# Session Summary: Phase 2 Game Engine Planning

**Date**: 2025-10-22
**Time**: ~23:00 - 01:13
**Duration**: ~2 hours
**Branch**: `implement-phase-2`
**Status**: Planning Complete ✅ - Ready to Begin Implementation

---

## Session Overview

### Primary Objectives
1. Plan Phase 2: Game Engine Core implementation (Weeks 4-6)
2. Design in-memory state management architecture
3. Document all player data fields for caching
4. Create detailed weekly implementation plans

### Technologies Involved
- **Backend**: FastAPI, Python 3.13, Pydantic, SQLAlchemy (async)
- **Database**: PostgreSQL (10.10.0.42:5432)
- **State Management**: In-memory Python dictionaries + async DB persistence
- **Testing**: pytest, automated Python tests

### Overall Outcome
✅ **Successfully completed comprehensive planning for Phase 2**
- Created 4 detailed planning documents
- Resolved key architectural questions about state caching
- Documented complete player data model from Discord bot
- Established "measure twice, cut once" approach
- Ready to begin Week 4 implementation

---

## Current State

### Active Todos (from TodoWrite)

**Completed:**
1. ✅ Create detailed Phase 2 implementation plan documents
2. ✅ Document all player data fields to cache in memory

**In Progress:**
3. 🔄 Review player data catalog and supplement with any missing fields

**Pending (Week 4):**
4. ⏳ Week 4: Create Pydantic game state models with rich player cache
5. ⏳ Week 4: Implement State Manager (in-memory game state)
6. ⏳ Week 4: Implement database operations layer
7. ⏳ Week 4: Build state recovery mechanism

**Pending (Week 5):**
8. ⏳ Week 5: Implement cryptographic dice system
9. ⏳ Week 5: Build play resolver (simplified charts)
10. ⏳ Week 5: Create game engine core logic
11. ⏳ Week 5: Implement rule validators

**Pending (Week 6):**
12. ⏳ Week 6: Create polymorphic player model architecture
13. ⏳ Week 6: Implement league configuration system
14. ⏳ Week 6: Build result charts (d20 tables)
15. ⏳ Week 6: Create league API client
16. ⏳ Week 6: End-to-end testing of complete at-bat flow

### Running Services
- Backend FastAPI: Multiple instances running on port 8000 (multiple background processes)
- Frontend SBA: Running on port 3000
- Frontend PD: Running on port 3001

### Git Status
```
Branch: implement-phase-2
Modified: .claude/implementation/02-game-engine.md
Recent commits:
  d8a43fa - CLAUDE: Complete Phase 1 - Frontend Infrastructure Setup
  fc7f53a - CLAUDE: Complete Phase 1 backend infrastructure setup
  5c75b93 - CLAUDE: Initial project setup - documentation and infrastructure
```

### Key Files Being Worked On
- `.claude/implementation/02-game-engine.md` (updated with approach)
- `.claude/implementation/02-week4-state-management.md` (created)
- `.claude/implementation/02-week5-game-logic.md` (created)
- `.claude/implementation/02-week6-league-features.md` (created)
- `.claude/implementation/player-data-catalog.md` (created)

---

## Changes Made

### Files Created

1. **`.claude/implementation/02-week4-state-management.md`**
   - Comprehensive Week 4 implementation plan
   - Pydantic game state models specification
   - StateManager class design
   - DatabaseOperations async layer
   - State recovery mechanism
   - Complete test specifications

2. **`.claude/implementation/02-week5-game-logic.md`**
   - Week 5 implementation plan
   - DiceSystem with cryptographic RNG
   - PlayResolver with simplified charts
   - GameEngine orchestration
   - Rule validators
   - Integration test framework

3. **`.claude/implementation/02-week6-league-features.md`**
   - Week 6 implementation plan
   - Polymorphic player models (BasePlayer → SbaPlayer/PdPlayer)
   - League configuration system
   - Result charts for SBA and PD
   - LeagueApiClient implementation
   - End-to-end testing strategy

4. **`.claude/implementation/player-data-catalog.md`**
   - **CRITICAL REFERENCE**: Complete player data field specifications
   - Batting card data (8 basic fields + 54 rating values per player)
   - Pitching card data (7 basic fields + 60 rating values per player)
   - Defensive ratings (5 fields per position, multi-position support)
   - Memory usage analysis (~500-700 bytes per player)
   - Usage examples for gameplay scenarios
   - Data loading and caching strategies

