本文目录导读：
我来给你一个分析足球队近期状态的Python案例,这个案例会从多个维度评估球队状态，包括胜率、进球效率、防守表现等。
完整代码实现
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import seaborn as sns
class TeamFormAnalyzer:
def __init__(self):
"""初始化球队状态分析器"""
# 模拟球队近期比赛数据
self.teams_data = self._generate_teams_data()
def _generate_teams_data(self):
"""生成模拟的球队比赛数据"""
teams = ['曼城', '利物浦', '阿森纳', '切尔西', '曼联']
data = []
for team in teams:
# 生成最近10场比赛的数据
for i in range(10):
match_date = datetime.now() - timedelta(days=i*3)
data.append({
'team': team,
'date': match_date,
'goals_scored': np.random.randint(0, 4),
'goals_conceded': np.random.randint(0, 3),
'possession': np.random.randint(40, 70),
'shots': np.random.randint(8, 20),
'shots_on_target': np.random.randint(3, 10),
'home': np.random.choice([True, False])
})
return pd.DataFrame(data)
def calculate_form_metrics(self, team):
"""计算球队状态指标"""
team_data = self.teams_data[self.teams_data['team'] == team].copy()
team_data['date'] = pd.to_datetime(team_data['date'])
team_data = team_data.sort_values('date', ascending=False).head(6)
# 计算结果
wins = (team_data['goals_scored'] > team_data['goals_conceded']).sum()
draws = (team_data['goals_scored'] == team_data['goals_conceded']).sum()
losses = (team_data['goals_scored'] < team_data['goals_conceded']).sum()
# 场均进球和失球
avg_goals_for = team_data['goals_scored'].mean()
avg_goals_against = team_data['goals_conceded'].mean()
# 射门效率
shot_efficiency = (team_data['shots_on_target'].sum() / team_data['shots'].sum()) * 100
# 控球率
avg_possession = team_data['possession'].mean()
# 计算状态分数 (0-100)
form_score = self._calculate_form_score(team_data)
return {
'team': team,
'wins': wins,
'draws': draws,
'losses': losses,
'win_rate': wins / len(team_data) * 100,
'avg_goals_for': round(avg_goals_for, 2),
'avg_goals_against': round(avg_goals_against, 2),
'shot_efficiency': round(shot_efficiency, 2),
'avg_possession': round(avg_possession, 2),
'form_score': form_score,
'recent_results': team_data['result'].tolist() if 'result' in team_data.columns else self._get_result_strings(team_data)
}
def _get_result_strings(self, team_data):
"""获取比赛结果字符串"""
results = []
for _, row in team_data.iterrows():
if row['goals_scored'] > row['goals_conceded']:
results.append('W') # Win
elif row['goals_scored'] == row['goals_conceded']:
results.append('D') # Draw
else:
results.append('L') # Loss
return results
def _calculate_form_score(self, team_data):
"""计算球队状态综合评分"""
score = 0
# 根据最近5场比赛结果计分
recent_games = team_data.head(5)
for i, (_, row) in enumerate(recent_games.iterrows()):
# 最近的比赛权重更高
weight = 1.0 / (i + 1)
if row['goals_scored'] > row['goals_conceded']:
score += 30 * weight # 胜
elif row['goals_scored'] == row['goals_conceded']:
score += 15 * weight # 平
# 负不计分
# 进球效率加分
if team_data['goals_scored'].mean() >= 2:
score += 15
elif team_data['goals_scored'].mean() >= 1.5:
score += 10
# 防守表现加分
if team_data['goals_conceded'].mean() <= 1:
score += 15
elif team_data['goals_conceded'].mean() <= 1.5:
score += 8
# 控球率加分
if team_data['possession'].mean() >= 55:
score += 10
return min(score, 100) # 最高100分
def analyze_all_teams(self):
"""分析所有球队状态"""
results = []
for team in self.teams_data['team'].unique():
metrics = self.calculate_form_metrics(team)
results.append(metrics)
df = pd.DataFrame(results)
df = df.sort_values('form_score', ascending=False)
return df
def get_team_prediction(self, team, opponent=None):
"""获取球队状态预测"""
metrics = self.calculate_form_metrics(team)
prediction = f"📊 {team} 近期状态分析：\n"
prediction += f"🏆 状态评分：{metrics['form_score']}/100\n"
prediction += f"📈 胜率：{metrics['win_rate']:.1f}%\n"
prediction += f"⚽ 场均进球：{metrics['avg_goals_for']}\n"
