Note: This document is a first draft for a business plan. It is intended to provide a comprehensive overview of the business idea, market analysis, financial projections, and organizational structure.

1. Executive Summary

This section provides a concise overview of Deriven Sports’ innovative business model, target market, financial projections, and our overall vision. We aim to revolutionize team sports decision-making through AI-driven predictive analytics, leveraging our proprietary Large Sports Models (LSMs).

1.1 Business Concept

Deriven Sports is developing an AI-based platform designed to deliver advanced predictive analytics and game forecasting in team sports—starting with football. Our solution is powered by LSMs, specifically tuned for game dynamics and performance data. We offer this as a Platform as a Service (PaaS) with modular subscription models and an API-as-a-Service (AaaS) option that seamlessly integrates our analytics into existing sports management systems.

Key features include:

• Customized Solutions: Modular offerings that address specific needs such as squad planning, player development, and tactical analysis.
• Consultation and Support: In-depth customer support and expert consultation to ensure our technology is tailored to meet operational needs.
• Data-Driven Insights: Real-time predictive analytics that empower coaches, analysts, and managers to make informed, strategic decisions.

1.2 Target Audience and Problem Solving

Our platform targets both professional and amateur teams, coaches, analysts, and club managers. By providing predictive insights:

• Strategic Planning: Teams can optimize squad selection and long-term player development.
• Tactical Preparation: Coaches receive real-time game-day recommendations to adjust tactics dynamically.
• Enhanced Decision-Making: Data-driven insights reduce guesswork, leading to improved on-field performance and efficient resource allocation.

where all the decision-making in based on LSM (Large Sports Models) that are trained on historical data and are able to predict future events.

1.3 Financial Objectives and Capital Requirements

In the initial phase, our priority is to develop a functional prototype and onboard our first pilot customers. Our long-term vision is to scale globally and establish the platform as the benchmark for team sports analytics. To support research, development, and partial market entry of Lightweight LSMs, we estimate a capital requirement of €250,000. A small portion will be contributed by the founders, approximately 50% (€125,000) can be covered through Cloud Infrastructure Credits, and the remaining amount will be secured through Government Grants and Subsidies as we progress.

This funding will support:

• Technology and AI development
• Data acquisition and integration
• Marketing and operational expenses

1.4 Vision and Mission

The vision is to revolutionize decision-making in team sports by harnessing cutting-edge AI technology. We set our mission to embed LSMs into the fabric of team sports, providing an intuitive, data-driven platform that benefits both professional and amateur teams through continuous innovation and a modular approach. With a general LSM understanding the game, more use cases can be developed and integrated into the platform.

2. Business Idea and Company Description

This section outlines the core business idea, underlying model, specific use cases, and the innovative features that distinguish Deriven Sports from existing solutions.

2.1 Business Model

At the heart of our business is the development of specialized LLMs for predictive sports analytics. The power of the LSMs can be utilized through three pillars:

1. Platform as a Service (PaaS):
   A subscription-based online service offering ready-to-go modular tools for various sports analytics needs.
2. API as a Service (AaaS):
   An integration-friendly API that allows clubs and organizations to incorporate our AI models directly into their existing systems.
3. Comprehensive Services:
   Beyond software, we provide personalized consultation, training workshops, and ongoing support to ensure optimal utilization of our platform.

Below is a visual representation of our business model:

mindmap
  root((Deriven Sports Business Model))
    PaaS
      sub[Modular Offerings]
      sub[Subscription Models]
    AaaS
      sub[Integration with Existing Systems]
      sub[Flexible API Access]
    Services
      sub[Personalized Consultation]
      sub[Training Workshops]
      sub[Continuous Support]

2.2 Use Cases and Their Benefits

Our LSM platform supports both short-term tactical decisions and long-term strategic planning in sports. Selected use cases include:

Long-Term Planning

• Squad Planning:
  Identification of ideal player combinations and transfer recommendations.
• Player Development:
  Personalized training plans and potential forecasts using historical performance data.
• Team Development:
  Tactical optimization and synergy analyses to refine team formations.

Short-Term Planning

• Game Day Strategy:
  Dynamic lineup optimization, tactical and substitution recommendations.
• Opponent Analysis:
  In-depth analysis of rival tactics and identification of weaknesses.
• Situational Strategy:
  Simulation of game scenarios to prepare for multiple outcomes.

