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我将为您创建一个统计足球比赛中高球传中争顶成功率的Python案例,这个案例会模拟处理比赛数据并计算相关统计指标。
import pandas as pd
import numpy as np
from datetime import datetime
class CrossSuccessAnalyzer:
"""
高球传中争顶成功率分析器
"""
def __init__(self):
# 初始化统计数据结构
self.all_crosses = []
self.player_stats = {}
self.team_stats = {}
def add_cross_event(self, match_id, player_name, team_name,
cross_type, is_successful, defender_count=0,
is_set_piece=False, cross_zone=''):
"""
添加一个传中事件
参数:
- match_id: 比赛ID
- player_name: 球员姓名
- team_name: 球队名称
- cross_type: 传中类型 (高球/低平球/地面球)
- is_successful: 是否成功争顶
- defender_count: 防守球员数量
- is_set_piece: 是否定位球
- cross_zone: 传中区域
"""
event = {
'match_id': match_id,
'player_name': player_name,
'team_name': team_name,
'cross_type': cross_type,
'is_successful': is_successful,
'defender_count': defender_count,
'is_set_piece': is_set_piece,
'cross_zone': cross_zone,
'timestamp': datetime.now()
}
self.all_crosses.append(event)
# 更新球员统计
if player_name not in self.player_stats:
self.player_stats[player_name] = {
'total': 0, 'successful': 0, 'team': team_name
}
self.player_stats[player_name]['total'] += 1
if is_successful:
self.player_stats[player_name]['successful'] += 1
# 更新球队统计
if team_name not in self.team_stats:
self.team_stats[team_name] = {
'total': 0, 'successful': 0, 'high_ball': 0, 'high_success': 0
}
self.team_stats[team_name]['total'] += 1
if cross_type == '高球':
self.team_stats[team_name]['high_ball'] += 1
if is_successful:
self.team_stats[team_name]['high_success'] += 1
def generate_sample_data(self):
"""
生成示例数据用于测试
"""
sample_players = [
('梅西', '巴萨'), ('C罗', '皇马'), ('内马尔', '巴萨'),
('姆巴佩', '巴黎'), ('哈兰德', '曼城'), ('凯恩', '热刺')
]
# 模拟一场比赛的数据
for player, team in sample_players:
# 每个球员进行3-8次传中
for _ in range(np.random.randint(3, 9)):
cross_type = np.random.choice(['高球', '低平球', '地面球'],
p=[0.4, 0.35, 0.25])
is_successful = np.random.choice([True, False],
p=[0.4, 0.6])
self.add_cross_event(
match_id='MATCH001',
player_name=player,
team_name=team,
cross_type=cross_type,
is_successful=is_successful,
defender_count=np.random.randint(0, 5),
is_set_piece=np.random.choice([True, False], p=[0.2, 0.8])
)
def calculate_success_rate(self, data=None):
"""
计算成功率
返回: DataFrame包含各项统计指标
"""
if data is None:
data = self.all_crosses
df = pd.DataFrame(data)
if df.empty:
return pd.DataFrame()
# 计算总体统计
stats = []
# 1. 总体传中成功率
total = len(df)
successful = df['is_successful'].sum()
overall_rate = (successful / total * 100) if total > 0 else 0
stats.append({
'统计维度': '总传中',
'总次数': total,
'成功次数': successful,
'成功率(%)': round(overall_rate, 2)
})
# 2. 高球传中成功率
high_balls = df[df['cross_type'] == '高球']
if not high_balls.empty:
high_total = len(high_balls)
high_success = high_balls['is_successful'].sum()
high_rate = (high_success / high_total * 100) if high_total > 0 else 0
stats.append({
'统计维度': '高球传中',
'总次数': high_total,
'成功次数': high_success,
'成功率(%)': round(high_rate, 2)
})
# 3. 按传中类型统计
for cross_type in df['cross_type'].unique():
type_df = df[df['cross_type'] == cross_type]
type_total = len(type_df)
type_success = type_df['is_successful'].sum()
type_rate = (type_success / type_total * 100) if type_total > 0 else 0
stats.append({
'统计维度': f'{cross_type}传中',
'总次数': type_total,
'成功次数': type_success,
'成功率(%)': round(type_rate, 2)
})
return pd.DataFrame(stats)
def player_analysis(self, min_crosses=3):
"""
球员分析
参数:
- min_crosses: 最少传中次数（筛选标准）
返回: 球员排名DataFrame
"""
player_list = []
for player, stats in self.player_stats.items():
if stats['total'] >= min_crosses:
rate = (stats['successful'] / stats['total'] * 100)
player_list.append({
'球员': player,
'球队': stats['team'],
'总传中': stats['total'],
'成功争顶': stats['successful'],
'成功率(%)': round(rate, 2)
})
df = pd.DataFrame(player_list)
if not df.empty:
df = df.sort_values('成功率(%)', ascending=False).reset_index(drop=True)
return df
def team_high_ball_analysis(self):
"""
球队高球传中分析
"""
team_list = []
for team, stats in self.team_stats.items():
if stats['high_ball'] > 0:
