x-apisports-key: ${API_SPORTS_KEY}

curl -s "https://v2.nba.api-sports.io/games?date=2025-01-15" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v2.nba.api-sports.io/players?search=maxey" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v2.nba.api-sports.io/players/statistics?id=265&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v2.nba.api-sports.io/players/statistics?id=265&game=12345" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v2.nba.api-sports.io/teams/statistics?id=20&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v2.nba.api-sports.io/standings?league=standard&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/games?date=2025-01-12" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/games?league=1&season=2024&week=15" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/players?search=mahomes" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/players/statistics?id=1234&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/players/statistics?id=1234&game=5678" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/teams/statistics?id=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.american-football.api-sports.io/standings?league=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/games?date=2025-07-15" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/games?league=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/players?search=ohtani" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/players/statistics?id=1234&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/teams/statistics?id=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.baseball.api-sports.io/standings?league=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/games?date=2025-01-15" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/games?league=57&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/players?search=mcdavid" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/players/statistics?id=1234&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/teams/statistics?id=1&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

curl -s "https://v1.hockey.api-sports.io/standings?league=57&season=2024" \
  -H "x-apisports-key: ${API_SPORTS_KEY}"

import json
import os
import subprocess

def fetch_player_stats(player_id: int, season: int) -> list:
    """Fetch player's game-by-game stats for a season."""
    result = subprocess.run([
        "curl", "-s",
        f"https://v2.nba.api-sports.io/players/statistics?id={player_id}&season={season}",
        "-H", f"x-apisports-key: {os.environ['API_SPORTS_KEY']}"
    ], capture_output=True, text=True)
    data = json.loads(result.stdout)
    return data.get("response", [])

def calculate_hit_rate(games: list, stat: str, line: float) -> dict:
    """Calculate how often a player exceeds a line."""
    valid_games = [g for g in games if g.get(stat) is not None]
    hits = sum(1 for g in valid_games if g[stat] > line)

    return {
        "games_analyzed": len(valid_games),
        "hits": hits,
        "hit_rate": hits / len(valid_games) if valid_games else 0,
        "average": sum(g[stat] for g in valid_games) / len(valid_games) if valid_games else 0,
        "median": sorted([g[stat] for g in valid_games])[len(valid_games)//2] if valid_games else 0,
        "max": max(g[stat] for g in valid_games) if valid_games else 0,
        "min": min(g[stat] for g in valid_games) if valid_games else 0,
    }

def analyze_trend(games: list, stat: str, recent_n: int = 5) -> dict:
    """Compare recent performance to season average."""
    if len(games) < recent_n:
        return {"error": "Not enough games"}

    # Sort by date (most recent first)
    sorted_games = sorted(games, key=lambda g: g.get("game", {}).get("date", ""), reverse=True)

    recent = sorted_games[:recent_n]
    season = sorted_games

    recent_avg = sum(g[stat] for g in recent) / len(recent)
    season_avg = sum(g[stat] for g in season) / len(season)

    pct_change = ((recent_avg - season_avg) / season_avg * 100) if season_avg else 0

    return {
        "recent_avg": round(recent_avg, 1),
        "season_avg": round(season_avg, 1),
        "trend": "hot" if pct_change > 10 else "cold" if pct_change < -10 else "neutral",
        "pct_change": round(pct_change, 1),
    }

def fetch_game_metadata(game_ids: list, sport: str = "nba") -> dict:
    """
    Fetch game metadata to determine home/away teams.
    Returns: {game_id: {"home_team_id": int, "away_team_id": int}}
    """
    base_urls = {
        "nba": "https://v2.nba.api-sports.io",
        "nfl": "https://v1.american-football.api-sports.io",
        "mlb": "https://v1.baseball.api-sports.io",
        "nhl": "https://v1.hockey.api-sports.io",
    }
    base = base_urls.get(sport, base_urls["nba"])
    metadata = {}

    for game_id in game_ids:
        result = subprocess.run([
            "curl", "-s", f"{base}/games?id={game_id}",
            "-H", f"x-apisports-key: {os.environ['API_SPORTS_KEY']}"
        ], capture_output=True, text=True)
        data = json.loads(result.stdout)
        if data.get("response"):
            game = data["response"][0]
            metadata[game_id] = {
                "home_team_id": game.get("teams", {}).get("home", {}).get("id"),
                "away_team_id": game.get("teams", {}).get("away", {}).get("id"),
            }
    return metadata


def analyze_vs_team(games: list, game_metadata: dict, opponent_team_id: int, stat: str) -> dict:
    """
    Analyze player performance against a specific team.

    Args:
        games: Player's game-by-game stats (from /players/statistics)
        game_metadata: Dict mapping game_id -> {home_team_id, away_team_id}
        opponent_team_id: Team ID to filter against
        stat: Stat field to analyze (e.g., 'points', 'yards')
    """
    def get_opponent_id(game):
        game_id = game.get("game", {}).get("id")
        meta = game_metadata.get(game_id, {})
        player_team_id = game.get("team", {}).get("id")
        # If player's team is home, opponent is away (and vice versa)
        if meta.get("home_team_id") == player_team_id:
            return meta.get("away_team_id")
        return meta.get("home_team_id")

    vs_team = [g for g in games if get_opponent_id(g) == opponent_team_id]
    vs_others = [g for g in games if get_opponent_id(g) != opponent_team_id]

    if not vs_team:
        return {"error": "No games against this opponent"}

    vs_team_avg = sum(g[stat] for g in vs_team) / len(vs_team)
    vs_others_avg = sum(g[stat] for g in vs_others) / len(vs_others) if vs_others else 0

    return {
        "vs_team_games": len(vs_team),
        "vs_team_avg": round(vs_team_avg, 1),
        "vs_others_avg": round(vs_others_avg, 1),
        "advantage": "favorable" if vs_team_avg > vs_others_avg else "unfavorable",
    }

# Step 1: Fetch player stats
games = fetch_player_stats(player_id=265, season=2024)

# Step 2: Extract game IDs and fetch metadata
game_ids = [g.get("game", {}).get("id") for g in games if g.get("game", {}).get("id")]
game_metadata = fetch_game_metadata(game_ids, sport="nba")

# Step 3: Analyze vs specific opponent
result = analyze_vs_team(games, game_metadata, opponent_team_id=20, stat="points")

def analyze_home_away(games: list, stat: str) -> dict:
    """Compare home vs away performance."""
    home_games = [g for g in games if g.get("game", {}).get("home", False)]
    away_games = [g for g in games if not g.get("game", {}).get("home", False)]

    home_avg = sum(g[stat] for g in home_games) / len(home_games) if home_games else 0
    away_avg = sum(g[stat] for g in away_games) / len(away_games) if away_games else 0

    return {
        "home_games": len(home_games),
        "home_avg": round(home_avg, 1),
        "away_games": len(away_games),
        "away_avg": round(away_avg, 1),
        "home_boost": round(home_avg - away_avg, 1),
    }

**Tyrese Maxey Over 26.5 Points** → LEAN OVER (68% confidence)

**Hit Rate:** 14/20 games (70%) this season
**Recent Trend:** Averaging 29.3 pts last 5 games (↑12% from 26.1 season avg)
**Tonight's Matchup:** vs MIA - historically scores 28.5 PPG against Heat (3 games)

**Supporting Factors:**
- Joel Embiid OUT → Maxey usage rate increases ~8%
- Heat allow 4th-most points to opposing PGs
- B2B but well-rested (2 days off prior)

*Want detailed game-by-game logs? Just ask.*