Implement TrueSkill rating system for NHL players

[Copilot]

Adds a Bayesian skill rating system for NHL players based on Microsoft's TrueSkill algorithm, enabling skill-based player evaluation weighted by time-on-ice contributions.

Core Implementation

• nhl_trueskill.py: TrueSkill rating engine with NHL-specific parameters (draw_probability=0.10 for OT/SO, TOI-weighted player contributions)
• Database schema: player_trueskill_ratings (current μ/σ/skill_estimate) and player_trueskill_history (per-game rating evolution)
• Season processing: Chronological game-by-game rating updates with team outcome propagation to individual players

Usage

from nhl_trueskill import NHLTrueSkillRatings

with NHLTrueSkillRatings() as ratings:
    # Process 2023-24 season
    ratings.process_season(season=2023, game_type=2)
    
    # Get top players (conservative estimate: μ - 3σ)
    top_players = ratings.get_top_players(limit=50, min_games=20)
    
    # Calculate team strength from lineup
    team_rating = ratings.calculate_team_rating(
        player_ids=[8478402, 8479318, 8477934],
        weights=[1200, 1150, 1100]  # TOI in seconds
    )

CLI Tools

• calculate_trueskill_ratings.py: Batch process seasons with --season 2023 or --all-seasons
• query_trueskill_ratings.py: Query top N players, search by name, view rating history, position rankings

Key Features

• Uncertainty tracking (σ decreases with game count)
• TOI-weighted contributions (more ice time = greater rating impact)
• Position-agnostic ratings comparable across forwards/defense/goalies
• JSON export for external analysis
• Rating history for time-series analysis

Dependencies

• trueskill>=0.4.5 (added to requirements.txt)

Original prompt

Look at the stats we are collecting and develop a TrueSkill type rating system for NHL Players. TrueSkill - Wikipedia https://share.google/AFxMNW8PwoxkMI6uN

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