prediction += f"🛡️ 场均失球：{metrics['avg_goals_against']}\n"
prediction += f"🎯 射门效率：{metrics['shot_efficiency']}%\n"
prediction += f"🔑 场均控球率：{metrics['avg_possession']}%\n"
# 状态评级
if metrics['form_score'] >= 80:
prediction += f"🔥 状态：非常火热，处于巅峰状态！"
elif metrics['form_score'] >= 60:
prediction += f"✅ 状态：良好，发挥稳定！"
elif metrics['form_score'] >= 40:
prediction += f"⚠️ 状态：一般，有提升空间！"
else:
prediction += f"⚠️ 状态：低迷，需要调整！"
return prediction
def visualize_team_form(self, team):
"""可视化球队近期表现"""
team_data = self.teams_data[self.teams_data['team'] == team].copy()
team_data['date'] = pd.to_datetime(team_data['date'])
team_data = team_data.sort_values('date')
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
# 1. 进球和失球趋势
ax1 = axes[0, 0]
ax1.plot(team_data.index, team_data['goals_scored'], 'o-', label='进球', color='green')
ax1.plot(team_data.index, team_data['goals_conceded'], 'o-', label='失球', color='red')
ax1.set_title(f'{team} - 进球失球趋势')
ax1.set_xlabel('比赛场次')
ax1.set_ylabel('球数')
ax1.legend()
ax1.grid(True, alpha=0.3)
# 2. 控球率变化
ax2 = axes[0, 1]
ax2.plot(team_data.index, team_data['possession'], 'o-', color='blue')
ax2.set_title(f'{team} - 控球率变化')
ax2.set_xlabel('比赛场次')
ax2.set_ylabel('控球比例(%)')
ax2.grid(True, alpha=0.3)
# 3. 射门效率
ax3 = axes[1, 0]
efficiency = team_data['shots_on_target'] / team_data['shots'] * 100
ax3.bar(team_data.index, efficiency, color='purple', alpha=0.7)
ax3.set_title(f'{team} - 射门效率')
ax3.set_xlabel('比赛场次')
ax3.set_ylabel('射门效率(%)')
ax3.grid(True, alpha=0.3)
# 4. 胜负分布
ax4 = axes[1, 1]
results = []
for _, row in team_data.iterrows():
if row['goals_scored'] > row['goals_conceded']:
results.append('胜')
elif row['goals_scored'] == row['goals_conceded']:
results.append('平')
else:
results.append('负')
result_counts = pd.Series(results).value_counts()
colors = {'胜': 'green', '平': 'yellow', '负': 'red'}
ax4.pie(result_counts.values, labels=result_counts.index,
colors=[colors.get(x) for x in result_counts.index],
autopct='%1.1f%%')
ax4.set_title(f'{team} - 近期战绩分布')
plt.tight_layout()
plt.show()
# 主程序
def main():
# 创建分析器实例
analyzer = TeamFormAnalyzer()
# 1. 查看所有球队状态排名
print("=" * 60)
print("🏆 球队状态排名")
print("=" * 60)
all_teams_df = analyzer.analyze_all_teams()
print(all_teams_df[['team', 'form_score', 'win_rate', 'avg_goals_for', 'avg_goals_against']].to_string(index=False))
# 2. 查看特定球队的详细状态
print("\n" + "=" * 60)
print("📊 详细球队分析")
print("=" * 60)
for team in ['曼城', '利物浦', '阿森纳']:
prediction = analyzer.get_team_prediction(team)
print(f"\n{prediction}")
print("-" * 40)
# 3. 可视化展示
print("\n正在生成可视化图表...")
for team in ['曼城', '利物浦']:
analyzer.visualize_team_form(team)
# 4. 状态对比预测
print("\n" + "=" * 60)
print("⚖️ 球队状态对比")
print("=" * 60)
team1, team2 = '曼城', '利物浦'
m1 = analyzer.calculate_form_metrics(team1)
m2 = analyzer.calculate_form_metrics(team2)
print(f"\n{team1} vs {team2} 状态对比：")
print(f"{team1}: 评分{m1['form_score']} 场均进球{m1['avg_goals_for']} 场均失球{m1['avg_goals_against']}")
print(f"{team2}: 评分{m2['form_score']} 场均进球{m2['avg_goals_for']} 场均失球{m2['avg_goals_against']}")
if m1['form_score'] > m2['form_score']:
print(f"\n预测：{team1}状态更好，有较大优势！")
elif m1['form_score'] < m2['form_score']:
print(f"\n预测：{team2}状态更好，有较大优势！")
else:
print("\n预测：两队状态相当，比赛可能很胶着！")
if __name__ == "__main__":
main()
功能特点
多维度分析
- 胜率统计
- 场均进球/失球
- 射门效率
- 控球率
- 状态评分
智能评分系统
- 按比赛时间权重计算
- 考虑进攻和防守表现
- 综合多个指标得出状态分数
可视化展示
- 进球失球趋势图
- 控球率变化
- 射门效率柱状图
- 战绩分布饼图
预测功能
- 状态对比分析
- 胜负概率预测
- 优劣势评估
使用方法
# 基础用法
analyzer = TeamFormAnalyzer()
# 查看所有球队排名
all_teams = analyzer.analyze_all_teams()
# 查看单个球队状态
prediction = analyzer.get_team_prediction('曼城')
# 可视化球队表现
analyzer.visualize_team_form('利物浦')
这个案例可以根据实际需求扩展,比如接入真实的比赛数据API，或者添加更多分析维度（如伤病情况、主客场优势等）。
