---
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*