### Betting Strategy: International Soccer Away Underdog Value Bets\012\012**Strategy Overview** (tailored for intermediate bettors, medium risk tolerance):  \012Target away teams (visitor) in major international soccer leagues (EPL, La Liga, Serie A, Bundesliga, Ligue 1) during 2024 calendar year data. Criteria:  \012- Proxy for <30% pre-match win probability: Away team's overall season win rate so far < 30% (using running total wins/games; a reasonable proxy for underdog status since BallDontLie lacks historical odds).  \012- Strong recent away form: >40% win rate (i.e., ≥40% wins) in their most recent 10 (or fewer if not enough history) away games.  \012\012**Bet Details**:  \012- Bet type: Moneyline on **away** team win.  \012- Assumed decimal odds: 4.0 (implied ~25% probability, typical for such underdogs; adjust based on real odds).  \012- Stake: Flat 1 unit per bet.  \012- Expected payout: +3 units profit on win, -1 unit on loss.  \012- **Risk Warning**: Underdog moneyline bets have high variance. Even with form filters, expect long losing streaks. Use <5% bankroll per bet in live play. Only for backtesting here—no real money advice.\012\012**Data Source**: BallDontLie API (supports EPL, La Liga, etc.). Fetches 2024 games chronologically. Limitation: No native odds/win probs, so proxy used. Filters to completed games in 2024. Leagues: EPL, La Liga, Serie A, Bundesliga, Ligue 1 (adjustable).\012\012**Backtest Implementation**: Full Python code below simulates chronologically:  \012- Fetches & combines games across leagues for 2024.  \012- Processes game-by-game, checks criteria pre-match.  \012- Simulates outcomes, tracks P/L.  \012- Outputs performance metrics, list of cumulative P/L points (for graphing), and auto-generates profit/loss curve plot.  \012\012Run the code locally (requires `requests`, `pandas`, `matplotlib`, `collections`—install via `pip install requests pandas matplotlib`). It handles API pagination. Results depend on API data availability.\012\012```python\012import requests\012from collections import defaultdict, deque\012import pandas as pd\012import matplotlib.pyplot as plt\012from datetime import datetime\012import json\012\012# Leagues supported (adjust slugs based on BallDontLie league identifiers; e.g., 'premier-league', 'la-liga')\012LEAGUES = ['premier-league', 'la-liga', 'serie-a', 'bundesliga', 'ligue-1']\012YEAR = 2024\012ODDS = 4.0  # Assumed decimal odds for qualifying underdogs\012STAKE = 1.0\012BASE_URL = 'https://www.balldontlie.io/api/v1/games'\012\012def fetch_games_for_league(league, year):\012    """Fetch paginated games for a league/season from BallDontLie."""\012    all_games = []\012    page = 1\012    per_page = 100\012    while True:\012        params = {\012            'league': league,\012            f'seasons[]': year,\012            'per_page': per_page,\012            'page': page,\012            'start_date': f'{year}-01-01',\012            'end_date': f'{year}-12-31'\012        }\012        response = requests.get(BASE_URL, params=params)\012        if response.status_code != 200:\012            print(f"Error fetching {league}: {response.status_code}")\012            break\012        data = response.json()\012        games = data['data']\012        if not games:\012            break\012        all_games.extend(games)\012        page += 1\012    return all_games\012\012def parse_date(date_str):\012    """Parse ISO date to datetime."""\012    return datetime.fromisoformat(date_str.replace('Z', '+00:00'))\012\012# Fetch all 2024 games across leagues\012all_games = []\012for league in LEAGUES:\012    print(f"Fetching {league} 2024 games...")\012    league_games = fetch_games_for_league(league, YEAR)\012    all_games.extend(league_games)\012\012# Filter to 2024 calendar year and completed games (with scores)\012games_2024 = [\012    g for g in all_games\012    if g.get('status') in ['Final', 'Final Overtime'] and  # Completed\012    parse_date(g['date']).year == YEAR and\012    g.get('home_team_score') is not None and g.get('visitor_team_score') is not None\012]\012\012# Sort chronologically\012games_2024.sort(key=lambda g: parse_date(g['date']))\012\012print(f"Loaded {len(games_2024)} games from 2024.")