### Files Modified

1. **`.claude/implementation/02-game-engine.md`** (lines 171-209)
   - Added "Implementation Approach" section
   - Documented key decisions from user
   - Added links to detailed weekly plans
   - Updated status to "In Progress - Planning Complete"

### No Code Files Created/Modified
This session was **planning only** - no implementation code written yet.

---

## Key Decisions & Discoveries

### Architectural Decision: Rich In-Memory Caching

**Decision**: Cache complete player objects with ALL ratings in memory, not minimal state.

**Rationale** (from Discord bot experience):
- Gameplay requires frequent access to:
  - Player images for display
  - Defensive ratings for x-checks (unpredictable position)
  - Catcher passed ball / pitcher wild pitch for chaos rolls
  - Stealing ratings, bunting ratings, etc.
- Memory cost is negligible: ~10-15KB per game (20 players × ~500-700 bytes)
- 100 concurrent games = < 2MB total
- Discord bot suffered from slow DB queries - this solves that

**Pattern Established**:
```python
class CachedPlayer(BaseModel):
    # Complete player data cached
    - Identity & display (12 fields)
    - Batting attributes (8 fields)
    - Batting ratings vL and vR (54 values total)
    - Pitching attributes (7 fields)
    - Pitching ratings vL and vR (60 values total)
    - Defense ratings per position (Dict[str, DefenseRatings])

# Cache in GameState
home_lineup: Dict[int, CachedPlayer]  # {lineup_id: player}
away_lineup: Dict[int, CachedPlayer]
```

### Decision: SBA First, PD Second

**Approach**: Build each component for SBA league first, learn lessons, apply to PD.

**Rationale**:
- SBA is simpler (fewer fields, manual result selection)
- PD adds complexity (auto-selection via scouting model)
- Ensures base case works before adding complexity
- Matches user's request

### Decision: Automated Python Testing (No WebSocket UI Tests in Phase 2)

**Approach**: Test via Python scripts and unit/integration tests, not through UI.

**Benefits**:
- Faster iteration during development
- Easier to debug game logic
- Can test edge cases more thoroughly
- UI testing comes in Phase 3

**Test Script Pattern**:
```python
# scripts/test_game_flow.py
async def test_at_bat():
    state = await state_manager.create_game(...)
    await game_engine.start_game(game_id)
    await game_engine.submit_defensive_decision(...)
    await game_engine.submit_offensive_decision(...)
    result = await game_engine.resolve_play(game_id)
```

### Decision: Hybrid State Management

**Pattern**:
```
User Action → WebSocket → Game Engine
                ↓
         Update In-Memory State (fast, <200ms)
                ↓
         Async Write to PostgreSQL (non-blocking, <100ms)
                ↓
         Broadcast via WebSocket
```

**Data Consistency Strategy**:
- In-memory state is source of truth for active games
- Database is async backup + historical record
- On crash: Recover from DB plays, rebuild in-memory cache
- Write-through cache pattern

### Discovery: Discord Bot Data Model Complexity

**Finding**: Paper Dynasty Discord bot has extensive player data:
- **Batting**: 27 rating fields × 2 platoon splits (vs LHP/RHP) = 54 values
- **Pitching**: 30 rating fields × 2 platoon splits (vs LHB/RHB) = 60 values
- **Defense**: Multi-position support (player can have ratings for 2-8 positions)
- **Chaos Events**: Wild pitch, passed ball, balk, pickoff ratings
- **X-Checks**: 9 position-specific x-check probabilities on pitcher cards

**Implication**: Must cache ALL this data for fast gameplay. Initial "minimal state" approach would have failed.

### Pattern: Result Selection Models

**SBA League**:
- Players see dice roll FIRST
- Select from available results on chart
- Manual decision required

**PD League**:
- Flexible approach
- Manual selection OR auto-resolution via scouting model
- Scouting model uses detailed BattingCardRatings/PitchingCardRatings

