docs(02.1): create phase plan for additional sports stadiums

Phase 2.1: Additional Sports Stadiums - 3 plans created (MLS, WNBA, NWSL modules) - CBB deferred to future phase (350+ D1 teams) - 6 total tasks defined - Ready for execution Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-10 00:47:02 -06:00
parent 64137b57bf
commit 02d154cf46
4 changed files with 424 additions and 5 deletions
--- a/.planning/ROADMAP.md
+++ b/.planning/ROADMAP.md
@@ -46,13 +46,15 @@ Plans:
 - [x] 02-02: Regenerate canonical data and verify pipeline

 ### Phase 2.1: Additional Sports Stadiums (INSERTED)
-**Goal**: Add hardcoded stadium data for secondary sports: MLS, WNBA, NWSL, and CBB (College Basketball)
+**Goal**: Add hardcoded stadium data for secondary sports: MLS, WNBA, NWSL (CBB deferred - 350+ D1 teams requires separate scoped phase)
 **Depends on**: Phase 2
-**Research**: Unlikely (stadium data compilation)
-**Plans**: TBD
+**Research**: No (stadium data compilation following established patterns)
+**Plans**: 3 plans

 Plans:
- [ ] 02.1-01: TBD (run /gsd:plan-phase 2.1 to break down)
+- [ ] 02.1-01: Create MLS module with 30 hardcoded stadiums
+- [ ] 02.1-02: Create WNBA module with 13 hardcoded arenas
+- [ ] 02.1-03: Create NWSL module with 13+ hardcoded stadiums

 ### Phase 3: Alias Systems
 **Goal**: Implement alias systems for both stadiums and teams to handle name variations across data sources
@@ -100,7 +102,7 @@ Phases execute in numeric order: 1 → 2 → 2.1 → 3 → 4 → 5 → 6
 |-------|----------------|--------|-----------|
 | 1. Script Architecture | 3/3 | Complete | 2026-01-10 |
 | 2. Stadium Foundation | 2/2 | Complete | 2026-01-10 |
-| 2.1. Additional Sports Stadiums | 0/TBD | Not started | - |
+| 2.1. Additional Sports Stadiums | 0/3 | Not started | - |
 | 3. Alias Systems | 0/TBD | Not started | - |
 | 4. Canonical Linking | 0/TBD | Not started | - |
 | 5. CloudKit CRUD | 0/TBD | Not started | - |
--- a/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-PLAN.md
+++ b/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-PLAN.md
@@ -0,0 +1,149 @@
+---
+phase: 2.1-additional-sports-stadiums
+plan: 01
+type: execute
+---
+
+<objective>
+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.
+</objective>
+
+<execution_context>
+~/.claude/get-shit-done/workflows/execute-phase.md
+~/.claude/get-shit-done/templates/summary.md
+</execution_context>
+
+<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)
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Create mls.py module with complete stadium data</name>
+  <files>Scripts/mls.py</files>
+  <action>
+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).
+  </action>
+  <verify>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)"</verify>
+  <done>mls.py exists with 30 teams, 30 stadiums, all with non-zero capacity and year_opened values</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Integrate MLS module with scrape_schedules.py</name>
+  <files>Scripts/scrape_schedules.py</files>
+  <action>
+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.
+  </action>
+  <verify>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'"</verify>
+  <done>scrape_schedules.py imports MLS_TEAMS from mls.py, get_team_abbrev works for MLS, inline MLS_TEAMS removed</done>
+</task>
+
+</tasks>
+
+<verification>
+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
+</verification>
+
+<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>
+
+<output>
+After completion, create `.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-SUMMARY.md`
+</output>
--- a/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-02-PLAN.md
+++ b/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-02-PLAN.md
@@ -0,0 +1,128 @@
+---
+phase: 2.1-additional-sports-stadiums
+plan: 02
+type: execute
+---
+
+<objective>
+Create WNBA sport module with complete hardcoded stadium data.
+
+Purpose: Enable WNBA stadium data to flow through the canonicalization pipeline.
+Output: wnba.py module with 13 arenas including capacity, year_opened, and coordinates.
