根据python案例，进攻三区传球成功率？

wen python案例 2026-08-27 176

本文目录导读：

完整代码案例
关键分析指标
扩展建议

我来为你提供一个分析进攻三区传球成功率的Python案例，这个分析通常用于足球数据分析,我会创建一个完整的示例。

完整代码案例

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
# 设置中文显示
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
# 1. 创建模拟数据
def create_sample_data():
    """创建模拟的传球数据"""
    np.random.seed(42)
    # 模拟1000次传球数据
    n_passes = 1000
    data = {
        'player_id': np.random.randint(1, 21, n_passes),  # 20名球员
        'player_name': np.random.choice(['张伟', '李强', '王浩', '刘洋', '陈杰',
                                          '杨帆', '赵磊', '孙斌', '周明', '吴涛',
                                          '郑强', '冯军', '胡飞', '王敏', '李华',
                                          '张磊', '刘强', '陈明', '杨光', '赵勇'], n_passes),
        'match_id': np.random.randint(1, 11, n_passes),  # 10场比赛
        'pass_type': np.random.choice(['短传', '长传', '直塞', '横传', '回传'], n_passes),
        'is_attacking_third': np.random.choice([True, False], n_passes, p=[0.4, 0.6]),  # 是否进攻三区
        'pass_success': np.random.choice([True, False], n_passes, p=[0.75, 0.25]),  # 传球成功率
        'pass_direction': np.random.choice(['向前', '横向', '向后'], n_passes),
        'pressure_level': np.random.choice(['低', '中', '高'], n_passes, p=[0.3, 0.4, 0.3]),
        'pass_distance': np.random.uniform(5, 40, n_passes)  # 传球距离(米)
    }
    df = pd.DataFrame(data)
    # 逻辑调整：进攻三区的成功率和压力有关
    for idx in df.index:
        if df.loc[idx, 'is_attacking_third']:
            # 进攻三区成功率较低
            success_prob = 0.55
            if df.loc[idx, 'pressure_level'] == '高':
                success_prob = 0.40
            elif df.loc[idx, 'pressure_level'] == '低':
                success_prob = 0.70
            df.loc[idx, 'pass_success'] = np.random.choice([True, False], p=[success_prob, 1-success_prob])
        else:
            # 非进攻三区成功率较高
            success_prob = 0.82
            if df.loc[idx, 'pressure_level'] == '高':
                success_prob = 0.70
            elif df.loc[idx, 'pressure_level'] == '低':
                success_prob = 0.90
            df.loc[idx, 'pass_success'] = np.random.choice([True, False], p=[success_prob, 1-success_prob])
    # 添加时间戳
    df['timestamp'] = pd.date_range('2024-01-01', periods=n_passes, freq='30s')
    return df
# 2. 数据分析函数
def analyze_attacking_third_passes(df):
    """分析进攻三区传球成功率"""
    print("=" * 60)
    print("进攻三区传球成功率分析")
    print("=" * 60)
    # 过滤出进攻三区的传球
    attacking_third_passes = df[df['is_attacking_third'] == True]
    # 总体成功率
    total_passes = len(attacking_third_passes)
    successful_passes = attacking_third_passes['pass_success'].sum()
    success_rate = successful_passes / total_passes * 100
    print(f"\n📊 总体统计:")
    print(f"进攻三区传球总次数: {total_passes}")
    print(f"成功传球次数: {successful_passes}")
    print(f"总体成功率: {success_rate:.2f}%")
    # 按传球类型分析
    print(f"\n📊 按传球类型分析:")
    pass_type_stats = attacking_third_passes.groupby('pass_type')['pass_success'].agg(['mean', 'count'])
    pass_type_stats['mean'] = pass_type_stats['mean'] * 100
    pass_type_stats.columns = ['成功率(%)', '传球次数']
    print(pass_type_stats.round(2))
    # 按传球方向分析
    print(f"\n📊 按传球方向分析:")
    direction_stats = attacking_third_passes.groupby('pass_direction')['pass_success'].agg(['mean', 'count'])
    direction_stats['mean'] = direction_stats['mean'] * 100
    direction_stats.columns = ['成功率(%)', '传球次数']
    print(direction_stats.round(2))
    # 按压力等级分析
    print(f"\n📊 按压力等级分析:")
    pressure_stats = attacking_third_passes.groupby('pressure_level')['pass_success'].agg(['mean', 'count'])
    pressure_stats['mean'] = pressure_stats['mean'] * 100
    pressure_stats.columns = ['成功率(%)', '传球次数']
    print(pressure_stats.round(2))
    # 按球员分析
    print(f"\n📊 球员表现Top 10:")
    player_stats = attacking_third_passes.groupby('player_name')['pass_success'].agg(['mean', 'count']).reset_index()
    player_stats['mean'] = player_stats['mean'] * 100
    player_stats.columns = ['球员', '成功率(%)', '传球次数']
    player_stats = player_stats.sort_values('成功率(%)', ascending=False).head(10)
    print(player_stats.round(2))
    # 按比赛分析
    print(f"\n📊 按比赛分析:")
    match_stats = attacking_third_passes.groupby('match_id')['pass_success'].agg(['mean', 'count'])
    match_stats['mean'] = match_stats['mean'] * 100
    match_stats.columns = ['成功率(%)', '传球次数']
    print(match_stats.round(2))
    return attacking_third_passes
# 3. 可视化函数
def visualize_analysis(attacking_third_passes, all_passes):
    """创建可视化图表"""
    fig, axes = plt.subplots(2, 2, figsize=(15, 12))
    fig.suptitle('进攻三区传球成功率分析', fontsize=16, fontweight='bold')
    # 1. 整体成功率对比（进攻三区 vs 非进攻三区）
    ax1 = axes[0, 0]
    zone_comparison = [
        attacking_third_passes['pass_success'].mean() * 100,
        all_passes[~all_passes['is_attacking_third']]['pass_success'].mean() * 100
    ]
