70acfb7bc6
NBA team mappings, Basketball-Reference/ESPN/CBS scrapers, stadium data with coordinates. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
412 lines
17 KiB
Python
412 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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NBA schedule and stadium scrapers for SportsTime.
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This module provides:
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- NBA game scrapers (Basketball-Reference, ESPN, CBS Sports)
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- NBA stadium scrapers (hardcoded with coordinates)
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- Multi-source fallback configurations
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"""
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from datetime import datetime, timedelta
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from typing import Optional
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import requests
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# Support both direct execution and import from parent directory
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try:
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from core import (
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Game,
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Stadium,
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ScraperSource,
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StadiumScraperSource,
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fetch_page,
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scrape_with_fallback,
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scrape_stadiums_with_fallback,
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)
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except ImportError:
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from Scripts.core import (
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Game,
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Stadium,
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ScraperSource,
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StadiumScraperSource,
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fetch_page,
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scrape_with_fallback,
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scrape_stadiums_with_fallback,
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)
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__all__ = [
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# Team data
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'NBA_TEAMS',
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# Game scrapers
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'scrape_nba_basketball_reference',
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'scrape_nba_espn',
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'scrape_nba_cbssports',
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# Stadium scrapers
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'scrape_nba_stadiums',
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# Source configurations
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'NBA_GAME_SOURCES',
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'NBA_STADIUM_SOURCES',
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# Convenience functions
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'scrape_nba_games',
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'get_nba_season_string',
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]
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# =============================================================================
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# TEAM MAPPINGS
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# =============================================================================
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NBA_TEAMS = {
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'ATL': {'name': 'Atlanta Hawks', 'city': 'Atlanta', 'arena': 'State Farm Arena'},
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'BOS': {'name': 'Boston Celtics', 'city': 'Boston', 'arena': 'TD Garden'},
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'BRK': {'name': 'Brooklyn Nets', 'city': 'Brooklyn', 'arena': 'Barclays Center'},
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'CHO': {'name': 'Charlotte Hornets', 'city': 'Charlotte', 'arena': 'Spectrum Center'},
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'CHI': {'name': 'Chicago Bulls', 'city': 'Chicago', 'arena': 'United Center'},
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'CLE': {'name': 'Cleveland Cavaliers', 'city': 'Cleveland', 'arena': 'Rocket Mortgage FieldHouse'},
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'DAL': {'name': 'Dallas Mavericks', 'city': 'Dallas', 'arena': 'American Airlines Center'},
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'DEN': {'name': 'Denver Nuggets', 'city': 'Denver', 'arena': 'Ball Arena'},
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'DET': {'name': 'Detroit Pistons', 'city': 'Detroit', 'arena': 'Little Caesars Arena'},
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'GSW': {'name': 'Golden State Warriors', 'city': 'San Francisco', 'arena': 'Chase Center'},
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'HOU': {'name': 'Houston Rockets', 'city': 'Houston', 'arena': 'Toyota Center'},
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'IND': {'name': 'Indiana Pacers', 'city': 'Indianapolis', 'arena': 'Gainbridge Fieldhouse'},
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'LAC': {'name': 'Los Angeles Clippers', 'city': 'Inglewood', 'arena': 'Intuit Dome'},
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'LAL': {'name': 'Los Angeles Lakers', 'city': 'Los Angeles', 'arena': 'Crypto.com Arena'},
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'MEM': {'name': 'Memphis Grizzlies', 'city': 'Memphis', 'arena': 'FedExForum'},
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'MIA': {'name': 'Miami Heat', 'city': 'Miami', 'arena': 'Kaseya Center'},
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'MIL': {'name': 'Milwaukee Bucks', 'city': 'Milwaukee', 'arena': 'Fiserv Forum'},
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'MIN': {'name': 'Minnesota Timberwolves', 'city': 'Minneapolis', 'arena': 'Target Center'},
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'NOP': {'name': 'New Orleans Pelicans', 'city': 'New Orleans', 'arena': 'Smoothie King Center'},
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'NYK': {'name': 'New York Knicks', 'city': 'New York', 'arena': 'Madison Square Garden'},
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'OKC': {'name': 'Oklahoma City Thunder', 'city': 'Oklahoma City', 'arena': 'Paycom Center'},
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'ORL': {'name': 'Orlando Magic', 'city': 'Orlando', 'arena': 'Kia Center'},
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'PHI': {'name': 'Philadelphia 76ers', 'city': 'Philadelphia', 'arena': 'Wells Fargo Center'},
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'PHO': {'name': 'Phoenix Suns', 'city': 'Phoenix', 'arena': 'Footprint Center'},
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'POR': {'name': 'Portland Trail Blazers', 'city': 'Portland', 'arena': 'Moda Center'},
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'SAC': {'name': 'Sacramento Kings', 'city': 'Sacramento', 'arena': 'Golden 1 Center'},
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'SAS': {'name': 'San Antonio Spurs', 'city': 'San Antonio', 'arena': 'Frost Bank Center'},
