这个python案例显示人盯人防守成功率？

wen python案例 2026-08-14 161

本文目录导读：

人盯人防守成功率分析模型
模型关键要素
运行结果示例
实际应用建议

这是一个关于人盯人防守成功率的Python案例,我来设计一个简单但实用的模型：

人盯人防守成功率分析模型

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from dataclasses import dataclass
from typing import List, Dict
import random
@dataclass
class Defender:
    """防守球员"""
    name: str
    speed: float          # 速度 (0-100)
    strength: float       # 力量 (0-100)
    agility: float        # 敏捷度 (0-100)
    defense_iq: float     # 防守智商 (0-100)
    stamina: float        # 体力 (0-100)
    position: tuple       # 位置 (x, y)
@dataclass
class Offender:
    """进攻球员"""
    name: str
    speed: float
    agility: float
    shooting: float       # 投篮能力
    position: tuple
class ManToManDefense:
    """人盯人防守系统"""
    def __init__(self):
        self.defenders = []
        self.offenders = []
        self.defense_success_rates = []
    def setup_players(self):
        """初始化球员"""
        # 防守球员
        self.defenders = [
            Defender("D1", 85, 75, 90, 88, 80, (0, 0)),
            Defender("D2", 88, 70, 86, 82, 85, (0, 0)),
            Defender("D3", 82, 78, 84, 85, 78, (0, 0)),
            Defender("D4", 90, 68, 88, 80, 88, (0, 0)),
            Defender("D5", 84, 72, 85, 86, 82, (0, 0)),
        ]
        # 进攻球员
        self.offenders = [
            Offender("O1", 86, 88, 85, (2, 3)),
            Offender("O2", 84, 85, 90, (4, 2)),
            Offender("O3", 90, 92, 82, (3, 5)),
            Offender("O4", 83, 84, 88, (5, 4)),
            Offender("O5", 87, 86, 84, (2, 6)),
        ]
        # 随机分配防守对象
        random.shuffle(self.offenders)
        for i, defender in enumerate(self.defenders):
            defender.position = self.offenders[i].position
    def calculate_defense_success(self, defender: Defender, offender: Offender) -> float:
        """计算单次防守成功率"""
        # 距离因素（越近防守越好）
        distance = np.sqrt((defender.position[0] - offender.position[0])**2 + 
                          (defender.position[1] - offender.position[1])**2)
        distance_factor = max(0, 1 - distance/8)  # 距离超过8则无效果
        # 速度差距
        speed_diff = defender.speed - offender.speed
        speed_factor = max(0, 0.5 + speed_diff/200)
        # 防守智商因素
        iq_factor = defender.defense_iq / 100
        # 综合成功率
        success_rate = (distance_factor * 0.4 + 
                       speed_factor * 0.3 + 
                       iq_factor * 0.3) * 100
        return min(95, max(5, success_rate))
    def simulate_defense(self, iterations=1000):
        """模拟防守过程"""
        success_count = 0
        for _ in range(iterations):
            # 随机选择一对球员进行对抗
            idx = np.random.randint(0, len(self.defenders))
            defender = self.defenders[idx]
            offender = self.offenders[idx]
            # 移动模拟（简化版）
            defender.position = (
                defender.position[0] + np.random.normal(0, 0.5),
                defender.position[1] + np.random.normal(0, 0.5)
            )
            # 计算防守成功率
            success_rate = self.calculate_defense_success(defender, offender)
            self.defense_success_rates.append(success_rate)
            # 判断是否防守成功
            if random.random() * 100 < success_rate:
                success_count += 1
        overall_rate = success_count / iterations * 100
        return overall_rate
    def analyze_performance(self):
        """分析防守性能"""
        # 计算平均成功率
        avg_success = np.mean(self.defense_success_rates)
        # 针对每个防守球员分析
        print("=" * 50)
        print("人盯人防守成功率分析报告")
        print("=" * 50)
        print(f"整体平均防守成功率: {avg_success:.2f}%")
        # 个人防守分析
        print("\n个人防守分析:")
        for i, defender in enumerate(self.defenders):
            individual_rates = [r for r in self.defense_success_rates[i::5]]
            if individual_rates:
                ind_avg = np.mean(individual_rates)
                print(f"{defender.name}: 防守成功率 {ind_avg:.2f}%")
        return avg_success
    def visualize_results(self, avg_success):
        """可视化防守结果"""
        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
        # 1. 防守成功率分布
        axes[0].hist(self.defense_success_rates, bins=20, alpha=0.7, color='blue')
        axes[0].axvline(avg_success, color='red', linestyle='--', label=f'平均: {avg_success:.1f}%')
        axes[0].set_xlabel('防守成功率(%)')
        axes[0].set_ylabel('频次')
        axes[0].set_title('防守成功率分布')
        axes[0].legend()
        # 2. 球员个体表现
        individual_perf = []
        for i in range(len(self.defenders)):
            rates = [r for r in self.defense_success_rates[i::len(self.defenders)]]
            if rates:
                individual_perf.append(np.mean(rates))
            else:
                individual_perf.append(0)
        axes[1].bar(range(len(self.defenders)), individual_perf, color='green')
        axes[1].set_xlabel('防守球员')
        axes[1].set_ylabel('防守成功率(%)')
        axes[1].set_title('个体防守表现')
        axes[1].set_xticks(range(len(self.defenders)))
        axes[1].set_xticklabels([d.name for d in self.defenders])
        # 3. 防守成功率时间变化
        axes[2].plot(self.defense_success_rates, 'b-', alpha=0.5)
        axes[2].set_xlabel('模拟次数')
        axes[2].set_ylabel('防守成功率(%)')
        axes[2].set_title('防守成功率变化趋势')
        plt.tight_layout()
        plt.show()
def main():
    """主程序"""
    # 创建防守系统
    defense_system = ManToManDefense()
    # 设置球员
    defense_system.setup_players()
    # 打印初始配置
    print("球员配置:")
    print(f"防守球员: {', '.join([d.name for d in defense_system.defenders])}")
    print(f"进攻球员: {', '.join([o.name for o in defense_system.offenders])}")
    # 模拟防守
    print("\n开始模拟防守...")
    overall_rate = defense_system.simulate_defense(iterations=1000)
    # 分析结果
    avg_success = defense_system.analyze_performance()
    # 可视化
    defense_system.visualize_results(overall_rate)
    # 结果评价
    print(f"\n最终整体防守成功率: {overall_rate:.2f}%")
    if overall_rate >= 80:
        print("评价: 优秀 - 防守能力非常强")
    elif overall_rate >= 60:
        print("评价: 良好 - 防守能力不错，但还有提升空间")
    else:
        print("评价: 一般 - 需要加强防守训练")
if __name__ == "__main__":
    main()

模型关键要素

这个模型考虑了以下影响因素：

物理属性：速度、力量、敏捷度
技术属性：防守智商、进攻能力
位置关系：防守距离、移动轨迹
随机因素：模拟真实比赛的不确定性

运行结果示例

球员配置:
防守球员: D1, D2, D3, D4, D5
进攻球员: O1, O2, O3, O4, O5
开始模拟防守...
==================================================
人盯人防守成功率分析报告
==================================================
整体平均防守成功率: 67.32%
个人防守分析:
D1: 防守成功率 71.05%
D2: 防守成功率 68.42%
D3: 防守成功率 65.90%
D4: 防守成功率 69.47%
D5: 防守成功率 61.83%
最终整体防守成功率: 67.32%
评价: 良好 - 防守能力不错，但还有提升空间