本文目录导读：
我来为您创建一个统计SofaScore综合评分的Python案例，由于SofaScore没有官方API，我将提供两种方法：模拟数据和网页爬取。
方法1：模拟数据统计（推荐教学用）
import pandas as pd
import numpy as np
from datetime import datetime
import random
class SofaScoreAnalyzer:
def __init__(self):
self.players_data = []
def generate_sample_data(self, num_players=20):
"""生成模拟的SofaScore球员数据"""
positions = ['前锋', '中场', '后卫', '门将']
teams = ['皇马', '巴萨', '曼城', '利物浦', '拜仁', '巴黎']
for i in range(num_players):
player = {
'player_id': f'P{i+1:03d}',
'name': f'球员{i+1}',
'team': random.choice(teams),
'position': random.choice(positions),
'age': random.randint(18, 35),
'rating': round(random.uniform(6.0, 9.5), 1), # SofaScore评分范围6.0-10.0
'goals': random.randint(0, 20),
'assists': random.randint(0, 15),
'minutes_played': random.randint(300, 3000),
'matches_played': random.randint(10, 38),
'yellow_cards': random.randint(0, 8),
'red_cards': random.randint(0, 2)
}
self.players_data.append(player)
return pd.DataFrame(self.players_data)
def analyze_ratings(self, df):
"""统计分析评分"""
print("=" * 60)
print("📊 SofaScore综合评分统计分析")
print("=" * 60)
# 1. 总体统计
print("\n【总体评分统计】")
print(f"平均评分: {df['rating'].mean():.2f}")
print(f"最高评分: {df['rating'].max():.2f}")
print(f"最低评分: {df['rating'].min():.2f}")
print(f"中位数: {df['rating'].median():.2f}")
print(f"标准差: {df['rating'].std():.2f}")
# 2. 按位置分析
print("\n【按位置评分统计】")
position_stats = df.groupby('position')['rating'].agg(['mean', 'max', 'min', 'count'])
print(position_stats.to_string())
# 3. 按球队分析
print("\n【按球队评分统计】")
team_stats = df.groupby('team')['rating'].agg(['mean', 'max', 'min', 'count'])
team_stats = team_stats.sort_values('mean', ascending=False)
print(team_stats.to_string())
# 4. 评分分布
print("\n【评分分布】")
rating_bins = pd.cut(df['rating'], bins=[6.0, 7.0, 7.5, 8.0, 8.5, 9.0, 10.0],
labels=['6.0-7.0', '7.0-7.5', '7.5-8.0', '8.0-8.5', '8.5-9.0', '9.0+'])
distribution = df['rating'].groupby(rating_bins, observed=False).count()
for interval, count in distribution.items():
bar = '█' * count
print(f"{interval}: {count}人 {bar}")
def find_top_players(self, df, top_n=5):
"""找出评分最高的球员"""
print("\n" + "=" * 60)
print(f"🏆 评分最高的{top_n}名球员")
print("=" * 60)
top_players = df.nlargest(top_n, 'rating')[['name', 'team', 'position', 'rating', 'goals', 'assists']]
for idx, player in top_players.iterrows():
print(f"⭐ {player['name']} ({player['team']}-{player['position']})")
print(f" 评分: {player['rating']} | 进球: {player['goals']} | 助攻: {player['assists']}")
def performance_metrics(self, df):
"""计算综合表现指标"""
print("\n" + "=" * 60)
print("📈 综合表现指数")
print("=" * 60)
# 综合评分（评分权重70% + 进攻贡献20% + 纪律10%）
df['attack_score'] = (df['goals'] * 2 + df['assists'] * 1.5) / 50
df['discipline_score'] = 10 - (df['yellow_cards'] * 0.5 + df['red_cards'] * 2)
df['overall_index'] = df['rating'] * 0.6 + df['attack_score'] * 30 + df['discipline_score'] * 0.4
# 标准化到0-100
df['overall_index'] = np.clip(df['overall_index'], 0, 100)
print("\n评分TOP10球员的综合指数:")
top_overall = df.nlargest(10, 'overall_index')[['name', 'team', 'rating', 'goals', 'assists', 'overall_index']]
print(top_overall.to_string(index=False))
return df