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'TOR': {'name': 'Toronto Raptors', 'city': 'Toronto', 'arena': 'Scotiabank Arena'},
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'UTA': {'name': 'Utah Jazz', 'city': 'Salt Lake City', 'arena': 'Delta Center'},
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'WAS': {'name': 'Washington Wizards', 'city': 'Washington', 'arena': 'Capital One Arena'},
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}
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def get_nba_team_abbrev(team_name: str) -> str:
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"""Get NBA team abbreviation from full name."""
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for abbrev, info in NBA_TEAMS.items():
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if info['name'].lower() == team_name.lower():
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return abbrev
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if team_name.lower() in info['name'].lower():
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return abbrev
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# Return first 3 letters as fallback
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return team_name[:3].upper()
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def get_nba_season_string(season: int) -> str:
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"""
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Get NBA season string in "2024-25" format.
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Args:
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season: The ending year of the season (e.g., 2025 for 2024-25 season)
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Returns:
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Season string like "2024-25"
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"""
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return f"{season-1}-{str(season)[2:]}"
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# =============================================================================
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# GAME SCRAPERS
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# =============================================================================
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def scrape_nba_basketball_reference(season: int) -> list[Game]:
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"""
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Scrape NBA schedule from Basketball-Reference.
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URL: https://www.basketball-reference.com/leagues/NBA_{YEAR}_games-{month}.html
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Season year is the ending year (e.g., 2025 for 2024-25 season)
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"""
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games = []
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months = ['october', 'november', 'december', 'january', 'february', 'march', 'april', 'may', 'june']
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print(f"Scraping NBA {season} from Basketball-Reference...")
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for month in months:
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url = f"https://www.basketball-reference.com/leagues/NBA_{season}_games-{month}.html"
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soup = fetch_page(url, 'basketball-reference.com')
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if not soup:
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continue
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table = soup.find('table', {'id': 'schedule'})
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if not table:
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continue
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tbody = table.find('tbody')
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if not tbody:
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continue
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for row in tbody.find_all('tr'):
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if row.get('class') and 'thead' in row.get('class'):
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continue
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cells = row.find_all(['td', 'th'])
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if len(cells) < 6:
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continue
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try:
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# Parse date
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date_cell = row.find('th', {'data-stat': 'date_game'})
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if not date_cell:
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continue
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date_link = date_cell.find('a')
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date_str = date_link.text if date_link else date_cell.text
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# Parse time
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time_cell = row.find('td', {'data-stat': 'game_start_time'})
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time_str = time_cell.text.strip() if time_cell else None
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# Parse teams
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visitor_cell = row.find('td', {'data-stat': 'visitor_team_name'})
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home_cell = row.find('td', {'data-stat': 'home_team_name'})
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if not visitor_cell or not home_cell:
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continue
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visitor_link = visitor_cell.find('a')
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home_link = home_cell.find('a')
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away_team = visitor_link.text if visitor_link else visitor_cell.text
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home_team = home_link.text if home_link else home_cell.text
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# Parse arena
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arena_cell = row.find('td', {'data-stat': 'arena_name'})
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arena = arena_cell.text.strip() if arena_cell else ''
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# Convert date
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try:
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parsed_date = datetime.strptime(date_str.strip(), '%a, %b %d, %Y')
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date_formatted = parsed_date.strftime('%Y-%m-%d')
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except:
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continue
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# Generate game ID
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away_abbrev = get_nba_team_abbrev(away_team)
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home_abbrev = get_nba_team_abbrev(home_team)
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game_id = f"nba_{date_formatted}_{away_abbrev}_{home_abbrev}".lower().replace(' ', '')
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game = Game(
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id=game_id,
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sport='NBA',
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season=get_nba_season_string(season),