Gamification

• Training Simulations:
  Interactive scenarios that enhance decision-making and skills through gamified training modules.
• Game Engines:
  Integration of our models into simulation engines to create realistic match conditions.

A summary visualization of our use cases is provided below:

mindmap
  root((Use Cases))
    Long-Term Planning
      sub[Squad & Transfer Decisions]
      sub[Player Development]
      sub[Team Tactics]
    Short-Term Planning
      sub[Lineup & Substitution]
      sub[Opponent & Game Analysis]
      sub[Scenario Simulation]
    Gamification
      sub[Interactive Training]
      sub[Simulation Engines]

2.3 Innovations and Unique Selling Propositions (USP)

Our competitive edge lies in:

• Next-Level Predictive Analytics:
  Unlike traditional systems that merely classify events, our LSMs analyze causes and suggest improvements.
• Holistic AI Integration:
  We draw an analogy with the evolution of language models—from classification to understanding—our LSMs predict game scenarios comprehensively.
• Modular Flexibility:
  Our platform adapts to varied sports requirements through a flexible, user-centric design.

We do not conduct independent individual analyses. Instead, similar to how LLMs process language, we leverage LSMs to grasp the sport as a whole. This comprehensive understanding allows us to derive deeper insights into its processes, which in turn serve as a foundation for precise individual analyses.

flowchart TD
    subgraph Traditional[Traditional Approach]
        A[Data] --> B[Data Selection A] --> C[Analysis A]
        A --> D[Data Selection B] --> E[Analysis B]
    end

    subgraph LSM[Deriven Sports Approach]
        F[Data] --> G[LSM]
        G --> H[Context-Aware Analysis A]
        G --> I[Context-Aware Analysis B]
    end

2.4 Legal Structure of the Company

To ensure liability protection and operational flexibility, Deriven Sports is structured as a GmbH (limited liability company).

It is intended that the company will be first founded as a UG (Unternehmergesellschaft) and then transformed into a GmbH (Gesellschaft mit beschränkter Haftung) after the first funding round.

3. Market and Competitive Analysis

This chapter examines the target market, its size, trends, and the competitive landscape, establishing a strategic positioning for our platform.

3.1 Target Audience

Our primary market includes:

• Professional, amateur and academy team sports clubs (e.g., football, basketball, American football)
• Coaches, analysts, and sports managers

Secondary audiences include betting companies, gaming industry, third party platforms and fan engagement platforms.

mindmap
  root((Target Audience))
    Clubs
      sub[Professional]
      sub[Amateur]
      sub[Academies]
    Individuals
      sub[Coaches]
      sub[Analysts]
      sub[Managers]
    Secondary
      sub[Betting Companies]
      sub[Gaming Industry]
      sub[Third Party Platforms]
      sub[Fan Engagement Platforms]

3.2 Market Size and Trends

Recent research indicates that the global sports technology market is expected to grow at a CAGR ~20%, reaching USD 5 billion by 2030. The increased adoption of data analytics, AI, and IoT across sports has been a key driver.

flowchart LR
    A[2025: $1B] -->|CAGR ~20%| B[2030: $5B]

Source: Grand View Research

Key Trends:

• Data-Driven Decision Making:
  Teams increasingly rely on advanced analytics for strategic and tactical planning.
• Wearable Technologies:
  Growth in wearables for real-time performance and health monitoring.
• Fan Engagement Innovations:
  Enhanced stadium experiences through AR, VR, and digital platforms.

3.3 Competitor Analysis

There are 10 key competitors in the market. Key competitors such as Opta, StatsBomb, and InStat focus primarily on data processing and historical classification. Their limitations include:

• Retrospective Analysis:
  Lack of predictive, real-time decision support.
• Isolated Solutions:
  Offering segmented insights without holistic integration.

Deriven Sports differentiates itself with next-generation predictive models that provide actionable, forward-looking insights.

3.4 Market Positioning

Our platform positions itself as a comprehensive, modular, and AI-driven solution that in first place not processes data but provides deep holistic strategic insights—enabling teams to achieve a competitive advantage.

4. Marketing and Sales Strategy

This chapter outlines our strategy for market positioning, pricing, and customer engagement.

4.1 Pricing Model

Our flexible pricing structure includes:

• Monthly/Annual Subscriptions:
  Scalable options tailored for clubs of all sizes.
• Pay-per-Use Options:
  Ensuring customers pay only for the analytics they need.
• One-Time Analysis Fees:
  For specialized reports (e.g., player development studies).

