Game1 AI - Key Considerations Q4 2025¶
Source: PDF + screenshots uploaded by Shane (2026-02-12), confidential discussion document.
Executive Summary¶
- Industry: Sports > Activity and Performance
- Core hypothesis: Rising investment in youth sports creates demand for sophisticated AI decision-support tools
- Product: Comprehensive AI-powered data layer for sports programs, academies, coaches, parents, and players
Why Youth (not Pro)¶
Initial exploration targeted professional clubs for recruitment/transfer strategy. Pivoted because: - Top-tier clubs stick with existing (even suboptimal) solutions - Lower-tier clubs reluctant to manage multiple vendors - Limited TAM for pro clubs - Constrained willingness to pay for third-party data - Player data ownership is contentious - Hard to build scalable B2B focused exclusively on pro teams
Further Exploration (Youth Focus)¶
- Significant gap in minimally tech-enabled youth sports ecosystem
- No unified, data-driven platforms for contextualizing athletic performance
- Empowers: team execs, coaches (talent ID, evaluation, real-time management) + parents/players (performance context, improvement areas)
- Long-term vision: Game1 data becomes foundation of broader youth sports platform (e.g., centralized content hub for player development)
- Initially soccer, with opportunity to expand to other sports
Overarching Strategic Risks / Clarifications (p.10)¶
Data Moat¶
- Moat stems less from exclusive raw data, more from AI's ability to contextualize, predict potential, and strategically apply lessons
- Key questions: How meaningful is the data advantage? Will it derive from standard measurements, depth of player data, or integrations into team workflows?
- As Game1 accumulates data and observes outcomes, can it generate proprietary insights that meaningfully improve player ID and development? At what scale and time horizon?
- How transferable are predictive insights across age groups, competitive levels, or different sports?
Data vs. Content Business¶
- Long-term value creation depends on using data-driven insights as a foundation for additional solutions -- evolving into a centralized hub within youth sports
- Can Game1 operate as both data and content platform?
- Should it be passive repository or active delivery mechanism (structured training plans, drills, instructional guidance)?
- Adjacent offerings over time: one-on-one coaching, expert marketplaces, peer-to-peer communities?
TAM / Target Market¶
- Should not overly constrain addressable market -- scale depends on serving multiple segments with differing needs without diluting core product
- Can it deliver sufficient value to both top-tier academies and mass market?
- How much differentiation in UX, features, pricing by age group, customer type, competitive level?
- How quickly can it incorporate other sports?
Tactical Risks and Potential Mitigants (pp.11-12)¶
Value of AI-Powered Data Layer¶
Data Intake - Risk: Coaches/parents may see performance differences as intuitive, limiting willingness to adopt - Mitigant: Deliver personalized insights that translate data into support and recommendations
Changes to Data Over Time - Risk: After first assessment, stakeholders become more informed and may perceive diminishing value - Mitigant: Frequently track player progression, highlight new strengths/weaknesses, update benchmarks, adjust training types
Nature of Datasets - Risk: Standardized tests may fail to capture dynamic game-like performance; overemphasizes controlled tasks vs. live competition patterns - Mitigant: Supplement with drills designed to replicate game conditions; offer additional assessments with higher data complexity over time
Measures of Proof - Risk: Prioritizing only data points most predictive of long-term success may eschew highly visible skills; disconnect between platform's core data and how coaches/parents evaluate progress - Mitigant: Incorporate insights as directional indicators of development rather than absolute measures; include areas like passing, shooting, goalkeeping as additional context
Application of AI-Powered Data Layer¶
Testing Apparatus - Risk: Physical on-ground tests have inherent friction (setup, implementation, QC) -- delays "aha moment" - Mitigant: Step-by-step instructional videos, automated data capture, delegate testing to trained staff/vetted third parties including former players as role models
Targeted User / End Customer - Risk: Coaches, parents, players each approach development through different lens -- misalignment risk - Mitigant: Tailor how insights are surfaced based on user's role and competitive context
Content & Assistance - Risk: Being overly cautious in providing actionable suggestions limits prescriptiveness and value - Mitigant: Provide specific guidance as Game1 becomes more ingrained in workflows, especially in mass market where coaches may lack formal training
Expansion into Other Sports - Risk: No historical data for other sports; quality, consistency, and time to operationalize unclear - Mitigant: Aggregate historical data from basketball, football, etc. and train algorithms on those datasets
Open Questions¶
- How will Game1 demonstrate incremental, non-obvious value from its data layer as players develop?