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date=date_formatted,
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time=time_str,
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home_team=home_team,
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away_team=away_team,
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home_team_abbrev=home_abbrev,
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away_team_abbrev=away_abbrev,
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venue=arena,
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source='basketball-reference.com'
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)
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games.append(game)
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except Exception as e:
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print(f" Error parsing row: {e}")
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continue
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print(f" Found {len(games)} games from Basketball-Reference")
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return games
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def scrape_nba_espn(season: int) -> list[Game]:
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"""
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Scrape NBA schedule from ESPN.
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URL: https://www.espn.com/nba/schedule/_/date/{YYYYMMDD}
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"""
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games = []
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print(f"Scraping NBA {season} from ESPN...")
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# Determine date range for season
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start_date = datetime(season - 1, 10, 1) # October of previous year
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end_date = datetime(season, 6, 30) # June of season year
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current_date = start_date
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while current_date <= end_date:
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date_str = current_date.strftime('%Y%m%d')
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url = f"https://www.espn.com/nba/schedule/_/date/{date_str}"
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soup = fetch_page(url, 'espn.com')
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if soup:
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# ESPN uses JavaScript rendering, so we need to parse what's available
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# This is a simplified version - full implementation would need Selenium
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pass
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current_date += timedelta(days=7) # Sample weekly to respect rate limits
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print(f" Found {len(games)} games from ESPN")
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return games
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def scrape_nba_cbssports(season: int) -> list[Game]:
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"""
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Fetch NBA schedule from CBS Sports.
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CBS Sports provides a JSON API for schedule data.
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"""
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games = []
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print(f"Fetching NBA {season} from CBS Sports...")
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# CBS Sports has a schedule endpoint
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url = "https://www.cbssports.com/nba/schedule/"
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soup = fetch_page(url, 'cbssports.com')
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if not soup:
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return games
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# Find all game rows
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tables = soup.find_all('table', class_='TableBase-table')
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for table in tables:
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rows = table.find_all('tr')
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for row in rows:
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try:
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cells = row.find_all('td')
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if len(cells) < 2:
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continue
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# Parse teams from row
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team_cells = row.find_all('a', class_='TeamName')
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if len(team_cells) < 2:
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continue
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away_team = team_cells[0].get_text(strip=True)
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home_team = team_cells[1].get_text(strip=True)
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# Get date from table section
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date_formatted = datetime.now().strftime('%Y-%m-%d') # Placeholder
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away_abbrev = get_nba_team_abbrev(away_team)
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home_abbrev = get_nba_team_abbrev(home_team)
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game_id = f"nba_{date_formatted}_{away_abbrev}_{home_abbrev}".lower().replace(' ', '')
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game = Game(
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id=game_id,
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sport='NBA',
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season=get_nba_season_string(season),
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date=date_formatted,
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time=None,
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home_team=home_team,
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away_team=away_team,
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home_team_abbrev=home_abbrev,
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away_team_abbrev=away_abbrev,
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venue='',
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source='cbssports.com'
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)
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games.append(game)
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except Exception:
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continue
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print(f" Found {len(games)} games from CBS Sports")
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return games
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# =============================================================================
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# STADIUM SCRAPERS
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# =============================================================================
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def scrape_nba_stadiums() -> list[Stadium]:
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"""
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Fetch NBA arena data (hardcoded with accurate coordinates).
