Focus: Statistical Modeling, Visualization, & Decision Support This track focuses on transforming raw performance data into actionable intelligence. Utilizing tools like Python, Tableau and R, this section showcases advanced projects such as Style of Play clustering, longitudinal player performance reports, and team feedback analysis. The goal is to provide objective evidence that supports coaching intuition and strategic planning.

Focus: Scouting, Opposition Research, & Match Preparation This track highlights the integration of video with tactical theory. It features comprehensive Pre-Match Reports for Youth National Teams (YNT) and collegiate programs, as well as Hudl Insight dashboards. This work bridges the gap between film study and game-day execution, breaking down opponent tendencies and defining tactical roadmaps for success.

Focus: Agentic Workflows, Computer Vision, & Predictive Intelligence This track moves beyond static analysis to build systems that act. It leverages Agentic AI to create autonomous "digital scouts" capable of executing complex workflows—such as watching match footage, identifying specific tactical events, and automatically distributing tailored video clips to coaching staff without human intervention. By combining Computer Vision and Large Language Models (LLMs), this section demonstrates how to automate the loop between data collection, decision-making, and operational execution.

Focus: Tactical Identity, Periodization, & Coaching Methodology This track establishes the structural blueprint for how the team plays. It focuses on developing a cohesive Game Model that defines specific principles and sub-principles for all four phases of the game. By codifying the team's style—from build-up patterns to pressing triggers—this section demonstrates how to create a shared language and consistent methodology that aligns recruitment, academy development, and daily training session design.

Focus: Load Management, Athlete Monitoring, & Physiology This track centers on the physical optimization of the athlete. It involves analyzing physiological datasets—such as GPS loads, heart rate variability, and wellness questionnaires—to minimize injury risk and maximize physical output. The objective is to translate complex performance science into clear benchmarks that inform training periodization and recovery protocols.

Focus: Infrastructure, Workflow Efficiency, & Data Architecture This track addresses the technological backbone of the organization. It covers the implementation and management of Athlete Management Systems (AMS), database architecture, and information workflow. This ensures that data from medical, coaching, and performance departments is centralized, secure, and accessible, streamlining communication across the entire staff.