+</objective>
+
+<execution_context>
+~/.claude/get-shit-done/workflows/execute-phase.md
+~/.claude/get-shit-done/templates/summary.md
+</execution_context>
+
+<context>
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Prior plan in this phase:
+@.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-SUMMARY.md
+
+# Pattern reference:
+@Scripts/mlb.py
+@Scripts/mls.py (created in 02.1-01)
+
+# Current WNBA data:
+@Scripts/scrape_schedules.py (WNBA_TEAMS dict at line 77)
+
+# NBA arenas (many shared with WNBA):
+@Scripts/nba.py
+
+# Core module:
+@Scripts/core.py
+
+**Tech stack available:** Python 3, dataclasses, requests
+**Established patterns:** Sport module structure from mlb.py, mls.py
+**Key insight:** Many WNBA teams share arenas with NBA teams - can reference nba.py hardcoded data for coordinates/capacity
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Create wnba.py module with complete stadium data</name>
+  <files>Scripts/wnba.py</files>
+  <action>
+Create wnba.py following the established pattern:
+
+1. Module docstring and imports (try/except for core imports)
+2. __all__ exports list
+3. WNBA_TEAMS dict (copy from scrape_schedules.py, 13 teams)
+4. get_wnba_team_abbrev() function
+5. Hardcoded WNBA arenas dict with COMPLETE data:
+
+WNBA Teams and Arenas (2025 season - 13 teams):
+- ATL: Atlanta Dream → Gateway Center Arena (College Park, GA) - WNBA-specific, ~3,500 capacity, opened 2018
+- CHI: Chicago Sky → Wintrust Arena (Chicago, IL) - WNBA-specific, ~10,387 capacity, opened 2017
+- CON: Connecticut Sun → Mohegan Sun Arena (Uncasville, CT) - ~10,000 capacity, opened 2001
+- DAL: Dallas Wings → College Park Center (Arlington, TX) - ~7,000 capacity, opened 2012
+- GSV: Golden State Valkyries → Chase Center (San Francisco, CA) - shared with NBA Warriors, ~18,064, opened 2019
+- IND: Indiana Fever → Gainbridge Fieldhouse (Indianapolis, IN) - shared with NBA Pacers, ~17,923, opened 1999
+- LVA: Las Vegas Aces → Michelob Ultra Arena (Las Vegas, NV) - ~12,000 capacity, opened 2016
+- LA: Los Angeles Sparks → Crypto.com Arena (Los Angeles, CA) - shared with NBA Lakers/Clippers, ~19,079, opened 1999
+- MIN: Minnesota Lynx → Target Center (Minneapolis, MN) - shared with NBA Timberwolves, ~18,978, opened 1990
+- NY: New York Liberty → Barclays Center (Brooklyn, NY) - shared with NBA Nets, ~17,732, opened 2012
+- PHO: Phoenix Mercury → Footprint Center (Phoenix, AZ) - shared with NBA Suns, ~17,071, opened 1992
+- SEA: Seattle Storm → Climate Pledge Arena (Seattle, WA) - shared with NHL Kraken, ~17,100, opened 1962 (renovated 2021)
+- WAS: Washington Mystics → Entertainment & Sports Arena (Washington, DC) - WNBA-specific, ~4,200, opened 2018
+
+6. scrape_wnba_stadiums_hardcoded() function returning list[Stadium]
+7. scrape_wnba_stadiums() function with fallback sources
+8. WNBA_STADIUM_SOURCES configuration
+
+Note: Use WNBA-specific capacity where different from NBA configuration.
+Cross-reference nba.py for shared arena coordinates.
+  </action>
+  <verify>python3 -c "from Scripts.wnba import WNBA_TEAMS, scrape_wnba_stadiums_hardcoded; s = scrape_wnba_stadiums_hardcoded(); print(f'{len(s)} arenas'); assert len(s) == 13; assert all(st.capacity > 0 for st in s); assert all(st.year_opened for st in s)"</verify>
+  <done>wnba.py exists with 13 teams, 13 arenas, all with non-zero capacity and year_opened values</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Integrate WNBA module with scrape_schedules.py</name>
+  <files>Scripts/scrape_schedules.py</files>
+  <action>
+Update scrape_schedules.py to use the new wnba.py module:
+
+1. Add import at top (with try/except pattern):
+   - from wnba import WNBA_TEAMS, get_wnba_team_abbrev, scrape_wnba_stadiums, WNBA_STADIUM_SOURCES
+
+2. Remove inline WNBA_TEAMS dict (lines ~77-91) - now imported from wnba.py
+
+3. Update get_team_abbrev() function to use get_wnba_team_abbrev() for WNBA
+
+4. Update scrape_wnba_stadiums() stub function to use the new module's implementation
+
+5. Verify WNBA games scraping still works
+
+Do NOT remove the game scraping functions - those stay inline for now.
