80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
"""球員道具盤（Props）蒙地卡羅模擬模組。"""
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from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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@dataclass(frozen=True)
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class PlayerPropsDistribution:
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shots: np.ndarray
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shots_on_target: np.ndarray
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passes: np.ndarray
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def simulate_player_stats(
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player_metrics: dict,
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opponent_defense_metrics: dict,
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iterations: int = 10_000,
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) -> PlayerPropsDistribution:
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"""快速模擬球員事件次數分佈。"""
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if iterations <= 0:
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raise ValueError('iterations 必須大於 0')
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avg_touches = float(player_metrics.get('avg_touches', 45) or 0.0)
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base_shot_rate = float(player_metrics.get('shots_per_touch', 0.08) or 0.0)
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base_target_rate = float(player_metrics.get('shot_on_target_rate', 0.35) or 0.0)
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base_pass_rate = float(player_metrics.get('passes_per_touch', 0.65) or 0.0)
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opp_pressure = float(opponent_defense_metrics.get('pressing_index', 1.0) or 1.0)
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opp_tackling = float(opponent_defense_metrics.get('marking_index', 1.0) or 1.0)
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adj_touches = max(1.0, avg_touches * max(0.6, 1.0 / max(0.5, opp_pressure)))
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shot_lambda = adj_touches * base_shot_rate
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pass_lambda = adj_touches * base_pass_rate
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rng = np.random.default_rng()
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shots = rng.poisson(lam=shot_lambda, size=iterations)
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passes = rng.poisson(lam=pass_lambda, size=iterations)
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# 對方壓迫會降低射正率
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effective_target_rate = max(0.02, base_target_rate / max(opp_tackling, 0.3))
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shots_on_target = rng.binomial(shots, p=min(effective_target_rate, 0.99), size=iterations)
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return PlayerPropsDistribution(shots=shots.astype(int), shots_on_target=shots_on_target.astype(int), passes=passes.astype(int))
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def evaluate_prop_bet(
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simulated_distribution: PlayerPropsDistribution,
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line: float,
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odds: float,
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) -> dict[str, float | bool]:
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"""從 10,000 次模擬結果計算超過盤口機率與 EV。"""
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if odds <= 1:
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raise ValueError('odds 必須大於 1')
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if line < 0:
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raise ValueError('line 必須大於等於 0')
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shots = simulated_distribution.shots
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if shots.size == 0:
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raise ValueError('distribution 為空')
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probability_over = float((shots > line).mean())
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from .ev_calculator import calculate_expected_value
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ev = calculate_expected_value(probability_over, odds)
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return {
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'metric': 'shots',
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'line': line,
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'over_probability': round(probability_over, 6),
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'under_probability': round(1.0 - probability_over, 6),
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'implied_ev': ev['ev_value'],
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'ev_percentage': ev['ev_percentage'],
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'is_value_bet': bool(ev['is_value_bet']),
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}
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