feat(02.1-01): integrate MLS module with scrape_schedules.py
- Import MLS_TEAMS, get_mls_team_abbrev, scrape_mls_stadiums from mls.py - Remove inline MLS_TEAMS dict (now imported from module) - Remove inline MLS stadium scraper functions (now in mls.py) - Update TODO comments to reflect MLS extraction complete Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Trey t
@@ -67,11 +67,18 @@ from nfl import (
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get_nfl_season_string,
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NFL_TEAMS,
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)
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from mls import (
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MLS_TEAMS,
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get_mls_team_abbrev,
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scrape_mls_stadiums,
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MLS_STADIUM_SOURCES,
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)
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# =============================================================================
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# NON-CORE SPORT TEAM MAPPINGS
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# TODO: Extract to separate modules (wnba.py, mls.py, nwsl.py, cbb.py)
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# TODO: Extract to separate modules (wnba.py, nwsl.py, cbb.py)
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# NOTE: MLS_TEAMS is now imported from mls.py
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# =============================================================================
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WNBA_TEAMS = {
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@@ -90,39 +97,6 @@ WNBA_TEAMS = {
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'WAS': {'name': 'Washington Mystics', 'city': 'Washington', 'arena': 'Entertainment & Sports Arena'},
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}
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MLS_TEAMS = {
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'ATL': {'name': 'Atlanta United FC', 'city': 'Atlanta', 'stadium': 'Mercedes-Benz Stadium'},
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'AUS': {'name': 'Austin FC', 'city': 'Austin', 'stadium': 'Q2 Stadium'},
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'CLT': {'name': 'Charlotte FC', 'city': 'Charlotte', 'stadium': 'Bank of America Stadium'},
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'CHI': {'name': 'Chicago Fire FC', 'city': 'Chicago', 'stadium': 'Soldier Field'},
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'CIN': {'name': 'FC Cincinnati', 'city': 'Cincinnati', 'stadium': 'TQL Stadium'},
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'COL': {'name': 'Colorado Rapids', 'city': 'Commerce City', 'stadium': "Dick's Sporting Goods Park"},
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'CLB': {'name': 'Columbus Crew', 'city': 'Columbus', 'stadium': 'Lower.com Field'},
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'DAL': {'name': 'FC Dallas', 'city': 'Frisco', 'stadium': 'Toyota Stadium'},
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'DC': {'name': 'D.C. United', 'city': 'Washington', 'stadium': 'Audi Field'},
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'HOU': {'name': 'Houston Dynamo FC', 'city': 'Houston', 'stadium': 'Shell Energy Stadium'},
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'LAG': {'name': 'LA Galaxy', 'city': 'Carson', 'stadium': 'Dignity Health Sports Park'},
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'LAFC': {'name': 'Los Angeles FC', 'city': 'Los Angeles', 'stadium': 'BMO Stadium'},
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'MIA': {'name': 'Inter Miami CF', 'city': 'Fort Lauderdale', 'stadium': 'Chase Stadium'},
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'MIN': {'name': 'Minnesota United FC', 'city': 'Saint Paul', 'stadium': 'Allianz Field'},
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'MTL': {'name': 'CF Montreal', 'city': 'Montreal', 'stadium': 'Stade Saputo'},
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'NSH': {'name': 'Nashville SC', 'city': 'Nashville', 'stadium': 'Geodis Park'},
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'NE': {'name': 'New England Revolution', 'city': 'Foxborough', 'stadium': 'Gillette Stadium'},
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'NYCFC': {'name': 'New York City FC', 'city': 'New York', 'stadium': 'Yankee Stadium'},
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'NYRB': {'name': 'New York Red Bulls', 'city': 'Harrison', 'stadium': 'Red Bull Arena'},
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'ORL': {'name': 'Orlando City SC', 'city': 'Orlando', 'stadium': 'Inter&Co Stadium'},
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'PHI': {'name': 'Philadelphia Union', 'city': 'Chester', 'stadium': 'Subaru Park'},
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'POR': {'name': 'Portland Timbers', 'city': 'Portland', 'stadium': 'Providence Park'},
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'RSL': {'name': 'Real Salt Lake', 'city': 'Sandy', 'stadium': 'America First Field'},
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'SJ': {'name': 'San Jose Earthquakes', 'city': 'San Jose', 'stadium': 'PayPal Park'},
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'SEA': {'name': 'Seattle Sounders FC', 'city': 'Seattle', 'stadium': 'Lumen Field'},
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'SKC': {'name': 'Sporting Kansas City', 'city': 'Kansas City', 'stadium': "Children's Mercy Park"},
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'STL': {'name': 'St. Louis City SC', 'city': 'St. Louis', 'stadium': 'CityPark'},
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'TOR': {'name': 'Toronto FC', 'city': 'Toronto', 'stadium': 'BMO Field'},
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'VAN': {'name': 'Vancouver Whitecaps FC', 'city': 'Vancouver', 'stadium': 'BC Place'},
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'SD': {'name': 'San Diego FC', 'city': 'San Diego', 'stadium': 'Snapdragon Stadium'},
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}
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NWSL_TEAMS = {