+  </action>
+  <verify>cd Scripts && python3 -c "from scrape_schedules import WNBA_TEAMS, get_team_abbrev; print(f'WNBA teams: {len(WNBA_TEAMS)}'); abbrev = get_team_abbrev('Las Vegas Aces', 'WNBA'); print(f'Aces abbrev: {abbrev}'); assert abbrev == 'LVA'"</verify>
+  <done>scrape_schedules.py imports WNBA_TEAMS from wnba.py, get_team_abbrev works for WNBA, inline WNBA_TEAMS removed</done>
+</task>
+
+</tasks>
+
+<verification>
+Before declaring plan complete:
+- [ ] wnba.py exists with complete module structure
+- [ ] All 13 WNBA arenas have capacity > 0 and year_opened values
+- [ ] scrape_schedules.py imports from wnba.py successfully
+- [ ] No import errors when running pipeline
+</verification>
+
+<success_criteria>
+
+- wnba.py module created following established pattern
+- 13 WNBA arenas with complete data (capacity, year_opened, coordinates)
+- scrape_schedules.py integration works
+- Shared NBA arenas have correct coordinates
+</success_criteria>
+
+<output>
+After completion, create `.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-02-SUMMARY.md`
+</output>
--- a/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-03-PLAN.md
+++ b/.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-03-PLAN.md
@@ -0,0 +1,140 @@
+---
+phase: 2.1-additional-sports-stadiums
+plan: 03
+type: execute
+---
+
+<objective>
+Create NWSL sport module with complete hardcoded stadium data.
+
+Purpose: Enable NWSL stadium data to flow through the canonicalization pipeline.
+Output: nwsl.py module with 13+ stadiums including capacity, year_opened, and coordinates.
+</objective>
+
+<execution_context>
+~/.claude/get-shit-done/workflows/execute-phase.md
+~/.claude/get-shit-done/templates/summary.md
+</execution_context>
+
+<context>
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Prior plans in this phase:
+@.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-01-SUMMARY.md
+@.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-02-SUMMARY.md
+
+# Pattern reference:
+@Scripts/mlb.py
+@Scripts/mls.py (created in 02.1-01)
+@Scripts/wnba.py (created in 02.1-02)
+
+# Current NWSL data:
+@Scripts/scrape_schedules.py (NWSL_TEAMS dict at line 126)
+
+# MLS stadiums (some shared with NWSL):
+@Scripts/mls.py
+
+# Core module:
+@Scripts/core.py
+
+**Tech stack available:** Python 3, dataclasses, requests
+**Established patterns:** Sport module structure from mlb.py, mls.py, wnba.py
+**Key insight:** Several NWSL teams share stadiums with MLS teams - can reference mls.py hardcoded data
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Create nwsl.py module with complete stadium data</name>
+  <files>Scripts/nwsl.py</files>
+  <action>
+Create nwsl.py following the established pattern:
+
+1. Module docstring and imports (try/except for core imports)
+2. __all__ exports list
+3. NWSL_TEAMS dict (copy from scrape_schedules.py, 13 teams - verify current roster)
+4. get_nwsl_team_abbrev() function
+5. Hardcoded NWSL stadiums dict with COMPLETE data:
+
+NWSL Teams and Stadiums (2025 season - 14 teams as of expansion):
+- LA: Angel City FC → BMO Stadium (Los Angeles, CA) - shared with LAFC, ~22,000, opened 2018
+- SJ: Bay FC → PayPal Park (San Jose, CA) - shared with SJ Earthquakes, ~18,000, opened 2015
+- CHI: Chicago Red Stars → SeatGeek Stadium (Bridgeview, IL) - ~20,000 capacity, opened 2006
+- HOU: Houston Dash → Shell Energy Stadium (Houston, TX) - shared with Houston Dynamo, ~22,039, opened 2012
+- KC: Kansas City Current → CPKC Stadium (Kansas City, MO) - NWSL-specific, ~11,500, opened 2024
+- NJ: NJ/NY Gotham FC → Red Bull Arena (Harrison, NJ) - shared with NY Red Bulls, ~25,000, opened 2010
+- NC: North Carolina Courage → WakeMed Soccer Park (Cary, NC) - ~10,000, opened 2002
+- ORL: Orlando Pride → Inter&Co Stadium (Orlando, FL) - shared with Orlando City SC, ~25,500, opened 2017
+- POR: Portland Thorns FC → Providence Park (Portland, OR) - shared with Portland Timbers, ~25,218, opened 1926 (renovated 2019)
+- SEA: Seattle Reign FC → Lumen Field (Seattle, WA) - shared with Sounders/Seahawks, ~69,000, opened 2002
+- SD: San Diego Wave FC → Snapdragon Stadium (San Diego, CA) - shared, ~35,000, opened 2022
+- UTA: Utah Royals FC → America First Field (Sandy, UT) - shared with Real Salt Lake, ~20,213, opened 2008
+- WAS: Washington Spirit → Audi Field (Washington, DC) - shared with DC United, ~20,000, opened 2018
+- BOS: Boston Breakers FC (if active - verify current NWSL roster)
+
+Cross-reference mls.py for shared stadium coordinates and verify current league membership.
