AI Integration into Youth Soccer
Budget: $3,000 – $5,000 USD
Our platform will be built to solve one of the biggest challenges in U.S. soccer: coaches simply don’t have enough time. Between balancing jobs, families, and teams, there are only so many hours in the week to analyze games, design training sessions, and track long-term development. The result is that player development often suffers, and clubs struggle to stand out. This platform changes that dynamic.
At its core, it’s designed to save coaches time without sacrificing quality. Coaches can upload a match clip or full game and instantly receive insights tied to global best practices and their club’s identity. From those insights, the system generates complete, age-appropriate sessions or microcycles that are rooted in tactical periodization, positional play, and proven development principles. Instead of scrambling to piece together sessions after work, coaches get elite-level training plans in minutes — often sharper and more consistent than what they could produce on their own.
But video is not a requirement. Coaches can also create sessions on demand with simple text prompts. They can type: “Generate me a session for pressing in a 4-3-3,” or “We haven’t been finishing well the last three games — give me two sessions to fix this.” The platform uses that input to design customized, high-quality training sessions complete with drills, diagrams, progressions, and coaching points. This flexibility ensures coaches always get what they need, whether or not they’ve filmed their matches.
The information is also fluid and constantly evolving. Sessions adapt based on the latest game data, coach feedback, and club identity. As teams progress and their needs shift, the training recommendations shift with them. At the club level, directors can review collective performance metrics — from trends in 4v4 grassroots matches to advanced team analytics in 11v11 — to track development, align philosophy across age groups, and reinforce their competitive edge.
Ultimately, this is more than an AI tool. It’s a complete system that makes every coach more effective, accelerates player development, and equips clubs with the data and flexibility to grow sustainably. Coaches gain back their time, sessions improve in quality, and clubs can clearly measure and showcase their progress season after season.
Screening Questions
RAG & Knowledge Base
Have you built a project that used retrieval-augmented generation (RAG) before?
If yes, briefly explain how you structured the knowledge base and how you ensured the AI always referenced it (and didn’t just hallucinate).
JSON Validation
Our system requires outputs in strict JSON format (drills, sessions, insights).
How would you enforce JSON schema validation and retries so that all responses are usable?
Computer Vision (Soccer Context)
What experience do you have with YOLO, ByteTrack, or other object detection/tracking models?
How would you extract simple soccer signals (e.g., build-out turnovers, counters, final-third entries) without building a full broadcast-level tagging system?
Age-Specific Sessions
This platform must generate sessions from 4v4 grassroots (U6–U9) up to 11v11 academy (U15+).
How would you ensure the system enforces age-appropriate loads, durations, and drill complexity?
Club Identity Integration
Each club can upload its own style of play, formations, and preferred drills.
How would you design the system so outputs blend global best practice with club-specific identity while still respecting age and player constraints?
Tech Stack
The preferred stack includes FastAPI (Python), Postgres + pgvector, YOLO/ByteTrack, GPT-4o, Bubble or FlutterFlow front-end.
Which parts of this stack are you most confident in, and what alternatives (if any) would you suggest?
Past Work Examples
Please share a repo, demo, or case study showing your experience with one or more of the following:
LLMs with structured outputs
RAG or vector databases
Computer vision in sports or movement tracking
Project Understanding
In 3–4 sentences, explain how you would build the MVP flow: Coach uploads a clip → system analyzes → coach gets insights and a training session PDF.
At its core, it’s designed to save coaches time without sacrificing quality. Coaches can upload a match clip or full game and instantly receive insights tied to global best practices and their club’s identity. From those insights, the system generates complete, age-appropriate sessions or microcycles that are rooted in tactical periodization, positional play, and proven development principles. Instead of scrambling to piece together sessions after work, coaches get elite-level training plans in minutes — often sharper and more consistent than what they could produce on their own.
But video is not a requirement. Coaches can also create sessions on demand with simple text prompts. They can type: “Generate me a session for pressing in a 4-3-3,” or “We haven’t been finishing well the last three games — give me two sessions to fix this.” The platform uses that input to design customized, high-quality training sessions complete with drills, diagrams, progressions, and coaching points. This flexibility ensures coaches always get what they need, whether or not they’ve filmed their matches.
The information is also fluid and constantly evolving. Sessions adapt based on the latest game data, coach feedback, and club identity. As teams progress and their needs shift, the training recommendations shift with them. At the club level, directors can review collective performance metrics — from trends in 4v4 grassroots matches to advanced team analytics in 11v11 — to track development, align philosophy across age groups, and reinforce their competitive edge.
Ultimately, this is more than an AI tool. It’s a complete system that makes every coach more effective, accelerates player development, and equips clubs with the data and flexibility to grow sustainably. Coaches gain back their time, sessions improve in quality, and clubs can clearly measure and showcase their progress season after season.
Screening Questions
RAG & Knowledge Base
Have you built a project that used retrieval-augmented generation (RAG) before?
If yes, briefly explain how you structured the knowledge base and how you ensured the AI always referenced it (and didn’t just hallucinate).
JSON Validation
Our system requires outputs in strict JSON format (drills, sessions, insights).
How would you enforce JSON schema validation and retries so that all responses are usable?
Computer Vision (Soccer Context)
What experience do you have with YOLO, ByteTrack, or other object detection/tracking models?
How would you extract simple soccer signals (e.g., build-out turnovers, counters, final-third entries) without building a full broadcast-level tagging system?
Age-Specific Sessions
This platform must generate sessions from 4v4 grassroots (U6–U9) up to 11v11 academy (U15+).
How would you ensure the system enforces age-appropriate loads, durations, and drill complexity?
Club Identity Integration
Each club can upload its own style of play, formations, and preferred drills.
How would you design the system so outputs blend global best practice with club-specific identity while still respecting age and player constraints?
Tech Stack
The preferred stack includes FastAPI (Python), Postgres + pgvector, YOLO/ByteTrack, GPT-4o, Bubble or FlutterFlow front-end.
Which parts of this stack are you most confident in, and what alternatives (if any) would you suggest?
Past Work Examples
Please share a repo, demo, or case study showing your experience with one or more of the following:
LLMs with structured outputs
RAG or vector databases
Computer vision in sports or movement tracking
Project Understanding
In 3–4 sentences, explain how you would build the MVP flow: Coach uploads a clip → system analyzes → coach gets insights and a training session PDF.