    bars1 = ax1.bar(['进攻三区', '非进攻三区'], zone_comparison, color=['red', 'blue'], alpha=0.7)
    ax1.set_ylabel('成功率 (%)')
    ax1.set_title('进攻三区 vs 非进攻三区传球成功率')
    ax1.set_ylim(0, 100)
    for bar, value in zip(bars1, zone_comparison):
        ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1, 
                f'{value:.1f}%', ha='center', fontsize=12)
    # 2. 按传球类型的成功率
    ax2 = axes[0, 1]
    pass_type_rate = attacking_third_passes.groupby('pass_type')['pass_success'].mean() * 100
    sns.barplot(x=pass_type_rate.index, y=pass_type_rate.values, ax=ax2, palette='Set2')
    ax2.set_ylabel('成功率 (%)')
    ax2.set_title('进攻三区各传球类型成功率')
    ax2.set_ylim(0, 100)
    for i, v in enumerate(pass_type_rate.values):
        ax2.text(i, v + 1, f'{v:.1f}%', ha='center', fontsize=11)
    # 3. 压力等级与成功率的关系
    ax3 = axes[1, 0]
    pressure_data = attacking_third_passes.groupby(['pressure_level', 'pass_success']).size().unstack(fill_value=0)
    pressure_data = pressure_data.div(pressure_data.sum(axis=1), axis=0) * 100
    pressure_data.plot(kind='bar', stacked=True, ax=ax3, color=['#ff6b6b', '#4ecdc4'])
    ax3.set_ylabel('占比 (%)')
    ax3.set_title('不同压力等级下的传球结果分布')
    ax3.legend(['失败', '成功'], loc='upper right')
    ax3.set_xlabel('压力等级')
    # 4. 传球距离与成功率的关系（散点图）
    ax4 = axes[1, 1]
    success_passes = attacking_third_passes[attacking_third_passes['pass_success'] == True]
    fail_passes = attacking_third_passes[attacking_third_passes['pass_success'] == False]
    ax4.scatter(success_passes['pass_distance'], [1]*len(success_passes), 
               alpha=0.5, c='green', label='成功', s=60)
    ax4.scatter(fail_passes['pass_distance'], [0]*len(fail_passes), 
               alpha=0.5, c='red', label='失败', s=60)
    ax4.set_yticks([0, 1])
    ax4.set_yticklabels(['失败', '成功'])
    ax4.set_xlabel('传球距离 (米)')
    ax4.set_title('传球距离与成功率关系')
    ax4.legend()
    plt.tight_layout()
    plt.show()
    return fig
# 4. 高级分析：球员表现排名
def advanced_player_analysis(attacking_third_passes):
    """高级球员分析"""
    print("\n" + "=" * 60)
    print("高级球员分析")
    print("=" * 60)
    # 球员综合评分
    player_stats = attacking_third_passes.groupby('player_name').agg({
        'pass_success': ['sum', 'count', 'mean'],
        'pass_distance': 'mean'
    }).round(2)
    player_stats.columns = ['成功次数', '总传球数', '成功率', '平均距离']
    player_stats['成功率'] = player_stats['成功率'] * 100
    # 计算综合评分（自定义公式）
    player_stats['综合评分'] = (
        player_stats['成功率'] * 0.6 + 
        (player_stats['成功次数'] / player_stats['成功次数'].max()) * 100 * 0.4
    )
    # 排序
    player_rank = player_stats.sort_values('综合评分', ascending=False).head(5)
    print("\n🏆 最佳球员TOP 5:")
    print(player_rank)
    return player_rank
# 5. 时间序列分析
def time_series_analysis(attacking_third_passes):
    """时间序列分析"""
    print("\n" + "=" * 60)
    print("时间序列分析")
    print("=" * 60)
    # 按比赛阶段分析
    attacking_third_passes['比赛阶段'] = pd.cut(
        attacking_third_passes['timestamp'].dt.hour, 
        bins=[0, 45, 90], 
        labels=['上半场', '下半场']
    )
    stage_stats = attacking_third_passes.groupby('比赛阶段')['pass_success'].mean() * 100
    print("\n📈 比赛阶段分析:")
    print(stage_stats.round(2))
    return stage_stats
# 6. 导出报告
def export_report(attacking_third_passes, filename='attacking_third_analysis.csv'):
    """导出分析报告"""
    # 准备报告数据
    report_data = attacking_third_passes[['player_name', 'match_id', 'pass_type', 
                                          'pass_direction', 'pressure_level', 
                                          'pass_success', 'pass_distance']]
    report_data['is_attacking_third'] = True
    # 添加其他区域的数据
    all_passes = create_sample_data()
    non_attacking_third = all_passes[~all_passes['is_attacking_third']]
    non_attacking_third_report = non_attacking_third[['player_name', 'match_id', 'pass_type', 
                                                       'pass_direction', 'pressure_level', 
                                                       'pass_success', 'pass_distance']].copy()
    non_attacking_third_report['is_attacking_third'] = False
    # 合并数据
    full_report = pd.concat([report_data, non_attacking_third_report])
    # 保存到CSV
    full_report.to_csv(filename, index=False, encoding='utf-8-sig')
    print(f"\n✅ 报告已保存到 {filename}")
    return full_report
# 主程序
if __name__ == "__main__":
    print("🔍 开始足球传球数据分析...")
    # 创建数据
    df = create_sample_data()
    # 数据分析
    attacking_third_passes = analyze_attacking_third_passes(df)
    # 可视化
    fig = visualize_analysis(attacking_third_passes, df)
    # 高级分析
    player_rank = advanced_player_analysis(attacking_third_passes)
    # 时间序列分析
    stage_stats = time_series_analysis(attacking_third_passes)
    # 导出报告
    report = export_report(attacking_third_passes)
    print("\n" + "=" * 60)
    print("分析完成！")
    print("=" * 60)

