本文目录导读：
我来为您设计一个门球短传配合成功率的统计系统,这个案例将包含数据输入、统计分析、可视化展示等功能。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import json
import os
class GateballPassingAnalyzer:
"""门球短传配合成功率分析系统"""
def __init__(self):
self.passes_data = []
self.players = {}
self.teams = {}
def add_pass_record(self, team_name, passer, receiver,
pass_type='short', distance=0,
is_success=True, position=None,
defense_pressure=False, match_id=None):
"""
添加传球记录
参数:
- team_name: 队伍名称
- passer: 传球队员
- receiver: 接球队员
- pass_type: 传球类型 (short/medium/long)
- distance: 传球距离（米）
- is_success: 是否成功
- position: 传球位置
- defense_pressure: 是否有防守压力
- match_id: 比赛编号
"""
record = {
'timestamp': datetime.now(),
'team': team_name,
'passer': passer,
'receiver': receiver,
'pass_type': pass_type,
'distance': distance,
'success': is_success,
'position': position,
'defense_pressure': defense_pressure,
'match_id': match_id
}
self.passes_data.append(record)
def analyze_short_pass_success_rate(self, team_name=None):
"""
分析短传成功率
统计不同维度的短传成功率
"""
df = pd.DataFrame(self.passes_data)
# 筛选短传数据
short_passes = df[df['pass_type'] == 'short']
if team_name:
short_passes = short_passes[short_passes['team'] == team_name]
if len(short_passes) == 0:
return None
# 总体成功率
total_success_rate = short_passes['success'].mean() * 100
# 按比赛统计
match_stats = short_passes.groupby('match_id').agg({
'success': ['count', 'sum', 'mean']
}).round(3)
# 按队伍统计
team_stats = short_passes.groupby('team').agg({
'success': ['count', 'sum', 'mean']
}).round(3)
# 按距离区间统计
short_passes['distance_range'] = pd.cut(
short_passes['distance'],
bins=[0, 5, 10, 15, 20],
labels=['0-5m', '5-10m', '10-15m', '15-20m']
)
distance_stats = short_passes.groupby('distance_range')['success'].agg(
['count', 'mean']
).round(3)
# 按防守压力统计
pressure_stats = short_passes.groupby('defense_pressure')['success'].agg(
['count', 'mean']
).round(3)
return {
'total_success_rate': total_success_rate,
'total_passes': len(short_passes),
'match_stats': match_stats,
'team_stats': team_stats,
'distance_stats': distance_stats,
'pressure_stats': pressure_stats,
'raw_data': short_passes
}
def analyze_player_passing(self, team_name, player_name):
"""
分析指定队员的短传表现
"""
df = pd.DataFrame(self.passes_data)
# 该队员作为传球者的短传
passer_data = df[
(df['team'] == team_name) &
(df['passer'] == player_name) &
(df['pass_type'] == 'short')
]
# 该队员作为接球者的短传
receiver_data = df[
(df['team'] == team_name) &
(df['receiver'] == player_name) &
(df['pass_type'] == 'short')
]
# 传球成功率和接球成功率
pass_success_rate = passer_data['success'].mean() * 100 if len(passer_data) > 0 else 0
receive_success_rate = receiver_data['success'].mean() * 100 if len(receiver_data) > 0 else 0
# 统计不同接球对象的成功率
if len(passer_data) > 0:
target_stats = passer_data.groupby('receiver')['success'].agg(['count', 'mean']).round(3)
else:
target_stats = None
return {
'player': player_name,
'team': team_name,
'pass_attempts': len(passer_data),
'pass_success': passer_data['success'].sum() if len(passer_data) > 0 else 0,
'pass_success_rate': pass_success_rate,
'receive_attempts': len(receiver_data),