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"""
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print("\nNBA STADIUMS")
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print("-" * 40)
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print(" Loading NBA arenas...")
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nba_arenas = {
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'State Farm Arena': {'city': 'Atlanta', 'state': 'GA', 'lat': 33.7573, 'lng': -84.3963, 'capacity': 18118, 'teams': ['ATL']},
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'TD Garden': {'city': 'Boston', 'state': 'MA', 'lat': 42.3662, 'lng': -71.0621, 'capacity': 19156, 'teams': ['BOS']},
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'Barclays Center': {'city': 'Brooklyn', 'state': 'NY', 'lat': 40.6826, 'lng': -73.9754, 'capacity': 17732, 'teams': ['BRK']},
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'Spectrum Center': {'city': 'Charlotte', 'state': 'NC', 'lat': 35.2251, 'lng': -80.8392, 'capacity': 19077, 'teams': ['CHO']},
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'United Center': {'city': 'Chicago', 'state': 'IL', 'lat': 41.8807, 'lng': -87.6742, 'capacity': 20917, 'teams': ['CHI']},
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'Rocket Mortgage FieldHouse': {'city': 'Cleveland', 'state': 'OH', 'lat': 41.4965, 'lng': -81.6882, 'capacity': 19432, 'teams': ['CLE']},
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'American Airlines Center': {'city': 'Dallas', 'state': 'TX', 'lat': 32.7905, 'lng': -96.8103, 'capacity': 19200, 'teams': ['DAL']},
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'Ball Arena': {'city': 'Denver', 'state': 'CO', 'lat': 39.7487, 'lng': -105.0077, 'capacity': 19520, 'teams': ['DEN']},
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'Little Caesars Arena': {'city': 'Detroit', 'state': 'MI', 'lat': 42.3411, 'lng': -83.0553, 'capacity': 20332, 'teams': ['DET']},
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'Chase Center': {'city': 'San Francisco', 'state': 'CA', 'lat': 37.7680, 'lng': -122.3879, 'capacity': 18064, 'teams': ['GSW']},
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'Toyota Center': {'city': 'Houston', 'state': 'TX', 'lat': 29.7508, 'lng': -95.3621, 'capacity': 18055, 'teams': ['HOU']},
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'Gainbridge Fieldhouse': {'city': 'Indianapolis', 'state': 'IN', 'lat': 39.7640, 'lng': -86.1555, 'capacity': 17923, 'teams': ['IND']},
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'Intuit Dome': {'city': 'Inglewood', 'state': 'CA', 'lat': 33.9425, 'lng': -118.3419, 'capacity': 18000, 'teams': ['LAC']},
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'Crypto.com Arena': {'city': 'Los Angeles', 'state': 'CA', 'lat': 34.0430, 'lng': -118.2673, 'capacity': 18997, 'teams': ['LAL']},
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'FedExForum': {'city': 'Memphis', 'state': 'TN', 'lat': 35.1382, 'lng': -90.0506, 'capacity': 17794, 'teams': ['MEM']},
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'Kaseya Center': {'city': 'Miami', 'state': 'FL', 'lat': 25.7814, 'lng': -80.1870, 'capacity': 19600, 'teams': ['MIA']},
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'Fiserv Forum': {'city': 'Milwaukee', 'state': 'WI', 'lat': 43.0451, 'lng': -87.9174, 'capacity': 17341, 'teams': ['MIL']},
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'Target Center': {'city': 'Minneapolis', 'state': 'MN', 'lat': 44.9795, 'lng': -93.2761, 'capacity': 18978, 'teams': ['MIN']},
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'Smoothie King Center': {'city': 'New Orleans', 'state': 'LA', 'lat': 29.9490, 'lng': -90.0821, 'capacity': 16867, 'teams': ['NOP']},
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'Madison Square Garden': {'city': 'New York', 'state': 'NY', 'lat': 40.7505, 'lng': -73.9934, 'capacity': 19812, 'teams': ['NYK']},
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'Paycom Center': {'city': 'Oklahoma City', 'state': 'OK', 'lat': 35.4634, 'lng': -97.5151, 'capacity': 18203, 'teams': ['OKC']},
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'Kia Center': {'city': 'Orlando', 'state': 'FL', 'lat': 28.5392, 'lng': -81.3839, 'capacity': 18846, 'teams': ['ORL']},
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'Wells Fargo Center': {'city': 'Philadelphia', 'state': 'PA', 'lat': 39.9012, 'lng': -75.1720, 'capacity': 20478, 'teams': ['PHI']},
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'Footprint Center': {'city': 'Phoenix', 'state': 'AZ', 'lat': 33.4457, 'lng': -112.0712, 'capacity': 17071, 'teams': ['PHO']},
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'Moda Center': {'city': 'Portland', 'state': 'OR', 'lat': 45.5316, 'lng': -122.6668, 'capacity': 19393, 'teams': ['POR']},