关键分析指标

这个案例涵盖了以下核心分析维度：

基础统计

进攻三区传球总次数和成功率
不同传球类型的成功率（短传、长传、直塞等）

情境分析

传球方向（向前、横向、向后）
压力等级对成功率的影响
传球距离与成功率的关系

球员分析

球员个人成功率排名
综合评分系统（成功率+传球次数加权）

比赛分析

不同比赛的表现对比
上下半场的表现差异

案例中包含4个核心图表：

区域对比图：进攻三区 vs 非进攻三区成功率对比
传球类型分析：不同传球方式的成功率
压力等级分布：不同压力下的成败占比
距离散点图：传球距离与成功与否的关系

扩展建议

如果需要更深入的分析,可以：

# 扩展1：分析对手压力下的表现
def pressure_analysis(df):
    """分析高压下的表现"""
    high_pressure_data = df[(df['is_attacking_third']) & (df['pressure_level'] == '高')]
    return high_pressure_data.groupby('player_name')['pass_success'].mean().sort_values(ascending=False)
# 扩展2：动态成功率趋势
def dynamic_success_rate(df):
    """计算移动平均成功率"""
    df['success_rate'] = df['pass_success'].rolling(window=50).mean() * 100
    return df[['timestamp', 'success_rate']].dropna()

这个案例提供了完整的进攻三区传球成功率分析框架,可以根据实际数据调整优化。

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