'receive_success': receiver_data['success'].sum() if len(receiver_data) > 0 else 0,
'receive_success_rate': receive_success_rate,
'target_stats': target_stats
}
def analyze_combination_pairs(self, team_name=None):
"""
分析传球组合（谁传给谁最成功）
"""
df = pd.DataFrame(self.passes_data)
if team_name:
df = df[df['team'] == team_name]
df = df[df['pass_type'] == 'short']
# 创建组合标识
df['combination'] = df['passer'] + ' -> ' + df['receiver']
# 统计组合成功率
combination_stats = df.groupby('combination').agg({
'success': ['count', 'sum', 'mean']
}).round(3)
combination_stats.columns = ['次数', '成功次数', '成功率']
combination_stats = combination_stats.sort_values('次数', ascending=False)
return combination_stats
def visualize_results(self, results, save_path=None):
"""
可视化分析结果
"""
if not results:
print("没有可用的数据")
return
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
fig.suptitle('门球短传配合成功率分析', fontsize=16)
# 1. 总体成功率饼图
ax1 = axes[0, 0]
success_count = results['total_passes'] * results['total_success_rate'] / 100
fail_count = results['total_passes'] - success_count
ax1.pie([success_count, fail_count],
labels=['成功', '失败'],
autopct='%1.1f%%',
colors=['#2ecc71', '#e74c3c'])
ax1.set_title(f'总体成功率: {results["total_success_rate"]:.1f}%\n总次数: {results["total_passes"]}')
# 2. 距离区间成功率柱状图
ax2 = axes[0, 1]
distance_stats = results['distance_stats']
ax2.bar(distance_stats.index.astype(str),
distance_stats['mean'] * 100,
color='#3498db')
ax2.set_ylabel('成功率 (%)')
ax2.set_title('不同距离的成功率')
ax2.set_ylim([0, 100])
# 3. 防守压力对比图
ax3 = axes[0, 2]
pressure_stats = results['pressure_stats']
pressure_labels = ['无压力' if not x else '有压力' for x in pressure_stats.index]
ax3.bar(pressure_labels,
pressure_stats['mean'] * 100,
color=['#9b59b6', '#e67e22'])
ax3.set_ylabel('成功率 (%)')
ax3.set_title('防守压力对成功率的影响')
ax3.set_ylim([0, 100])
# 4. 队伍间对比
ax4 = axes[1, 0]
team_stats = results['team_stats']
if len(team_stats) > 0:
team_names = [x[0] for x in team_stats.index]
team_rates = [x[2] * 100 for x in team_stats.values]
ax4.bar(team_names, team_rates)
ax4.set_ylabel('成功率 (%)')
ax4.set_title('各队伍成功率对比')
ax4.set_ylim([0, 100])
# 5. 比赛趋势（如果有多场比赛）
ax5 = axes[1, 1]
match_stats = results['match_stats']
if len(match_stats) > 0:
match_ids = [x[0] for x in match_stats.index]
match_rates = [x[2] * 100 for x in match_stats.values]
ax5.plot(match_ids, match_rates, marker='o', linewidth=2)
ax5.set_ylabel('成功率 (%)')
ax5.set_title('各场比赛短传成功率趋势')
ax5.set_ylim([0, 100])
ax5.tick_params(axis='x', rotation=45)
# 6. 传球组合热力图
ax6 = axes[1, 2]
combo_stats = self.analyze_combination_pairs()
if len(combo_stats) > 0:
top_combos = combo_stats.head(10)
ax6.barh(top_combos.index, top_combos['成功率'] * 100, color='#1abc9c')
ax6.set_xlabel('成功率 (%)')
ax6.set_title('最佳传球组合（前10）')
ax6.set_xlim([0, 100])
plt.tight_layout()
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
print(f"图表已保存至: {save_path}")
else:
plt.show()
def export_report(self, file_path, results=None):
"""
导出详细报告
"""
if not results:
results = self.analyze_short_pass_success_rate()