---

## Problems & Solutions

### Problem: Initial Architecture Too Simple

**Issue**: Original plan had minimal in-memory state (just current batter ID, outs, score).

**Discovery**: User explained real gameplay needs:
- "On each play there is a chance for a chaos roll if there are baserunners - need catcher passed_ball and pitcher wild_pitch"
- "X-check plays require calling <RandomDefender>.range and .error values"
- "Need to display player images for batter, runners, pitcher, catcher"

**Solution**: Switched to rich player caching with complete data model.

**Lesson**: Real-world production experience (Discord bot) revealed requirements that weren't obvious from specs.

### Problem: Unclear Data Requirements

**Issue**: Didn't know all the fields that needed to be cached.

**Solution**: Deep dive into Discord bot codebase:
- Read `paper-dynasty/database/app/routers_v2/battingcards.py`
- Read `paper-dynasty/database/app/routers_v2/pitchingcards.py`
- Read `paper-dynasty/database/app/routers_v2/battingcardratings.py`
- Read `paper-dynasty/database/app/routers_v2/pitchingcardratings.py`
- Read `paper-dynasty/database/app/routers_v2/cardpositions.py`
- Read `paper-dynasty/database/app/routers_v2/players.py`

**Result**: Created comprehensive `player-data-catalog.md` documenting all 100+ fields.

### Problem: Performance vs Consistency Trade-off

**Issue**: How to balance fast gameplay with data consistency?

**Solution**: Async write-through cache
- In-memory cache updated synchronously (fast)
- Database write happens asynchronously (non-blocking)
- On crash, rebuild from database plays

**Guarantees**:
- Response time < 500ms (in-memory reads)
- Data persisted within seconds (async writes)
- Full recovery possible from database

---

## Technology Context

### Database Server
- **Location**: `10.10.0.42:5432`
- **Database**: `paperdynasty_dev`
- **User**: `paperdynasty`
- **Connection**: `postgresql+asyncpg://paperdynasty:PASSWORD@10.10.0.42:5432/paperdynasty_dev`

### Python Environment
- **Version**: Python 3.13.3
- **Virtual Env**: `backend/venv/`
- **Activation**: `source venv/bin/activate` (from backend directory)

### Critical Dependencies
- **Pydantic**: v2.10.6 (data validation)
- **SQLAlchemy**: v2.0.36 (async ORM)
- **asyncpg**: v0.30.0 (PostgreSQL async driver)
- **Pendulum**: v3.0.0 (datetime - ALWAYS use instead of Python datetime)
- **greenlet**: Required for SQLAlchemy async

### League API References
- **SBA API**: Integration pending (Week 6)
- **PD API**: Discord bot at `/mnt/NV2/Development/paper-dynasty/database/`

### Database Models (Phase 1 Complete)
- `Game`: UUID primary key, AI support, team tracking
- `Play`: 25+ statistics fields, player FKs, on_base_code bit field
- `Lineup`: Substitution tracking, fatigue flags
- `GameCardsetLink`: PD cardset validation
- `RosterLink`: PD roster management
- `GameSession`: WebSocket state tracking

---

## Next Steps

### Immediate Actions (User Review Required)

1. **Review Player Data Catalog** (`.claude/implementation/player-data-catalog.md`)
   - Check for missing fields
   - Verify field types
   - Confirm usage scenarios
   - Answer questions at end of document

2. **Answer Architecture Questions**:
   - **Variant Cards**: Are these different versions of same player in same game?
   - **Offensive Column**: What is `offense_col` used for?
   - **SBA Simplifications**: Cache ratings or truly use simplified charts?
   - **Scouting Data**: Is BattingCardRatings the scouting data or separate?

### Once Review Complete → Begin Week 4

**First Implementation Task**: Create Pydantic game state models
- Location: `backend/app/models/game_models.py`
- Models needed:
  - `CachedPlayer` (complete player with all ratings)
  - `BattingAttributes`, `BattingRatings`
  - `PitchingAttributes`, `PitchingRatings`
  - `DefensivePosition`
  - `GameState` (with rich lineup caches)
  - `RunnerState`, decision models

**Test First**: Write unit tests before implementation
- `tests/unit/models/test_game_models.py`
- Test model validation
- Test helper methods
- Test platoon split lookups