\012\012# Backtest simulation\012team_away_recent = defaultdict(lambda: deque(maxlen=10))  # Last 10 away results (1=win, 0=loss)\012team_total_wins = defaultdict(int)\012team_total_games = defaultdict(int)\012\012bets = []\012cumulative_pnl = 0.0\012pnl_points = []  # List of (date, cumulative_pnl) for graph\012wins = 0\012total_bets = 0\012\012for game in games_2024:\012    home_id = game['home_team']['id']\012    away_id = game['visitor_team']['id']\012    home_score = game['home_team_score']\012    visitor_score = game['visitor_team_score']\012    game_date = parse_date(game['date'])\012\012    # Pre-match check for away team underdog + form\012    recent_away = team_away_recent[away_id]\012    num_recent = len(recent_away)\012    if num_recent == 0:\012        # Update post-game stats\012        pass\012    else:\012        recent_win_pct = sum(recent_away) / num_recent\012        overall_winrate = team_total_wins[away_id] / team_total_games[away_id] if team_total_games[away_id] > 0 else 0.0\012\012        if recent_win_pct > 0.4 and overall_winrate < 0.3:\012            # Qualifying bet: away moneyline\012            total_bets += 1\012            bet = {\012                'game_id': game['id'],\012                'league': game.get('league', 'unknown'),\012                'date': game['date'],\012                'home': game['home_team']['name'],\012                'away': game['visitor_team']['name'],\012                'bet_type': 'moneyline',\012                'prediction': 'away',\012                'confidence': 0.65,  # Medium confidence based on criteria\012                'stake': STAKE,\012                'assumed_odds': ODDS\012            }\012            bets.append(bet)\012\012            # Simulate outcome\012            if visitor_score > home_score:\012                profit = STAKE * (ODDS - 1)\012                wins += 1\012            else:\012                profit = -STAKE\012            cumulative_pnl += profit\012\012    # Post-game: Update stats (overall for both, recent away for away team)\012    team_total_games[home_id] += 1\012    team_total_games[away_id] += 1\012    if home_score > visitor_score:\012        team_total_wins[home_id] += 1\012    elif visitor_score > home_score:\012        team_total_wins[away_id] += 1\012\012    # Append away result to recent (for future checks)\012    away_result = 1 if visitor_score > home_score else 0\012    team_away_recent[away_id].append(away_result)\012\012    pnl_points.append((game_date, cumulative_pnl))\012\012# Results\012win_rate = wins / total_bets if total_bets > 0 else 0\012roi = (cumulative_pnl / total_bets) * 100 if total_bets > 0 else 0  # % ROI per bet\012\012print("\n=== BACKTEST RESULTS ===")\012print(f"Total bets: {total_bets}")\012print(f"Wins: {wins} ({win_rate:.1%} win rate)")\012print(f"Final P/L: {cumulative_pnl:.2f} units")\012print(f"ROI: {roi:.1f}% per bet")\012print(f"Bets placed: {len(bets)}")\012\012print("\n=== CUMULATIVE P/L GRAPH POINTS ===")\012for date, pnl in pnl_points[-20:]:  # Last 20 for brevity; full list available in pnl_points\012    print(f"{date.strftime('%Y-%m-%d')}: {pnl:.2f}")\012\012print("\nFull pnl_points list length:", len(pnl_points))\012\012# Plot cumulative P/L curve\012df = pd.DataFrame(pnl_points, columns=['date', 'pnl'])\012df['date_num'] = pd.to_datetime(df['date']).astype(int) / 10**9  # Unix timestamp for x-axis\012plt.figure(figsize=(12, 6))\012plt.plot(df['date_num'], df['pnl'], linewidth=2, label='Cumulative P/L')\012plt.axhline(y=0, color='r', linestyle='--', label='Break-even')\012plt.xlabel('Game Date (2024)')\012plt.ylabel('Cumulative Profit/Loss (units)')\012plt.title('2024 Soccer Underdog Backtest: Cumulative P/L')\012plt.legend()\012plt.grid(True, alpha=0.3)\012plt.xticks(rotation=45)\012plt.tight_layout()\012plt.show()\012\012# Optional: Save bets to JSON\012# with open('soccer_underdog_bets_2024.json\012\012⚠️ **Note:** Live backtesting encountered an issue. Please try again or use the Strategy Playground for detailed analysis.