Sportstime/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-PLAN.md

phase, plan, type

phase	plan	type
2.1-additional-sports-stadiums	01	execute

Create MLS sport module with complete hardcoded stadium data.

Purpose: Enable MLS stadium data to flow through the canonicalization pipeline like the core 4 sports. Output: mls.py module with 30 stadiums including capacity, year_opened, and coordinates.

<execution_context> ~/.claude/get-shit-done/workflows/execute-phase.md ~/.claude/get-shit-done/templates/summary.md </execution_context>

@.planning/PROJECT.md @.planning/ROADMAP.md @.planning/STATE.md

Prior phase context:

@.planning/phases/02-stadium-foundation/02-02-SUMMARY.md

Pattern reference (follow this module structure):

@Scripts/mlb.py @Scripts/nba.py

Current MLS data location:

@Scripts/scrape_schedules.py (MLS_TEAMS dict at line 93) @Scripts/data/stadiums.json (MLS entries have lat/lng but missing capacity/year_opened)

Core module for imports:

@Scripts/core.py

Tech stack available: Python 3, dataclasses, requests Established patterns: Sport module structure (team dict, get_abbrev function, hardcoded stadiums, scraper sources) Constraining decisions:

• Phase 02-02: MLS excluded from bundled JSON due to incomplete data (zero capacity, null year_opened)

Task 1: Create mls.py module with complete stadium data Scripts/mls.py Create mls.py following the mlb.py/nba.py pattern:

1. Module docstring and imports (try/except for core imports)
2. all exports list
3. MLS_TEAMS dict (copy from scrape_schedules.py, 30 teams)
4. get_mls_team_abbrev() function
5. Hardcoded MLS stadiums dict with COMPLETE data:
   • All 30 MLS stadiums
   • Each entry needs: city, state, lat, lng, capacity, teams (list of abbrevs), year_opened
   • Use existing lat/lng from Scripts/data/stadiums.json where available
   • Research capacity and year_opened for each stadium

Key stadiums to research (capacity/year_opened):

• Mercedes-Benz Stadium (ATL) - shared with NFL
• Q2 Stadium (Austin) - MLS-specific, opened 2021
• Bank of America Stadium (CLT) - shared with NFL
• Soldier Field (CHI) - shared with NFL
• TQL Stadium (CIN) - MLS-specific, opened 2021
• Dick's Sporting Goods Park (COL)
• Lower.com Field (CLB) - opened 2021
• Toyota Stadium (DAL)
• Audi Field (DC) - MLS-specific, opened 2018
• Shell Energy Stadium (HOU) - MLS-specific
• Dignity Health Sports Park (LAG)
• BMO Stadium (LAFC) - opened 2018
• Chase Stadium (MIA) - MLS-specific
• Allianz Field (MIN) - opened 2019
• Stade Saputo (MTL)
• Geodis Park (NSH) - opened 2022
• Gillette Stadium (NE) - shared with NFL
• Yankee Stadium (NYCFC) - shared with MLB
• Red Bull Arena (NYRB)
• Inter&Co Stadium (ORL)
• Subaru Park (PHI)
• Providence Park (POR)
• America First Field (RSL)
• PayPal Park (SJ)
• Lumen Field (SEA) - shared with NFL
• Children's Mercy Park (SKC)
• CityPark (STL) - opened 2023
• BMO Field (TOR)
• BC Place (VAN) - shared stadium
• Snapdragon Stadium (SD) - shared, opened 2022

6. scrape_mls_stadiums_hardcoded() function returning list[Stadium]
7. scrape_mls_stadiums() function with fallback sources
8. MLS_STADIUM_SOURCES configuration

Note: Some stadiums are shared with NFL/MLB - use correct MLS-specific capacity where different (soccer configuration). python3 -c "from Scripts.mls import MLS_TEAMS, scrape_mls_stadiums_hardcoded; s = scrape_mls_stadiums_hardcoded(); print(f'{len(s)} stadiums'); assert len(s) == 30; assert all(st.capacity > 0 for st in s); assert all(st.year_opened for st in s)" mls.py exists with 30 teams, 30 stadiums, all with non-zero capacity and year_opened values

Task 2: Integrate MLS module with scrape_schedules.py Scripts/scrape_schedules.py Update scrape_schedules.py to use the new mls.py module:

1. Add import at top (with try/except pattern):

   • from mls import MLS_TEAMS, get_mls_team_abbrev, scrape_mls_stadiums, MLS_STADIUM_SOURCES

2. Remove inline MLS_TEAMS dict (lines ~93-124) - now imported from mls.py

3. Update get_team_abbrev() function to use get_mls_team_abbrev() for MLS

4. Update scrape_mls_stadiums_gavinr() to be a secondary source (keep it, but mls.py hardcoded is primary)

5. Update the stadium scraping section to use scrape_mls_stadiums() from mls.py

6. Verify MLS games scraping still works (uses MLS_TEAMS for abbreviation lookup)

Do NOT remove the game scraping functions (scrape_mls_fbref, etc.) - those stay inline for now. cd Scripts && python3 -c "from scrape_schedules import MLS_TEAMS, get_team_abbrev; print(f'MLS teams: {len(MLS_TEAMS)}'); abbrev = get_team_abbrev('Atlanta United FC', 'MLS'); print(f'ATL United abbrev: {abbrev}'); assert abbrev == 'ATL'" scrape_schedules.py imports MLS_TEAMS from mls.py, get_team_abbrev works for MLS, inline MLS_TEAMS removed

Before declaring plan complete: - [ ] mls.py exists with complete module structure - [ ] All 30 MLS stadiums have capacity > 0 and year_opened values - [ ] scrape_schedules.py imports from mls.py successfully - [ ] `python3 Scripts/scrape_schedules.py --stadiums-update` includes MLS stadiums with complete data

<success_criteria>

• mls.py module created following established pattern
• 30 MLS stadiums with complete data (capacity, year_opened, coordinates)
• scrape_schedules.py integration works
• No import errors when running pipeline </success_criteria>

After completion, create `.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-SUMMARY.md`