high_rate = (stats['high_success'] / stats['high_ball'] * 100)
else:
high_rate = 0
total_rate = (stats['successful'] / stats['total'] * 100) if stats['total'] > 0 else 0
team_list.append({
'球队': team,
'总传中': stats['total'],
'总成功': stats['successful'],
'总成功率(%)': round(total_rate, 2),
'高球传中': stats['high_ball'],
'高球成功': stats['high_success'],
'高球成功率(%)': round(high_rate, 2)
})
df = pd.DataFrame(team_list)
if not df.empty:
df = df.sort_values('高球成功率(%)', ascending=False).reset_index(drop=True)
return df
def advanced_analysis(self):
"""
高级分析：考虑多种因素
"""
df = pd.DataFrame(self.all_crosses)
if df.empty:
return {}
# 1. 防守压力分析
def pressure_analysis():
pressure_groups = df.groupby(pd.cut(df['defender_count'],
bins=[0, 1, 3, 10],
labels=['低压力(0-1人)', '中等压力(2-3人)', '高压(4+人)']))
return pressure_groups['is_successful'].agg(['count', 'sum', 'mean']).assign(
success_rate=lambda x: round(x['mean'] * 100, 2)
)
# 2. 定位球vs运动战分析
def set_piece_analysis():
set_piece_stats = df.groupby('is_set_piece')['is_successful'].agg(['count', 'sum', 'mean'])
set_piece_stats.index = ['运动战', '定位球']
set_piece_stats['success_rate'] = round(set_piece_stats['mean'] * 100, 2)
return set_piece_stats
# 3. 区域分析（如果有区域数据）
def zone_analysis():
if 'cross_zone' in df.columns and df['cross_zone'].notna().any():
zone_stats = df.groupby('cross_zone')['is_successful'].agg(['count', 'sum', 'mean'])
zone_stats['success_rate'] = round(zone_stats['mean'] * 100, 2)
return zone_stats
return None
return {
'防守压力分析': pressure_analysis(),
'定位球分析': set_piece_analysis(),
'区域分析': zone_analysis()
}
def generate_report(self):
"""
生成完整报告
"""
print("=" * 60)
print("高球传中争顶成功率分析报告")
print("=" * 60)
# 1. 总体统计
print("\n【总体统计】")
overall_stats = self.calculate_success_rate()
print(overall_stats.to_string(index=False))
# 2. 球员排名
print("\n【球员排名（最少3次传中）】")
player_rank = self.player_analysis()
if not player_rank.empty:
print(player_rank.to_string(index=False))
else:
print("暂无足够数据")
# 3. 球队分析
print("\n【球队高球传中分析】")
team_analysis = self.team_high_ball_analysis()
if not team_analysis.empty:
print(team_analysis.to_string(index=False))
else:
print("暂无球队数据")
# 4. 高级分析
print("\n【高级分析】")
advanced = self.advanced_analysis()
print("\n防守压力对成功率的影响：")
print(advanced['防守压力分析'])
print("\n定位球vs运动战：")
print(advanced['定位球分析'])
if advanced['区域分析'] is not None:
print("\n传中区域分析：")
print(advanced['区域分析'])
def export_to_excel(self, filename='传中争顶分析.xlsx'):
"""
导出数据到Excel
"""
with pd.ExcelWriter(filename, engine='openpyxl') as writer:
# 原始数据
df = pd.DataFrame(self.all_crosses)
df.to_excel(writer, sheet_name='原始数据', index=False)
# 总体统计
self.calculate_success_rate().to_excel(writer, sheet_name='总体统计', index=False)
# 球员排名
self.player_analysis().to_excel(writer, sheet_name='球员排名', index=False)
# 球队分析
self.team_high_ball_analysis().to_excel(writer, sheet_name='球队分析', index=False)
# 高级分析
advanced = self.advanced_analysis()
advanced['防守压力分析'].to_excel(writer, sheet_name='防守压力分析')
advanced['定位球分析'].to_excel(writer, sheet_name='定位球分析')
print(f"\n数据已导出到 {filename}")
# 使用示例
if __name__ == "__main__":
# 创建分析器
analyzer = CrossSuccessAnalyzer()
# 生成示例数据
analyzer.generate_sample_data()
# 生成报告
analyzer.generate_report()
# 可选：导出到Excel
# analyzer.export_to_excel()
# 或者手动添加数据
# analyzer.add_cross_event(
# match_id='MATCH002',
# player_name='测试球员',
# team_name='测试队',
# cross_type='高球',
# is_successful=True,
# defender_count=2,
# is_set_piece=False,
# cross_zone='右路'
# )
这个案例包含以下功能：
主要功能：
- 数据收集 - 记录每次传中事件的详细信息
- 成功率计算 - 按不同维度统计成功率
- 球员分析 - 个人表现排名
- 球队分析 - 球队级别的统计
- 高级分析 - 考虑防守压力、定位球等因素
统计维度包括：
- 总体传中成功率
- 按传中类型（高球/低平球/地面球）分别统计
- 球员个人成功率排名
- 球队高球传中成功率
- 防守压力对成功率的影响
- 定位球vs运动战的对比
使用方法：
# 1. 创建分析器
analyzer = CrossSuccessAnalyzer()
# 2. 添加数据（可以手动添加）
analyzer.add_cross_event(
match_id='MATCH001',
player_name='球员A',
team_name='球队X',
cross_type='高球',
is_successful=True
)
# 3. 或生成示例数据
analyzer.generate_sample_data()
# 4. 生成分析报告
analyzer.generate_report()
你可以根据实际需求修改：
- 添加更多数据维度（如跑动距离、起跳高度等）
- 修改筛选条件
- 调整统计方法
- 扩展分析功能（如时间序列分析、趋势分析等）
