AI Product · Decision Intelligence · Simulation
War Room AI
A dynasty draft copilot designed to answer: What is the best decision?
— not simply: “Who is the best player?”
War Room AI combines current league state, player data, market information, roster strategy, probabilistic simulation, and a deterministic Expected Draft Value framework before using AI to explain and challenge the recommendation.
- simulations
- 10,000
- Expected Draft Value framework
- EDV
- league integration
- Sleeper
AI explanation layered on deterministic logic.
The product problem
Fantasy draft tools are usually optimized around player rankings. That is useful, but it does not fully answer the question a manager faces during a live draft.
At a specific pick, the real decision depends on who is available now, who may still be available later, roster needs, positional scarcity, tier cliffs, future draft capital, trade value, league format, and the opportunity cost of passing.
The product was designed around that decision.
Product philosophy
War Room AI does not ask: “Who is the best player?”
It asks: “What is the best decision at this specific pick?”
Every recommendation should explain
- Why this player?
- Why now?
- Why not later?
- What happens if I pass?
- What should the next sequence become?
Core architecture
League State
- League settings
- Rosters
- Draft picks
- Traded picks
Data Normalization
Market Data
- ADP
- Rankings
- Value
Player Data
- Position
- Draft capital
- Production
- Testing
User Evaluation
- Personal rank
- Tier
- Sentiment
- Risk / fit
Simulator Engine
- 10,000 simulations
Availability % / Board States
Tier Survival / Value Pockets
Strategist Engine
- Roster strength
- Roster weakness
- Competitive window
- Positional scarcity
- Future draft capital
Decision Engine
- Expected Draft Value (EDV)
Player Value
- Roster fit
- Upside / risk
Urgency
- Scarcity
- Tier cliff
Look Ahead
- Trade value
- Future picks
Recommendation
- + confidence
- + alternatives
- + decision tree
AI Copilot
- Explains / challenges the recommendation
War Room UI
The four core intelligence layers
Scout Engine
Owns player intelligence.
- Scouting summaries
- Strengths / weaknesses
- Ceiling / median / failure cases
- Landing-spot analysis
- Historical comparisons
- Risk, confidence, sources
Facts and calculated comparisons should exist before AI explanation.
Simulator Engine
Runs 10,000 draft simulations.
- Inputs: ADP, consensus rankings, NFL draft capital, team needs, positional runs, league settings, randomness
- Outputs: survival probability, tier survival, board states, value pockets, contingency scenarios
Key question: if I pass now, how likely am I to get this player or a comparable option later?
Strategist Engine
Knows the user's team.
- Evaluates roster strengths, weaknesses, competitive window, positional scarcity, future draft capital
- Produces pick priorities, draft philosophy, need vs. value balance, trade opportunities
Decision Engine
Calculates Expected Draft Value (EDV).
- Factors: player value, roster fit, scarcity, tier urgency, upside, risk, reach penalty, survival probability, future value, alternative-player survival, trade value, future pick sequencing
- Outputs: recommended decision, confidence, alternatives, decision tree, explanation factors
The most important architecture decision
The LLM does not decide the pick from scratch.
The quantitative / deterministic layer produces the underlying recommendation. The AI layer is used to explain, contextualize, compare, challenge, and answer natural-language questions.
This keeps current facts, probabilities, and decision logic separate from generative explanation.
AI explains the evidence. It does not invent the evidence.
Data model / grounding
Sleeper integration imports
- League metadata
- League settings
- Rosters
- Users
- Draft order
- Traded picks
- Player identities
Player intelligence
- Rookie ADP
- Consensus rankings
- Position rank
- NFL draft capital
- College production
- Athletic testing
- Age
- Size
- Landing spot
- Depth chart
- Injury status
- Scouting research
- Current news
Every current external data point retains provenance and freshness information.
The system should not rely on unverified AI memory for current prospect facts.
Live draft workflow
Start Draft
Load League + Current Board
Record Pick
Update Available Player Pool
Recalculate Team / Board State
Refresh Simulation
Recalculate EDV
Refresh Recommendation
Refresh Future Pick Plan
AI Explains What Changed
Next Pick
Every pick can change availability probabilities, positional scarcity, roster needs, future board states, recommendation, confidence, and trade signals.
Product experience
Draft prep
Primary screens
- Tier Board
- Pick Planner
- Player Explorer
- Simulation Center
- AI Chat
Questions it should help answer
- What happens if I wait?
- Which tiers disappear before my next pick?
- Where are the value pockets?
- Can I trade back?
- Which players fit my roster?
- What are my contingency plans?
Draft day
Primary interface
- Draft Board
- Available Players
- AI Recommendation
- Future Planner
Supporting functionality
- Manual pick entry
- Undo
- Autosave
- State recovery
- Recommendation refresh
- Queue
- Future availability
- Scout cards
- Trade-up / trade-back signals
- Branch comparison
Technical architecture
Planned / scoped stack
- Next.js
- TypeScript
- Tailwind CSS
- shadcn/ui
- Supabase
- PostgreSQL
- Prisma
- OpenAI Responses API / AI integration layer
- TypeScript and/or Python simulation logic
Next.js UI
Application / API Layer
Postgres / Supabase
- League data
- Player data
- Market snapshots
- Research snapshots
- Draft sessions
- Simulation runs
- Decision runs
Simulation + Decision Logic
AI Explanation Layer
Designing beyond the happy path
Example end-to-end flow
- 1.Import league
- 2.Review roster
- 3.Rank player
- 4.Run simulation
- 5.Open pick planner
- 6.Start live draft
- 7.Record picks
- 8.Undo a pick
- 9.Receive recommendation
- 10.Refresh page and restore state
Golden scenario
Last Tier 2 QB available
Three similar WRs likely to survive
Severe QB roster weakness
Next pick eight selections away
QB should receive higher EDV
Recommendation explains scarcity + look-ahead
High confidence
My role
I conceived the product, defined the product philosophy, scoped the MVP, designed the core system architecture, worked through the decision framework, specified inputs and outputs, defined the major data structures, and used AI-assisted development workflows to move from concept toward implementation.
What this demonstrates
- AI product design
- Product strategy
- Decision intelligence
- Simulation
- Data architecture
- Requirements
- Deterministic + generative AI
- AI grounding
- Live-state systems
- Product UX
- AI-assisted development
- Edge-case thinking
Status
A personal AI product / MVP architecture and build project.
The 10,000 figure refers to simulation iterations within the scoped simulator, not users.
The interesting part wasn't adding a chatbot to fantasy football. It was separating the decision system from the AI explanation layer.
That architecture makes the recommendation inspectable, testable, and grounded in current data.