### Week 4 Complete Deliverables
- ✅ Pydantic models with full player data
- ✅ StateManager with in-memory game states
- ✅ DatabaseOperations for async persistence
- ✅ State recovery from database
- ✅ All tests passing

### Week 5-6 (After Week 4)
Follow plans in:
- `.claude/implementation/02-week5-game-logic.md`
- `.claude/implementation/02-week6-league-features.md`

---

## Reference Information

### Critical Planning Documents

1. **`.claude/implementation/02-game-engine.md`**
   - Phase 2 overview
   - Implementation approach and decisions
   - Links to weekly plans

2. **`.claude/implementation/02-week4-state-management.md`**
   - Complete Week 4 plan with code examples
   - Pydantic models specification
   - StateManager design
   - Database operations layer
   - Testing strategy

3. **`.claude/implementation/02-week5-game-logic.md`**
   - Dice system (cryptographic d20 rolls)
   - Play resolver with result charts
   - Game engine orchestration
   - Rule validators
   - Integration tests

4. **`.claude/implementation/02-week6-league-features.md`**
   - Polymorphic player models
   - League configurations (SBA vs PD)
   - Result charts
   - API client
   - E2E testing

5. **`.claude/implementation/player-data-catalog.md`** ⭐ **MOST CRITICAL**
   - Complete field specifications for caching
   - Memory usage analysis
   - Usage examples
   - Loading strategies
   - **MUST READ before implementing models**

### Discord Bot Reference Locations

**Player Data Models**:
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/players.py:35-62` - PlayerPydantic
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/battingcards.py:22-33` - BattingCardModel
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/pitchingcards.py:22-33` - PitchingCardModel
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/cardpositions.py:22-39` - CardPositionModel
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/battingcardratings.py:29-60` - BattingCardRatingsModel
- `/mnt/NV2/Development/paper-dynasty/database/app/routers_v2/pitchingcardratings.py:28-61` - PitchingCardRatingsModel

### Existing Backend Structure

**Database Models** (Phase 1 Complete):
- `backend/app/models/db_models.py:34-70` - Game model
- `backend/app/models/db_models.py:72-205` - Play model (extensive stats)
- `backend/app/models/db_models.py:207-233` - Lineup model
- `backend/app/models/db_models.py:10-20` - GameCardsetLink
- `backend/app/models/db_models.py:22-31` - RosterLink
- `backend/app/models/db_models.py:235-246` - GameSession

**Backend Infrastructure** (Phase 1 Complete):
- `backend/app/main.py` - FastAPI app with Socket.io
- `backend/app/config.py` - Settings with Pydantic
- `backend/app/database/session.py` - Async database session
- `backend/app/websocket/connection_manager.py` - WebSocket lifecycle
- `backend/app/websocket/handlers.py` - Socket.io event handlers
- `backend/app/utils/logging.py` - Rotating logger setup

**Empty Directories Ready for Phase 2**:
- `backend/app/core/` - Game engine, state manager, play resolver
- `backend/app/config/` - League configs (currently just app config)
- `backend/app/data/` - API client

### Important Patterns from CLAUDE.md

**DateTime Handling** - ALWAYS use Pendulum:
```python
import pendulum
now = pendulum.now('UTC')  # ✅ Correct
formatted = now.format('YYYY-MM-DD HH:mm:ss')

# ❌ NEVER use:
from datetime import datetime
```

**Logging Pattern**:
```python
import logging
logger = logging.getLogger(f'{__name__}.ClassName')
logger.info(f"Message with context: {variable}")
```

**Error Handling** - "Raise or Return" pattern:
```python
# ✅ Raise exceptions for errors
def get_player(player_id: int) -> Player:
    player = find_player(player_id)
    if not player:
        raise ValueError(f"Player {player_id} not found")
    return player

# ❌ Don't return Optional unless specifically required
def get_player(player_id: int) -> Optional[Player]:  # Avoid this
```

**Git Commits** - Prefix with "CLAUDE: ":
```bash
git commit -m "CLAUDE: Implement Week 4 state manager"
```

### Common Operations

**Start Backend**:
```bash
cd /mnt/NV2/Development/strat-gameplay-webapp/backend
source venv/bin/activate
python -m app.main
# Available at http://localhost:8000
# API docs at http://localhost:8000/docs
```

