95861cae40
Phase 2: Stadium Foundation - 2 plans created - 5 total tasks defined - Ready for execution Plan 02-01: Audit & complete hardcoded stadium data Plan 02-02: Regenerate canonical data and verify pipeline Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
154 lines
4.9 KiB
Markdown
154 lines
4.9 KiB
Markdown
---
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phase: 02-stadium-foundation
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plan: 01
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type: execute
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---
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<objective>
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Audit and complete hardcoded stadium data across all 4 sport modules.
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Purpose: Ensure all sport modules have complete, accurate stadium data that will flow through the canonicalization pipeline.
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Output: All 4 sport modules with complete stadium data (city, state, lat/lng, capacity, year_opened, teams).
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</objective>
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<execution_context>
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~/.claude/get-shit-done/workflows/execute-phase.md
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~/.claude/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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@.planning/phases/01-script-architecture/01-03-SUMMARY.md
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**Key files:**
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@Scripts/mlb.py
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@Scripts/nba.py
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@Scripts/nhl.py
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@Scripts/nfl.py
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**Current state:**
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- MLB, NBA, NHL, NFL modules have hardcoded stadium data with city, state, lat/lng, capacity, teams
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- Missing field: year_opened (null in all canonical data)
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- NFL module created in Phase 1 Plan 03 with 30 hardcoded stadiums
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- Bundled stadiums_canonical.json has incomplete data (state="", capacity=0, missing NFL)
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**Expected stadium counts:**
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- MLB: 30 stadiums (30 teams)
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- NBA: 30 stadiums (30 teams)
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- NHL: 32 stadiums (32 teams)
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- NFL: 30 stadiums (32 teams, 2 shared: SoFi Stadium, MetLife Stadium)
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**Stadium data structure:**
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Each module has `scrape_{sport}_stadiums_hardcoded()` returning Stadium objects with:
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- name, city, state, lat/lng, capacity, teams
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- Missing: year_opened for filtering historical/renamed venues
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</context>
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<tasks>
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<task type="auto">
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<name>Task 1: Audit stadium data completeness across all 4 sport modules</name>
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<files>Scripts/mlb.py, Scripts/nba.py, Scripts/nhl.py, Scripts/nfl.py</files>
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<action>
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1. Read each sport module's hardcoded stadium function
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2. Create audit report listing for each sport:
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- Stadium count (should match expected)
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- Fields present/missing
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- Any stadiums with missing lat/lng (should be 0)
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- Any stadiums with missing capacity (should be 0)
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3. Identify gaps: stadiums missing from lists, incorrect coordinates, missing teams
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Do NOT modify any files in this task - audit only. The goal is to understand current state before making changes.
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</action>
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<verify>Print audit summary showing stadium counts per sport and any data quality issues found</verify>
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<done>Audit report shows MLB:30, NBA:30, NHL:32, NFL:30 stadiums with all required fields documented</done>
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</task>
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<task type="auto">
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<name>Task 2: Add year_opened to all hardcoded stadiums</name>
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<files>Scripts/mlb.py, Scripts/nba.py, Scripts/nhl.py, Scripts/nfl.py</files>
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<action>
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Add year_opened to each stadium's hardcoded data. Use the actual opening year for each venue:
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**MLB stadiums (sample):**
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- Fenway Park: 1912
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- Wrigley Field: 1914
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- Dodger Stadium: 1962
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- Globe Life Field: 2020
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**NBA arenas (sample):**
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- TD Garden: 1995
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- Madison Square Garden: 1968
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- Chase Center: 2019
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- Intuit Dome: 2024
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**NHL arenas:** Many share with NBA - verify and match
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**NFL stadiums (sample):**
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- Lambeau Field: 1957
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- SoFi Stadium: 2020
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- Allegiant Stadium: 2020
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For each module:
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1. Update the hardcoded dict to include 'year_opened' key
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2. Update Stadium object creation to include year_opened parameter
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3. Ensure Stadium dataclass in core.py has year_opened field (verify first)
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Research actual opening years from Wikipedia if unsure. Use the original opening year, not renovation years.
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</action>
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<verify>Run `python -c "from mlb import scrape_mlb_stadiums; s=scrape_mlb_stadiums(); print(f'MLB: {len(s)} stadiums, year_opened example: {s[0].year_opened if hasattr(s[0], \"year_opened\") else \"MISSING\"}')"` for each sport</verify>
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<done>All 4 sport modules have year_opened in hardcoded data, Stadium objects include year_opened field</done>
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</task>
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</tasks>
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<verification>
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Before declaring plan complete:
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- [ ] Audit confirms expected stadium counts: MLB:30, NBA:30, NHL:32, NFL:30
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- [ ] All 4 modules have year_opened in hardcoded stadium data
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- [ ] No Python syntax errors in any module
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- [ ] Stadium dataclass supports year_opened field
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</verification>
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<success_criteria>
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- Task 1: Audit complete with documented counts and any gaps identified
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- Task 2: year_opened added to all hardcoded stadiums in all 4 modules
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- No import errors when loading modules
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- Ready for Plan 02 (pipeline regeneration)
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</success_criteria>
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<output>
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After completion, create `.planning/phases/02-stadium-foundation/02-01-SUMMARY.md`:
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# Phase 2 Plan 01: Stadium Data Audit & Completion Summary
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**[Substantive one-liner]**
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## Accomplishments
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- [Stadium counts verified]
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- [year_opened added to all modules]
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## Files Created/Modified
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- `Scripts/mlb.py` - Added year_opened
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- `Scripts/nba.py` - Added year_opened
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- `Scripts/nhl.py` - Added year_opened
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- `Scripts/nfl.py` - Added year_opened
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## Decisions Made
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[Any gaps found and how resolved]
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## Issues Encountered
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[Any data issues discovered]
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## Next Step
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Ready for 02-02-PLAN.md (pipeline regeneration)
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</output>
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