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'LA': {'name': 'Angel City FC', 'city': 'Los Angeles', 'stadium': 'BMO Stadium'},
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'SJ': {'name': 'Bay FC', 'city': 'San Jose', 'stadium': 'PayPal Park'},
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@@ -408,87 +382,10 @@ def scrape_cbb_cbssports(season: int) -> list[Game]:
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# =============================================================================
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# NON-CORE STADIUM SCRAPERS
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# TODO: Extract to separate modules
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# TODO: Extract to separate modules (wnba.py, nwsl.py, cbb.py)
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# NOTE: scrape_mls_stadiums() is now imported from mls.py
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# =============================================================================
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def scrape_mls_stadiums_geojson() -> list[Stadium]:
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"""Source 1: gavinr/usa-soccer GeoJSON."""
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stadiums = []
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url = "https://raw.githubusercontent.com/gavinr/usa-soccer/master/mls.geojson"
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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data = response.json()
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for feature in data.get('features', []):
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props = feature.get('properties', {})
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coords = feature.get('geometry', {}).get('coordinates', [0, 0])
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stadium = Stadium(
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id=f"mls_{props.get('stadium', '').lower().replace(' ', '_')[:30]}",
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name=props.get('stadium', ''),
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city=props.get('city', ''),
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state=props.get('state', ''),
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latitude=coords[1] if len(coords) > 1 else 0,
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longitude=coords[0] if len(coords) > 0 else 0,
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capacity=props.get('capacity', 0),
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sport='MLS',
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team_abbrevs=[props.get('team', '')],
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source='github.com/gavinr'
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)
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stadiums.append(stadium)
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return stadiums
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def scrape_mls_stadiums_csv() -> list[Stadium]:
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"""Source 2: gavinr/usa-soccer CSV."""
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stadiums = []
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url = "https://raw.githubusercontent.com/gavinr/usa-soccer/master/mls.csv"
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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reader = csv.DictReader(StringIO(response.text))
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for row in reader:
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stadium = Stadium(
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id=f"mls_{row.get('stadium', '').lower().replace(' ', '_')[:30]}",
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name=row.get('stadium', ''),
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city=row.get('city', ''),
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state=row.get('state', ''),
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latitude=float(row.get('lat', 0) or 0),
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longitude=float(row.get('lng', 0) or 0),
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capacity=int(row.get('capacity', 0) or 0),
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sport='MLS',
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team_abbrevs=[row.get('team', '')],
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source='github.com/gavinr/csv'
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)
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stadiums.append(stadium)
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return stadiums
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def scrape_mls_stadiums_hardcoded() -> list[Stadium]:
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"""Source 3: Hardcoded MLS stadiums (fallback)."""
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# Placeholder - would include full stadium list
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return []
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def scrape_mls_stadiums() -> list[Stadium]:
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"""Fetch MLS stadium data with multi-source fallback."""
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print("\nMLS STADIUMS")
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print("-" * 40)
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sources = [
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StadiumScraperSource('gavinr GeoJSON', scrape_mls_stadiums_geojson, priority=1, min_venues=20),
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StadiumScraperSource('gavinr CSV', scrape_mls_stadiums_csv, priority=2, min_venues=20),
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StadiumScraperSource('Hardcoded', scrape_mls_stadiums_hardcoded, priority=3, min_venues=20),
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]
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return scrape_stadiums_with_fallback('MLS', sources)
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def scrape_wnba_stadiums() -> list[Stadium]:
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"""Fetch WNBA arena data (hardcoded)."""
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print("\nWNBA STADIUMS")
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