**Run Tests**:
```bash
cd /mnt/NV2/Development/strat-gameplay-webapp/backend
source venv/bin/activate
pytest tests/ -v
pytest tests/unit/models/test_game_models.py -v  # Specific test
```

**Database Connection Test**:
```bash
psql postgresql://paperdynasty:PASSWORD@10.10.0.42:5432/paperdynasty_dev
```

---

## Performance Targets (Phase 2)

**Critical Metrics**:
- Action response: < 500ms (user action → state update)
- WebSocket delivery: < 200ms
- Database write: < 100ms (async, non-blocking)
- State recovery: < 2 seconds (rebuild from DB)
- Concurrent games: 10+ simultaneous active games
- Memory per game: ~10-15KB (20 cached players)

**How We'll Achieve Them**:
- ✅ In-memory state (no DB queries during plays)
- ✅ Async database writes (non-blocking)
- ✅ Lightweight Pydantic models (fast serialization)
- ✅ Efficient state recovery (single query with joins)

---

## Architecture Summary

### The "Two-Model" Pattern

**In-Memory (Pydantic)**:
```python
class GameState(BaseModel):
    # Fast, lightweight, optimized for game logic
    game_id: UUID
    inning: int
    outs: int
    home_score: int
    away_score: int
    home_lineup: Dict[int, CachedPlayer]  # Complete player data
    away_lineup: Dict[int, CachedPlayer]
    runners: List[RunnerState]
    current_batter_id: int
    # ...
```

**Database (SQLAlchemy)**:
```python
class Game(Base):
    # Persistent, complete, auditable
    id = Column(UUID, primary_key=True)
    league_id = Column(String)
    current_inning = Column(Integer)
    home_score = Column(Integer)
    # Relationships
    plays = relationship("Play", cascade="all, delete-orphan")
    lineups = relationship("Lineup", cascade="all, delete-orphan")
    # ...
```

**Why Both?**:
- Pydantic: Fast reads, easy WebSocket serialization, optimized structure
- SQLAlchemy: Persistence, relationships, audit trail, crash recovery

**Translation Layer**: StateManager handles conversion between models

---

## Questions for User (Awaiting Answers)

1. **Variant Cards**: I saw `variant: int = 0` in batting/pitching card models. Are variants different versions of the same player that could be in the same game simultaneously? Or are they alternate ratings for different seasons?

2. **Offensive Column**: What is `offense_col` (BattingCard, PitchingCard) used for? Is this for result chart lookups or something else?

3. **SBA Simplifications**: For SBA league, should we:
   - Option A: Still cache BattingCardRatings/PitchingCardRatings but use simplified result selection
   - Option B: Truly simplify and not cache detailed ratings at all

4. **Scouting Data**: The PRD mentions "scouting data" for PD. Is the detailed `BattingCardRatings` / `PitchingCardRatings` (with all the probability fields) THE scouting data, or is there additional scouting information beyond those rating tables?

5. **Missing Fields**: Are there any player attributes or ratings used in gameplay that aren't captured in the player-data-catalog.md document?

---

## Success Criteria - Phase 2 Complete

By end of Phase 2, we will have:

- [x] Comprehensive planning documents ✅ (THIS SESSION)
- [ ] In-memory game state management working
- [ ] Play resolution engine with dice rolls
- [ ] League configuration system (SBA and PD configs)
- [ ] Polymorphic player models (BasePlayer, SbaPlayer, PdPlayer)
- [ ] Database persistence layer with async operations
- [ ] State recovery mechanism from database
- [ ] Basic game flow (start → plays → end)
- [ ] Complete ONE at-bat for SBA league
- [ ] Complete ONE at-bat for PD league
- [ ] All unit tests passing (90%+ coverage)
- [ ] All integration tests passing
- [ ] Dice distribution verified as uniform
- [ ] Performance targets met (<500ms response)

---

## Final Status

**Planning Phase: COMPLETE** ✅

**Ready to Begin**: Week 4 - State Management & Persistence

**Blockers**: None - awaiting user review of player-data-catalog.md

**Confidence Level**: High - comprehensive planning with proven Discord bot reference

**Next Session**: Implement Pydantic game state models after user review

---

**Session saved**: 2025-10-22 01:13
**Document**: `.claude/status-2025-10-22-0113.md`