and dedicated personnel manually tracking transfer portal entrants

Kill Thresholds

• Portal data ingestion latency > 6 hours
• Algorithm match acceptance rate < 15%
• Fewer than 2 distinct sports programs onboarded per university within 45 days
• Time-to-first-outreach > 24 hours for top-quartile matches

Leading Metrics

• Time-to-first-outreach for newly entered portal athletes
• Algorithm match acceptance rate
• Data ingestion latency from official NCAA portal
• Cross-sport DAU during active transfer windows
• Number of automated outreach sequences initiated daily

What Proves Right

Athletic departments integrate the matching algorithm within 48 hours and completely replace manual transfer portal tracking spreadsheets. Coaches execute daily roster gap queries that trigger automated, same-day outreach to matched portal entrants. Departments lock in at $30,000 annual price points, with multiple sports teams within the same university standardizing on the tool.

What Proves Wrong

Coaches default back to manual video scouting on Hudl and ignore the algorithmic match recommendations. Transfer portal data ingestion lags by more than 12 hours, causing teams to miss critical early-outreach windows. The platform requires manual roster data entry from coaching staffs, depressing weekly active usage below 10 percent.