with open(file_path, 'w', encoding='utf-8') as f:
f.write("=" * 50 + "\n")
f.write("门球短传配合成功率分析报告\n")
f.write(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
f.write("=" * 50 + "\n\n")
# 总体统计
f.write("【总体统计】\n")
f.write(f"总传球次数: {results['total_passes']}\n")
f.write(f"总体成功率: {results['total_success_rate']:.2f}%\n\n")
# 按距离统计
f.write("【按距离统计】\n")
f.write(results['distance_stats'].to_string())
f.write("\n\n")
# 按防守压力统计
f.write("【防守压力分析】\n")
f.write(results['pressure_stats'].to_string())
f.write("\n\n")
# 按队伍统计
f.write("【队伍统计】\n")
f.write(results['team_stats'].to_string())
f.write("\n\n")
# 最佳组合
f.write("【最佳传球组合 TOP10】\n")
combo_stats = self.analyze_combination_pairs()
f.write(combo_stats.head(10).to_string())
f.write("\n")
print(f"报告已导出至: {file_path}")
# 使用示例
def demo_usage():
"""演示使用方法"""
analyzer = GateballPassingAnalyzer()
# 模拟一些数据
import random
random.seed(42)
teams = ['红队', '蓝队']
players_red = ['张三', '李四', '王五', '赵六', '孙七']
players_blue = ['周八', '吴九', '郑十', '陈十一', '刘十二']
# 生成模拟数据
for match in range(1, 4):
for i in range(100): # 每场比赛100次传球
team = random.choice(teams)
if team == '红队':
players = players_red
else:
players = players_blue
passer = random.choice(players)
receiver = random.choice([p for p in players if p != passer])
# 80%为短传
pass_type = 'short' if random.random() < 0.8 else 'medium'
if pass_type == 'short':
distance = random.uniform(1, 15)
else:
distance = random.uniform(15, 25)
success = random.random() < 0.7 # 70%基础成功率
defense_pressure = random.random() < 0.3 # 30%有防守压力
analyzer.add_pass_record(
team_name=team,
passer=passer,
receiver=receiver,
pass_type=pass_type,
distance=distance,
is_success=success,
defense_pressure=defense_pressure,
match_id=f'M{match}'
)
# 分析
results = analyzer.analyze_short_pass_success_rate()
# 可视化
analyzer.visualize_results(results, save_path='short_pass_analysis.png')
# 导出报告
analyzer.export_report('short_pass_report.txt', results)
# 查看某个队员的表现
player_analysis = analyzer.analyze_player_passing('红队', '张三')
print(f"\n张三的传球分析:")
print(f"传球次数: {player_analysis['pass_attempts']}")
print(f"传球成功率: {player_analysis['pass_success_rate']:.2f}%")
print(f"接球次数: {player_analysis['receive_attempts']}")
print(f"接球成功率: {player_analysis['receive_success_rate']:.2f}%")
# 查看最佳组合
print("\n最佳传球组合:")
combo_stats = analyzer.analyze_combination_pairs()
print(combo_stats.head(5))
return analyzer, results
if __name__ == "__main__":
analyzer, results = demo_usage()
这个门球短传配合成功率分析系统具有以下特点：
主要功能：
-
数据记录功能
- 记录传球人、接球人、传球类型、距离、是否成功等详细信息
- 支持多场比赛数据管理
- 记录防守压力等复杂因素
-
统计分析功能
- 整体成功率统计
- 按距离分组的成功率分析
- 按防守压力对比
- 队伍间对比分析
- 个人传球和接球表现
- 传球组合分析（谁传给谁最成功）
-
可视化展示
- 成功率饼图
- 距离-成功率柱状图
- 防守压力影响图
- 队伍对比柱状图
- 比赛趋势折线图
- 最佳组合条形图
-
报告导出
- 自动生成详细分析报告
- 支持导出为文本文件
扩展建议：
-
添加更多分析维度
# 可以添加按位置分析 def analyze_by_position(self): positions = ['前场', '中场', '后场'] # 分析不同位置的成功率
-
实时数据输入界面
# 可以添加GUI界面 import tkinter as tk # 创建简单的数据输入表单
-
机器学习预测
# 使用历史数据预测成功率 from sklearn.linear_model import LogisticRegression # 根据距离、防守压力等特征预测成功率
-
视频分析集成
# 可以集成视频分析模块 import cv2 # 自动识别传球动作
这个系统可以根据实际需求进行扩展和修改,支持不同的使用场景。