# 使用示例
if __name__ == "__main__":
# 创建分析器实例
analyzer = SofaScoreAnalyzer()
# 生成模拟数据
df = analyzer.generate_sample_data(num_players=30)
# 执行分析
analyzer.analyze_ratings(df)
analyzer.find_top_players(df, top_n=5)
df_analyzed = analyzer.performance_metrics(df)
# 导出数据
df_analyzed.to_csv('sofascore_ratings.csv', index=False, encoding='utf-8-sig')
print("\n✅ 数据已保存到 sofascore_ratings.csv")
方法2：使用爬虫获取真实数据（需要安装相应库）
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
class SofaScoreScraper:
"""SofaScore网页数据爬虫示例"""
HEADERS = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}
def fetch_player_ratings(self, league_url):
"""获取联赛球员评分数据"""
try:
response = requests.get(league_url, headers=self.HEADERS)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
players = []
# 这里假设页面结构包含球员评分信息
# 实际SofaScore的页面结构需要根据实际HTML调整
rating_elements = soup.select('.player-rating')
name_elements = soup.select('.player-name')
for name, rating in zip(name_elements, rating_elements):
player_data = {
'name': name.text.strip(),
'rating': float(rating.text.strip())
}
players.append(player_data)
return pd.DataFrame(players)
except Exception as e:
print(f"抓取失败: {e}")
return pd.DataFrame()
def analyze_token_market(self, data_file='sofascore_data.csv'):
"""分析已下载的数据文件"""
if os.path.exists(data_file):
df = pd.read_csv(data_file)
return df.describe()
return None
方法3：可视化分析
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
class SofaScoreVisualizer:
"""SofaScore数据可视化"""
def __init__(self, df):
self.df = df
def plot_rating_distribution(self):
"""绘制评分分布图"""
plt.figure(figsize=(10, 6))
plt.subplot(1, 2, 1)
sns.histplot(self.df['rating'], bins=20, kde=True, color='skyblue')
plt.title('球员评分分布')
plt.xlabel('SofaScore评分')
plt.ylabel('球员数量')
plt.subplot(1, 2, 2)
sns.boxplot(x='position', y='rating', data=self.df)
plt.title('不同位置评分对比')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('rating_analysis.png', dpi=300)
plt.show()
def plot_team_comparison(self):
"""绘制球队评分对比"""
plt.figure(figsize=(10, 6))
team_stats = self.df.groupby('team')['rating'].mean().sort_values(ascending=True)
plt.barh(range(len(team_stats)), team_stats.values)
plt.yticks(range(len(team_stats)), team_stats.index)
plt.xlabel('平均评分')
plt.title('各球队球员平均评分比较')
for i, v in enumerate(team_stats.values):
plt.text(v + 0.02, i, f'{v:.2f}', va='center')
plt.tight_layout()
plt.savefig('team_comparison.png', dpi=300)
plt.show()
# 综合使用示例
def main():
# 创建分析器
analyzer = SofaScoreAnalyzer()
# 生成数据
df = analyzer.generate_sample_data(num_players=50)
# 统计分析
analyzer.analyze_ratings(df)
analyzer.find_top_players(df)
df_analyzed = analyzer.performance_metrics(df)
# 可视化
visualizer = SofaScoreVisualizer(df_analyzed)
visualizer.plot_rating_distribution()
visualizer.plot_team_comparison()
# 保存结果
df_analyzed.to_csv('sofascore_full_analysis.csv', index=False)
print("分析完成！")
if __name__ == "__main__":
main()
运行需要的库：
pip install pandas numpy matplotlib seaborn beautifulsoup4 requests
主要功能：
- 数据分析：均值、中位数、标准差等统计指标
- 分组统计：按位置、球队分析
- 排名功能：找出评分最高的球员
- 综合指标：结合进球、助攻、纪律的综合评分
- 可视化：评分分布图、球队对比图
这个案例可以用于足球数据分析和球员评估,您可以根据实际需求调整评分权重和分析维度。
