Files

Trey t e2d629b76f 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>

2026-01-10 00:55:01 -06:00

2.9 KiB

Raw Blame History

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, issues-created, duration, completed

phase

plan

subsystem

tags

requires

provides

affects

tech-stack

key-files

key-decisions

patterns-established

issues-created

duration

completed

2.1-additional-sports-stadiums

data

mls

soccer

stadiums

python

scraping

phase	provides
02-stadium-foundation	sport module pattern (mlb.py, nba.py) and canonicalization pipeline

MLS sport module with 30 hardcoded stadiums

Complete MLS stadium data (capacity, year_opened, coordinates)

Integration with scrape_schedules.py pipeline

02.1-02-wnba

02.1-03-nwsl

future stadium phases

added

patterns

sport module pattern from core sports applied to MLS

created

modified

Scripts/mls.py

Scripts/scrape_schedules.py

Used soccer configuration capacities for shared NFL stadiums

Prioritized hardcoded source over gavinr GeoJSON for complete data

Sport module structure: MLS_TEAMS dict, get_mls_team_abbrev(), scrape_mls_stadiums_hardcoded(), scrape_mls_stadiums(), MLS_STADIUM_SOURCES

6min

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:

Task 1: Create mls.py module with complete stadium data - addc9b3 (feat)
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

2.9 KiB Raw Blame History