4.2 Marketing Channels

We plan to leverage:

• Digital Marketing:
  Targeted campaigns on social media (LinkedIn, Instagram), and specialized sports tech forums providing insights in the research field of LSMs.
• Strategic Partnerships:
  We plan to have collaborations with local clubs and sports academies to pilot our technology. Also partnerships with data providers to ensure high-quality data without the need of data acquisition.
• Industry Events:
  Participation in sports technology fairs and conferences to showcase our innovations.

4.3 Sales Channels

Sales will be executed via:

• Direct Sales:
  Engaging with clubs and sports organizations through personalized outreach.
• Online Self-Service Portal:
  Enabling easy subscription and integration via our API.
• Partnership Networks:
  Collaborations with technology vendors and data providers to expand our reach.

4.4 Customer Acquisition and Retention

To acquire and retain customers, we will:

• Launch pilot projects with regional clubs to gather early feedback.
• Provide regular software updates, training sessions, and workshops.
• Develop customer success programs to build long-term relationships.

5. Organization and Team

A small and dedicated, agile team will drive our innovation. We focus on flat hierarchies and cross-functional collaboration to ensure rapid development and market responsiveness. Also we use automated processes in any possible way to ensure a high efficiency, fast development and reduce of repetitive tasks.

5.1 Organizational Structure

Our core team comprises experts in AI, sports analytics, and marketing. The flat organizational structure ensures fast decision-making and continuous innovation. Currently the team is still in the making.

Research focuses on the development of the Large Sports Models utilizing the latest AI technologies.

Products are responsible for the development of the platform and the integration of the LSMs.

Marketing & Sales drive customer acquisition, market positioning, and strategic partnerships.

flowchart TB
  CEO[Thomas Kreidl, CEO] --> R
  CEO --> P
  CEO --> M

  R[Research, CEO]
  P[Products, CTO]
  M[Marketing & Sales, CMO]

5.2 Founders and Key Personnel

• Thomas Kreidl (Founder):
  With extensive experience in AI and team sports, Thomas’s vision underpins our technological and strategic direction.
• Key Executives:
  Specialists in product development, operations, and sales lead our daily functions and strategic initiatives. ??

5.3 Company Location

We adopt a flexible, location-independent working model to foster international collaboration and rapid innovation. Our headquarters are in Crailsheim, Germany.

5.4 External Partners

Our partnerships with data providers, sports tech companies, and industry associations ensure access to high-quality data and accelerate product development.

6. Product and Service Development

This chapter details the development lifecycle of our platform.

6.1 Roadmap for Product Development

Our development is structured into the following phases:

1. POC (Proof of Concept):
   A very limited-scope test version to validate model functionality.

• Mocked-POC: Build on mocked data to ensure the model architecture.
• Real-World-POC: Build on real-world data to handle real-world challenges.

2. Prototype Development:
   Building a fully functional prototype based on POC insights.
3. Evaluation:
   An in-depth assessment to confirm the viability of the concept.
4. Early Pilot Projects:
   Drafted Real-world implementation and feedback collection.
5. Final Pilot Projects: Finalizing the product and preparing for open market entry.
6. Growth Phase: Scaling the platform and expanding the customer base.

A simplified product development timeline:

gantt
    title Product Development Timeline
    dateFormat  YYYY-MM
    axisFormat %Y-%m
    tickInterval 6month
    section Phases
    todayMarker off
    
    POC :a1, 2025-07, 6M
    Prototype :a2, after a1, 12M
    Early Pilot :a3, 2026-07, 12M
    Final Pilot :a4, after a3, 12M
    Growth :a5, after a4, 12M

6.2 Current Development Status

We are currently in the POC, focusing on model architecture and data integration. This foundational work will support our subsequent basis of the LSM.

6.3 Planned Future Developments

Future updates include:

• New versions of the LSM with enhanced features.
• Integration of real-time data for dynamic analytics.
• Expansion to additional team sports (e.g., basketball, handball).
• Continuous platform updates driven by user feedback and emerging market trends.

7. Financial Plan

Our financial strategy outlines startup financing, revenue projections, cost structures, and break-even analysis for different phases of growth.

