```python\012def mean(values):\012    return sum(values) / len(values) if values else 0.0\012\012def generate_bets(games: list, players: list, teams: list, params: dict) -> list:\012    """\012    Backtests a Connor McDavid performance-based betting strategy for Edmonton Oilers.\012    - Analyzes McDavid's 2024-25 game logs: PPG, +/-, shots.\012    - Computes correlations with Oilers wins.\012    - Backtests predictive strategy: Bet Oilers moneyline if McDavid's rolling PPG (from prior games) > threshold.\012    - Assumes -110 odds for moneyline (standard NHL juice).\012    - Medium risk: fixed stake.\012    - Returns list of simulated bets for backtest games.\012    - Prints breakdown, correlations, and backtest metrics (win rate, ROI).\012    \012    Data assumptions:\012    - games: [{'id': str, 'date': str (sortable YYYY-MM-DD), 'season': str,\012               'home_team_id': int, 'away_team_id': int, 'home_score': int, 'away_score': int}]\012    - players: [{'game_id': str, 'player_id': int, 'name': str, 'team_id': int,\012                 'points': float, 'plus_minus': float, 'shots': float}]  # shot_attempts as 'shots'\012    - teams: [{'id': int, 'name': str}]\012    - params: {'season': '2024-25', 'ppg_threshold': 1.0, 'initial_bankroll': 1000, 'stake_pct': 0.05}\012    """\012    season = params.get('season', '2024-25')\012    ppg_threshold = params.get('ppg_threshold', 1.0)\012    initial_bankroll = params.get('initial_bankroll', 1000)\012    stake_pct = params.get('stake_pct', 0.05)  # Medium risk: 5% of bankroll per bet\012    \012    # Find Oilers and McDavid IDs\012    oilers_id = next((t['id'] for t in teams if 'Edmonton' in t.get('name', '').upper() and 'Oilers' in t.get('name', '').upper()), None)\012    if not oilers_id:\012        return []  # No Oilers data\012    mcdavid_id = next((p['player_id'] for p in players if 'McDavid' in p.get('name', '').upper() and 'Connor' in p.get('name', '').upper()), None)\012    if not mcdavid_id:\012        return []  # No McDavid data\012    \012    # Filter 2024-25 Oilers games and McDavid stats\012    oilers_games = [g for g in games if g.get('season') == season and\012                    (g.get('home_team_id') == oilers_id or g.get('away_team_id') == oilers_id)]\012    if not oilers_games:\012        print("No Oilers games found for the specified season.")\012        return []\012    \012    game_ids = {g['id']: g for g in oilers_games}\012    mcdavid_stats = {p['game_id']: p for p in players if p['player_id'] == mcdavid_id and p['game_id'] in game_ids}\012    \012    # Ensure matching games (McDavid played)\012    oilers_games = [g for g in oilers_games if g['id'] in mcdavid_stats]\012    total_games = len(oilers_games)\012    \012    if total_games == 0:\012        print("No matching McDavid game logs found.")\012        return []\012    \012    # Compute season averages for breakdown\012    points_list = [mcdavid_stats[g['id']]['points'] for g in oilers_games]\012    pm_list = [mcdavid_stats[g['id']]['plus_minus'] for g in oilers_games]\012    shots_list = [mcdavid_stats[g['id']]['shots'] for g in oilers_games]\012    \012    avg_ppg = mean(points_list)\012    avg_pm = mean(pm_list)\012    avg_shots = mean(shots_list)\012    \012    print(f"Connor McDavid {season} Breakdown (Oilers games: {total_games}):")\012    print(f"  Points per Game (PPG): {avg_ppg:.2f}")\012    print(f"  Plus/Minus per Game: {avg_pm:.2f}")\012    print(f"  Shots per Game: {avg_shots:.2f}")\012    \012    # Hindsight correlations with Oilers wins\012    overall_wins = 0\012    high_ppg_wins = high_ppg_games = 0\012    high_pm_wins = high_pm_games = 0\012    high_shots_wins = high_shots_games = 0\012    \012    for g in oilers_games:\012        stat = mcdavid_stats[g['id']]\012        is_oilers_win = (\012            (g['home_team_id'] == oilers_id and g['home_score'] > g['away_score']) or\012            (g['away_team_id'] == oilers_id and g['away_score'] > g['home_score'])\012        )\012        if is_oilers_win:\012            overall_wins += 1\012        \012        if stat['points'] > avg_ppg:\012            high_ppg_games += 1\012            if is_oilers_win: high_ppg_wins += 1\012        if stat['plus_minus'] > 0:\012            high_pm_games += 1\012            if is_oilers_win: high_pm_wins += 1\012        if stat['shots'] > avg_shots:\012            high_shots_games += 1\012            if is_oilers_win: high_shots_wins += 1\012    \012    print(f"\nCorrelations with Oilers Wins:")\012    print(f"  Overall Win Rate: {overall_wins / total_games * 100:.1f}%")\012    if high_ppg_games: print(f"  When PPG > {avg_ppg:.1f}: {high_ppg_wins / high_ppg_games * 100:.1f}% ({high_ppg_wins}/{high_ppg_games})")\012    if high_pm_games: print(f"  When +/- > 0: {high_pm_wins / high_pm_games * 100:.1f}% ({high_pm_wins}/{high_pm_games})")\012    if high_shots_games: print(f"  When Shots > {avg_shots:.1f}: {high_shots_wins / high_shots_games * 100:.1f}% ({high_shots_wins}/{high_shots_games})")\012    \012    # Sort games chronologically for backtest\012    oilers_games.sort(key=lambda g: g['date'])\012    \012    # Predictive backtest: Bet if rolling PPG > threshold\012    bankroll = initial_bankroll\012    bets = []\012    past_points = []\012    total_bets_placed = 0\012    bet_wins = 0\012    \012    print(f"\nBacktest Strategy: Bet Oilers ML if prior PPG > {ppg_threshold} (threshold={ppg_threshold}).")\012    print("Assumes -110 odds (win pays ~90.9% profit on stake). Medium risk stake.")\012    \012    for game in oilers_games:\012        # Rolling prior PPG\012        prior_ppg = mean(past_points)\012        \012        # Bet condition\012        if prior_ppg > ppg_threshold:\012            stake = max(10, bankroll * stake_pct)  # Min $10\012            prediction = 'home' if game['home_team_id'] == oilers_id else 'away'\012            confidence = min(0.95, 0.5 + (prior_ppg - ppg_threshold) * 0.2)  # 0.5-0.95\012            \012            bet = {\012                'game_id': game['id'],\012                'bet_type': 'moneyline',\012                'prediction': prediction,\012                'confidence': confidence,\012                'stake': stake\012            }\012            bets.append(bet)\012            total_bets_placed += 1\012            \012            # Outcome simulation\012            is_win = (\012                (game['home_team_id'] == oilers_id and game['home_score'] > game['away_score']) or\012                (game['away_team_id'] == oilers_id and game['away_score'] > game['home_score'])\012            )\012            if is_win:\012                profit = stake * (10 / 11)  # -110 payout\012                bankroll += profit\012                bet_wins += 1\012                print(f"Game {game['id'][:8]}... WIN: Prior PPG {prior_ppg:.2f} | Stake ${stake:.0f} | Bankroll ${bankroll:.0f}")\012            else:\012                bankroll -= stake\012                print(f"Game {game['id'][:8]}... LOSS: Prior PPG {prior_ppg:.2f} | Stake ${stake:.0f} | Bankroll ${bankroll:.0f}")\012        \012        # Update rolling with current (for next games)\012        stat = mcdavid_stats[game['id']]\012        past_points.append(stat['points'])\012    \012    # Final metrics\012    win_rate = (bet_wins / total_bets_placed * 100) if total_bets_placed else 0\012    roi = ((bankroll - initial_bankroll) / initial_bankroll * 100)\012    print(f"\nBacktest Summary ({total_bets_placed} bets):")\012    print(f"  Win Rate: {win\012\012⚠️ **Note:** Live backtesting encountered an issue. Please try again or use the Strategy Playground for detailed analysis.