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'Golden 1 Center': {'city': 'Sacramento', 'state': 'CA', 'lat': 38.5802, 'lng': -121.4997, 'capacity': 17608, 'teams': ['SAC']},
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'Frost Bank Center': {'city': 'San Antonio', 'state': 'TX', 'lat': 29.4270, 'lng': -98.4375, 'capacity': 18418, 'teams': ['SAS']},
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'Scotiabank Arena': {'city': 'Toronto', 'state': 'ON', 'lat': 43.6435, 'lng': -79.3791, 'capacity': 19800, 'teams': ['TOR']},
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'Delta Center': {'city': 'Salt Lake City', 'state': 'UT', 'lat': 40.7683, 'lng': -111.9011, 'capacity': 18306, 'teams': ['UTA']},
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'Capital One Arena': {'city': 'Washington', 'state': 'DC', 'lat': 38.8982, 'lng': -77.0209, 'capacity': 20356, 'teams': ['WAS']},
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}
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stadiums = []
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for name, info in nba_arenas.items():
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stadium = Stadium(
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id=f"nba_{name.lower().replace(' ', '_')[:30]}",
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name=name,
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city=info['city'],
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state=info['state'],
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latitude=info['lat'],
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longitude=info['lng'],
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capacity=info['capacity'],
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sport='NBA',
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team_abbrevs=info['teams'],
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source='nba_hardcoded'
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)
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stadiums.append(stadium)
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print(f" ✓ Found {len(stadiums)} NBA arenas")
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return stadiums
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# =============================================================================
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# SOURCE CONFIGURATIONS
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# =============================================================================
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NBA_GAME_SOURCES = [
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ScraperSource('Basketball-Reference', scrape_nba_basketball_reference, priority=1, min_games=100),
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ScraperSource('CBS Sports', scrape_nba_cbssports, priority=2, min_games=50),
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ScraperSource('ESPN', scrape_nba_espn, priority=3, min_games=50),
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]
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NBA_STADIUM_SOURCES = [
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StadiumScraperSource('Hardcoded', scrape_nba_stadiums, priority=1, min_venues=25),
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]
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# =============================================================================
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# CONVENIENCE FUNCTIONS
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# =============================================================================
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def scrape_nba_games(season: int) -> list[Game]:
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"""
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Scrape NBA games for a season using multi-source fallback.
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Args:
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season: Season ending year (e.g., 2025 for 2024-25 season)
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Returns:
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List of Game objects from the first successful source
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"""
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print(f"\nNBA {get_nba_season_string(season)} SCHEDULE")
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print("-" * 40)
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return scrape_with_fallback('NBA', season, NBA_GAME_SOURCES)
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