7.1 Startup Capital and Financing

We plan to secure funding through different phases:

• POC + Prototype Phase

  • Founder Investments (FI):
    Initial capital from personal resources without external funding.
  • Cloud Infrastructure Credits (CIC):
    Leveraging partnerships (e.g., Azure credits worth approximately €150,000).
    • Microsoft
    • Google Cloud
    • AWS

• Pilot Phase

  • Government Grants and Subsidies (LI): For AI innovation and technology development.
    • Deep Tech
    • Horizon Europe
    • Innovation Council
    • Digital Europe Programme

• Growth Phase

  • Angel Investors and VC Funding (VCAI):
    Raising additional funds to support rapid growth if needed.

7.2 Revenue Projections

Our phased revenue targets are:

• Year 1.5: POC + Prototype Phase: No revenue generation, focus on product development and validation.
• Year 2: Early Pilot Phase Initial revenue from pilot projects and early customer onboarding. Up to €100K in revenue.
• Year 3: Final Pilot Phase Scaling revenue through platform subscriptions and API integrations. Up to €500K in revenue.
• Year 4: Growth Phase Accelerated revenue growth through expanded customer base and new product features. Up to €1.5M in revenue.

A simplified revenue forecast diagram:

gantt
    title Revenue Projections (€)
    dateFormat  YYYY-MM
    axisFormat %Y-%m
    tickInterval 6month
    section Phases
    todayMarker off
    
    POC + Prototype :a1, 2025-07, 18M
    Early Pilot :a2, 2026-07, 12M
    Final Pilot :a3, after a2, 12M
    Growth :a4, after a3, 12M

7.3 Cost Structure

The costs vary across the different phases of development:

7.4 Profit and Loss Statement

Early years may show losses due to high R&D investments. As the platform scales, operating margins are expected to improve significantly.

7.5 Liquidity and Break-even Analysis

Our cash flow management strategy includes:

• Regular monitoring of cash inflows and outflows.
• Maintaining a financial buffer to cover unforeseen expenses.
• Break-even analysis based on scaling sales, with expected profitability by Year 3.

8. Risk Analysis

We identify potential risks and outline mitigation strategies.

8.1 Internal Risks

• High Development Complexity:
  Intensive R&D required for building robust LSMs.
• Model Reliability:
  Ensuring consistent performance and accuracy of predictions.
• Capital Constraints:
  Managing cash flow and securing funding for growth and LSM development.

8.2 External Risks

• Data Quality and Acquisition:
  Dependency on external data sources and maintaining data integrity.
• Market Competition:
  Established providers may intensify their predictive analytics offerings.
• Regulatory Issues:
  Compliance with data privacy and sports data regulations.

8.3 Mitigation Measures

• Prototype Validation:
  Conducting rigorous POC and pilot projects to fine-tune models.
• Diversified Data Partnerships:
  Establishing multiple sources to mitigate data dependency risks.
• Continuous Improvement:
  Ongoing R&D to stay ahead of regulatory changes and market demands.

9. Timeline and Milestones

A clear timeline ensures structured product development and market entry.

9.1 Key Milestones

1. POC Development:

• Focus on developing and validating the initial Proof of Concept.
• Access to cloud infrastructure credits and initial data acquisition.

2. Company Founding:
   Founding the company “Deriven UG” and establishing the core team. Branding and marketing strategy development.
3. Prototype Development: Develop a fully functional prototype incorporating POC feedback. Evaluate the business model and the functionality of the LSM Lite.
4. Company Transform:
   Transform the company into a GmbH and secure initial funding.
5. Early Pilot:
   First version of the Large Sports Model for football and integration into the platform.
6. Final Pilot:
   Advanced version of the LSM for football and official launch of the scalable SaaS / AaaS platform to use the LSM-F within the platform.
7. Grow Onboard initial customers through targeted pilot projects and market outreach.

9.2 Overall Timeline

We plan to scale the platform within 4 years, ensuring iterative product improvements and expanding customer relationships over time.

10. Appendices

TODO

Supplementary documents include:

• Founders’ Resumes

• Market Studies and Survey Results (demonstrating the demand for AI in sports)

• Detailed Financial Overviews (tables, charts, and assumptions)

• Sample Legal Contracts and Pilot Agreements

• Actual progress in the development of the POC and Prototype

• Detailed product roadmap and feature list

• Detailed technical documentation of the LSMs

This business plan positions Deriven Sports at the intersection of sports and technology by capitalizing on growing market trends and delivering innovative AI-driven solutions. With robust R&D, strategic partnerships, and a scalable business model, Deriven Sports is poised to set a new standard in team sports analytics.