+
+6. scrape_nwsl_stadiums_hardcoded() function returning list[Stadium]
+7. scrape_nwsl_stadiums() function with fallback sources
+8. NWSL_STADIUM_SOURCES configuration
+
+Note: NWSL has had expansion and contraction - verify current team roster matches actual 2025 season.
+  </action>
+  <verify>python3 -c "from Scripts.nwsl import NWSL_TEAMS, scrape_nwsl_stadiums_hardcoded; s = scrape_nwsl_stadiums_hardcoded(); print(f'{len(s)} stadiums'); print(f'{len(NWSL_TEAMS)} teams'); assert all(st.capacity > 0 for st in s); assert all(st.year_opened for st in s)"</verify>
+  <done>nwsl.py exists with current NWSL teams, all stadiums with non-zero capacity and year_opened values</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Integrate NWSL module and finalize phase</name>
+  <files>Scripts/scrape_schedules.py</files>
+  <action>
+Update scrape_schedules.py to use the new nwsl.py module:
+
+1. Add import at top (with try/except pattern):
+   - from nwsl import NWSL_TEAMS, get_nwsl_team_abbrev, scrape_nwsl_stadiums, NWSL_STADIUM_SOURCES
+
+2. Remove inline NWSL_TEAMS dict (lines ~126-140) - now imported from nwsl.py
+
+3. Update get_team_abbrev() function to use get_nwsl_team_abbrev() for NWSL
+
+4. Update scrape_nwsl_stadiums() stub function to use the new module's implementation
+
+5. Verify NWSL games scraping still works
+
+6. Run full stadium update to verify all 3 new sports integrate:
+   python3 scrape_schedules.py --stadiums-update
+
+Do NOT remove the game scraping functions - those stay inline for now.
+  </action>
+  <verify>cd Scripts && python3 -c "from scrape_schedules import NWSL_TEAMS, get_team_abbrev; print(f'NWSL teams: {len(NWSL_TEAMS)}'); abbrev = get_team_abbrev('Portland Thorns FC', 'NWSL'); print(f'Thorns abbrev: {abbrev}'); assert abbrev == 'POR'"</verify>
+  <done>scrape_schedules.py imports NWSL_TEAMS from nwsl.py, get_team_abbrev works for NWSL, all 3 secondary sport modules integrated</done>
+</task>
+
+</tasks>
+
+<verification>
+Before declaring plan complete:
+- [ ] nwsl.py exists with complete module structure
+- [ ] All NWSL stadiums have capacity > 0 and year_opened values
+- [ ] scrape_schedules.py imports from nwsl.py successfully
+- [ ] `python3 Scripts/scrape_schedules.py --stadiums-update` includes MLS, WNBA, and NWSL stadiums
+- [ ] No import errors when running pipeline
+</verification>
+
+<success_criteria>
+
+- nwsl.py module created following established pattern
+- All NWSL stadiums with complete data (capacity, year_opened, coordinates)
+- scrape_schedules.py integration works for all 3 new sports
+- Phase 2.1 complete (MLS, WNBA, NWSL modules created)
+- CBB deferred to future phase (documented in summary)
+</success_criteria>
+
+<output>
+After completion, create `.planning/phases/2.1-add-stadium-data-mls-wnba-nwsl-cbb/02.1-03-SUMMARY.md` with:
+- Summary of all 3 plans (MLS, WNBA, NWSL modules)
+- Note that CBB was deferred (350+ D1 teams requires separate scoped phase)
+- Phase 2.1 complete status
+</output>