- Which items are most important to solve upfront vs. deferred until clear value hypothesis established?
Competitive Landscape (p.14)¶
Sports Activity and Performance Vendor Composition¶
- Market has evolved as narrow point solutions rather than integrated platforms
- Few vendors achieve scale across product categories or different customer end markets
- Areas of focus: Video Capture/Analysis, Motion Tracking, Performance Analytics, VR Training
Barriers to Full-Service Platform¶
- Required specialization
- Non-standard data and data control concerns
- Buyer fragmentation
- Compliance/licensing requirements
Analogy: RealPage in Real Estate¶
- RealPage became core system of record by: (1) automating back-office, (2) offering products to new segments, (3) expanding product set, (4) becoming end-to-end
- Clear opportunity for Game1 to create a more end-to-end solution within sports industry
Open Question¶
- Does Game1 want to evolve into a comprehensive, scaled vertical performance and activity platform?
U.S. Youth Sports Market Dynamics (pp.16-19)¶
Participation¶
- 30M+ children ages 6-17 in organized sports
- Casual participation climbing: 65% of kids try a sport at least once/year, 55% of 6-17 play sports throughout the year
- 42% of boys and 37% of girls consistently play in structured team formats
- ~30% now play on competitive travel teams
Spending¶
- Families spend $45bn+ annually on youth sports (excluding public spending)
- Average: $1K/child/season, nearly $2K/year; most dedicated athletes exceed $10K/year
- Lessons and instruction = 20% of total youth sports spending per child
- Soccer: $907 total ($237 travel, $210 registration, $169 equipment, $134 lessons, $127 camps, $30 other)
Family Spending by Age¶
- Ages 6-10: $1,112/year
- Ages 11-14: $1,584/year
- Ages 15-18: $1,931/year
Economic Resiliency¶
- Youth sports is resilient discretionary category (health, growth, social/emotional benefits)
- Parents maintain spending even in economic stress (1 in 3 eschew vacations, 1 in 5 find second jobs)
- Primary reasons parents pay more: inflation (32%), more/better equipment (22%), more frequent training (17%)
Parent Psychology¶
- ~5% of HS athletes go on to play NCAA; ~23% of parents believe their child can play college sports at low/high-major level
- NIL in college sports (projected $2bn+ annually) increased perceived sports upside; aspirational parents increased spending on primary sport by 46% since 2019
- Average child spends 3-4 years in a specific team sport, often quitting by age 12
- Soccer: avg age of last activity = 10.4; avg activity length = 3.0 years
Talent Funnel¶
- Youth Sports: 30-35M
- High School: 8-10M
- D3 or D2: 200K
- D1: 150K
Market Fragmentation¶
- 1M+ non-school-affiliated leagues and clubs
- Most locally owned/operated with limited tech
- Fragmented nature creates consolidation opportunity for national platforms
- Barbell market emerging: premium/destination experiences vs. local recreational, fewer developmental pathways in between
- Post-Covid: 3 out of 10 sports parents say their child's program closed, reduced capacity, or merged
Heightened Attention / Regulatory¶
- Federal goal: increase youth sports participation to 63.3% by 2030
- Children's Bill of Rights in Sports advancing
- Illinois and California legislation on quality, access, and equity
- Congressional subcommittee hearing on youth sports crisis
- PE investors under scrutiny for driving up costs
U.S. Youth Soccer Market Dynamics (p.19)¶
Soccer's Position¶
- 2nd most popular primary team sport (15% of kids, behind basketball at 20%)
-
1 in total spending: $5.0bn (vs basketball $4.7bn, dance $3.8bn, baseball $3.2bn)¶
Participation Settings¶
- Soccer: 48% free play, 53% community-based, 26% intramural, 37% interscholastic, 16% travel/club, 13% independent training
- Higher travel/club % than most sports (basketball 15%, baseball 17%)
Key Insight¶
- Soccer uniquely enables structured team play across wide range of levels alongside casual participation, resulting in more sustained engagement and long-term progression
European Elite Youth Soccer Academies (p.20)¶
Commercial Impetus¶
- Combined revenue of Europe's top 5 domestic leagues exceeded EUR 23bn in 2024-2025
- Ownership more willing to invest in youth development for long-term economic returns
- Youth players treated as financial levers with potential future transfer value
Multi-Club Ownership (MCO)¶
- Hundreds of teams now part of MCO structures
- Broader youth scouting network
