docs(02.1-01): complete MLS sport module plan

Tasks completed: 2/2
- Create MLS sport module with 30 hardcoded stadiums
- Integrate MLS module with scrape_schedules.py

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

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

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+---
+phase: 2.1-additional-sports-stadiums
+plan: 01
+subsystem: data
+tags: [mls, soccer, stadiums, python, scraping]
+
+# Dependency graph
+requires:
+  - phase: 02-stadium-foundation
+    provides: sport module pattern (mlb.py, nba.py) and canonicalization pipeline
+provides:
+  - MLS sport module with 30 hardcoded stadiums
+  - Complete MLS stadium data (capacity, year_opened, coordinates)
+  - Integration with scrape_schedules.py pipeline
+affects: [02.1-02-wnba, 02.1-03-nwsl, future stadium phases]
+
+# Tech tracking
+tech-stack:
+  added: []
+  patterns: [sport module pattern from core sports applied to MLS]
+
+key-files:
+  created: [Scripts/mls.py]
+  modified: [Scripts/scrape_schedules.py]
+
+key-decisions:
+  - "Used soccer configuration capacities for shared NFL stadiums"
+  - "Prioritized hardcoded source over gavinr GeoJSON for complete data"
+
+patterns-established:
+  - "Sport module structure: MLS_TEAMS dict, get_mls_team_abbrev(), scrape_mls_stadiums_hardcoded(), scrape_mls_stadiums(), MLS_STADIUM_SOURCES"
+
+issues-created: []
+
+# Metrics
+duration: 6min
+completed: 2026-01-10
+---
+
+# Phase 2.1-01: MLS Sport Module Summary
+
+**Complete MLS stadium data module with 30 stadiums including capacity (soccer config), year_opened, and coordinates for canonicalization pipeline**
+
+## Performance
+
+- **Duration:** 6 min
+- **Started:** 2026-01-10T06:48:48Z
+- **Completed:** 2026-01-10T06:54:27Z
+- **Tasks:** 2
+- **Files modified:** 2
+
+## Accomplishments
+- Created MLS sport module following established pattern from MLB/NBA/NHL/NFL
+- All 30 MLS stadiums with complete data (capacity, year_opened, coordinates)
+- Integrated with scrape_schedules.py pipeline for stadium updates
+- Hardcoded source prioritized over external GeoJSON for data completeness
+
+## Task Commits
+
+Each task was committed atomically:
+
+1. **Task 1: Create mls.py module with complete stadium data** - `addc9b3` (feat)
+2. **Task 2: Integrate MLS module with scrape_schedules.py** - `8f1803b` (feat)
+
+## Files Created/Modified
+- `Scripts/mls.py` - New MLS sport module with 30 teams, 30 stadiums, complete data
+- `Scripts/scrape_schedules.py` - Import MLS module, remove inline MLS_TEAMS dict and stadium scrapers
+
+## Decisions Made
+- Used soccer configuration capacities for shared stadiums (e.g., Mercedes-Benz Stadium 42,500 for soccer vs 71,000 for NFL)
+- Prioritized hardcoded source (priority=1) over gavinr GeoJSON (priority=2) since hardcoded has complete capacity and year_opened data
+- Kept game scrapers inline in scrape_schedules.py (only extracted stadium scrapers for this plan)
+
+## Deviations from Plan
+
+None - plan executed exactly as written
+
+## Issues Encountered
+
+None
+
+## Next Phase Readiness
+- MLS stadium data now complete and flowing through canonicalization pipeline
+- Pattern established for remaining sport modules (WNBA, NWSL, CBB)
+- Ready for 02.1-02-wnba plan
+
+---
+*Phase: 2.1-additional-sports-stadiums*
+*Plan: 01*
+*Completed: 2026-01-10*