- Invest in young players at one club to reduce future capital expenditure at another
- Can keep players in-house under shared organizational standards
Transfer Fees¶
- Premier League clubs alone: EUR 16bn in gross transfers since 2020-21 season (net spend EUR 8bn)
- Developing first-team contributors reduces reliance on inflated transfer market
- Identifying right player archetypes early = acquire talent at fraction of peak-market costs
Sustainability Rules (UEFA FFP)¶
- Limits club losses to EUR 60mm over 3-year period (potentially up to EUR 90mm)
- Curtails new squad costs to 70% of club revenue
- Youth infrastructure and development costs are EXCLUDED from sustainability measures -- clubs can freely invest in academies
Value Creation / Path Forward (p.22)¶
Four Layers of Value¶
- Existing Business: End-to-end data platform (diagnostic testing, performance evaluation, progress monitoring)
- Gamify player-facing UI (leaderboards, streaks, challenges)
-
Layer on social interaction among players to build community
-
Business Opportunity: Leverage positioning for adjacent opportunities
- Vertically integrate by rolling up training services or leagues, applying AI to support player development
-
Create investment vehicle with stakes across youth sports properties (see Appendix B)
-
Real-Option Value: Build on internal capabilities to launch new products/services
- Training assistants providing coaches live updates on player activity
-
Real-time monitoring of practice sessions through fixed cameras
-
Potential Value of Equity: Grow by maximizing identifiable elements while capturing optionality upside
- Pursue foreseeable opportunities
- Scale up investments that are difficult to discern at initial launch
Open Question¶
- What steps can Game1 take to become the key operating system for the youth sports market?
Appendix A - Case Studies¶
Steezy Case Study (p.25)¶
- Digital dance app offering online classes, coaching, tutorial videos
- Licenses/produces proprietary content, sells access via subscription
- Illustrates how vertically specialized platforms sustain differentiation against horizontal marketplaces
Marketplace vs. Reseller spectrum: - Marketplaces: scalable but lack control over transaction/delivery - Resellers: more control over experience but higher capital/operating expenses
Why Steezy wins despite free dance content existing elsewhere: - Content Ownership: produces/owns most content, high production standards - Curated Content: tailored across dance categories to individual learners - Integrated Lessons: structured curricula for desired outcomes - Use Case Expansion: specific programs for peripheral use cases
Key takeaway: "A thoughtfully designed, vertically integrated experience for a specific end market, particularly in highly technical, data-driven domains like in Game1's focus area of sports, can outweigh the breadth of content and supplier variety from horizontal marketplaces"
Appendix B - Illustrative Investment Vehicle (pp.27-28)¶
Structure¶
- Sporting organizations swap a small portion of equity/cash flow/revenue from their sports property for interest in a Game1-managed investment vehicle (HoldCo)
- HoldCo holds minority stakes across each asset
- Each org keeps majority control of their asset
Benefits for Sporting Organizations (OpCos)¶
- Near economic equivalent value in shared investment vehicle
- Portfolio diversification, minimize risk of asset concentration
- Support from HoldCo team and tech infrastructure
- Gain non-economic value from exposure to other sports orgs
- Minimal impact on operations
Benefits for Game1¶
- Centralized ownership and support platform
- Equity exposure to high-quality youth sporting assets without outside capital
- Direct access to sports organizations
- Aligns upside with potential customers of core data layer offering
Illustrative Example¶
| League | Revenue | Royalty % | Royalty $ | Vehicle Ownership % | Dividend $ | Dividend as % of Royalty |
|---|---|---|---|---|---|---|
| Youth League #1 (NCSA) | $5M | 1.5% | $75K | 10.0% | $67K | 89.8% |
| Youth League #2 (SMP) | $15M | 1.5% | $225K | 30.0% | $202K | 89.8% |
| Youth League #3 (MLS GO) | $25M | 1.5% | $375K | 50.0% | $337K | 89.8% |
| Game1 AI | - | - | - | 10.0% | $67K | - |
| Total | $45M | - | $675K | 100% | $337K | - |
- Game1 gets 10% ownership of the vehicle (incremental expenses = 0.25% of total royalty exchange)
- Each org owner is rewarded with near-proportionate ownership share based on contributed royalties