本文目录导读：
我来帮你创建几个Python案例来分析球队的近期状态,这里提供几个实用的方法：
方法1：从CSV文件分析球队状态
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
# 创建示例比赛数据
def create_sample_data():
data = {
'date': pd.date_range(end=datetime.now(), periods=30, freq='D'),
'team': ['主队'] * 15 + ['客队'] * 15,
'goals_for': [2, 1, 3, 0, 2, 1, 3, 2, 1, 2, 0, 3, 2, 1, 2] + [1, 0, 2, 1, 3, 0, 1, 2, 0, 1, 2, 1, 0, 2, 1],
'goals_against': [1, 0, 1, 2, 1, 1, 0, 1, 2, 0, 1, 2, 1, 0, 1] + [0, 1, 1, 0, 2, 1, 3, 1, 1, 0, 0, 2, 1, 1, 2]
}
df = pd.DataFrame(data)
return df
def analyze_team_form(df, team_name, recent_n=5):
"""分析球队近期状态"""
# 筛选球队数据
team_df = df[df['team'] == team_name].sort_values('date')
# 取最近N场比赛
recent = team_df.tail(recent_n).copy()
# 计算胜平负
results = []
for _, match in recent.iterrows():
if match['goals_for'] > match['goals_against']:
results.append('胜')
elif match['goals_for'] == match['goals_against']:
results.append('平')
else:
results.append('负')
recent['result'] = results
# 统计指标
stats = {
'胜场': results.count('胜'),
'平场': results.count('平'),
'负场': results.count('负'),
'胜率': results.count('胜') / recent_n * 100,
'进球数': recent['goals_for'].sum(),
'失球数': recent['goals_against'].sum(),
'场均进球': recent['goals_for'].mean(),
'场均失球': recent['goals_against'].mean()
}
# 积分计算（胜3分，平1分）
stats['积分'] = stats['胜场'] * 3 + stats['平场']
return recent, stats
def visualize_form(recent_df, team_name):
"""可视化球队近期状态"""
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# 进球趋势图
axes[0].plot(range(len(recent_df)), recent_df['goals_for'], 'o-', label='进球')
axes[0].plot(range(len(recent_df)), recent_df['goals_against'], 's-', label='失球')
axes[0].set_xlabel('比赛场次')
axes[0].set_ylabel('进球数')
axes[0].set_title(f'{team_name} 近期进球/失球趋势')
axes[0].legend()
axes[0].grid(True)
# 结果分布饼图
results = recent_df['result'].value_counts()
colors = ['green', 'yellow', 'red']
axes[1].pie(results, labels=results.index, autopct='%1.1f%%', colors=colors[:len(results)])
axes[1].set_title(f'{team_name} 近期比赛结果分布')
plt.tight_layout()
plt.show()
# 使用示例
df = create_sample_data()
team_name = '主队'
recent_matches, team_stats = analyze_team_form(df, team_name)
print(f"=== {team_name} 近期状态 ===")
print(f"近5场比赛：{' '.join(recent_matches['result'].tolist())}")
print(f"胜: {team_stats['胜场']} 平: {team_stats['平场']} 负: {team_stats['负场']}")
print(f"胜率: {team_stats['胜率']:.1f}%")
print(f"总进球: {team_stats['进球数']} 总失球: {team_stats['失球数']}")
print(f"场均进球: {team_stats['场均进球']:.1f} 场均失球: {team_stats['场均失球']:.1f}")
print(f"积分: {team_stats['积分']}")
# 可视化
visualize_form(recent_matches, team_name)
方法2：从API获取实时数据（以足球为例）
import requests
import json
from datetime import datetime, timedelta
def fetch_team_data(api_key, team_id):
"""从API获取球队数据（示例使用足球数据API）"""
headers = {'X-Auth-Token': api_key}
# 获取球队近期的比赛
url = f"https://api.football-data.org/v4/teams/{team_id}/matches"
params = {
'limit': 5,
'status': 'FINISHED'
}
try:
response = requests.get(url, headers=headers, params=params)
if response.status_code == 200:
return response.json()
else:
print(f"API请求失败: {response.status_code}")
return None
except Exception as e:
print(f"请求出错: {e}")
return None
def analyze_api_results(matches_data):
"""分析从API获取的比赛数据"""
if not matches_data or 'matches' not in matches_data:
return None
matches = matches_data['matches']
results = []
for match in matches:
home_goals = match['score']['fullTime']['home']
away_goals = match['score']['fullTime']['away']
# 判断主客场
home_team = match['homeTeam']['name']
away_team = match['awayTeam']['name']
# 计算比赛结果
if home_goals > away_goals:
result = '胜' if home_team else '负'
elif home_goals == away_goals:
result = '平'
else:
result = '负' if home_team else '胜'
results.append({
'date': match['utcDate'][:10],
'home': home_team,
'away': away_team,
'score': f'{home_goals}-{away_goals}',
'result': result
})
return results
方法3：多维度状态评估系统
import numpy as np
from collections import deque
class TeamFormAnalyzer:
"""多维度的球队状态分析器"""
def __init__(self, team_name):
self.team_name = team_name
self.matches = deque(maxlen=10) # 保存最近10场比赛
def add_match(self, goals_for, goals_against, opponent_strength=0.5):
"""添加一场比赛记录
opponent_strength: 对手强度 (0-1，1为最强)
"""
self.matches.append({
'goals_for': goals_for,
'goals_against': goals_against,
'result': 3 if goals_for > goals_against else (1 if goals_for == goals_against else 0),
'opponent': opponent_strength
})
def calculate_form_score(self):
"""计算综合状态评分 (0-100)"""
if not self.matches:
return 0
scores = []
weights = [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.0]
for i, match in enumerate(self.matches):
# 1. 结果得分 (40%)
result_score = match['result'] * 33.3/3
# 2. 进攻得分 (30%)
attack_score = min(match['goals_for'] / 3, 1) * 100
# 3. 防守得分 (20%)
defense_score = max(1 - match['goals_against'] / 3, 0) * 100
# 4. 对手强度得分 (10%)
opponent_score = match['opponent'] * 100
# 加权总分
match_score = (result_score * 0.4 +
attack_score * 0.3 +
defense_score * 0.2 +
opponent_score * 0.1)
# 应用时间权重（近期比赛权重更高）
match_score *= weights[len(self.matches)-1-i]
scores.append(match_score)
# 归一化
total_score = sum(scores) / sum(weights) if scores else 0
return min(total_score, 100)
def get_form_status(self):
"""获取状态等级"""
score = self.calculate_form_score()
if score >= 80:
return f"🔥 状态火热 ({score:.1f}分)"
elif score >= 60:
return f"✅ 状态良好 ({score:.1f}分)"
elif score >= 40:
return f"⚠️ 状态一般 ({score:.1f}分)"
else:
return f"❄️ 状态低迷 ({score:.1f}分)"
def predict_next_outcome(self):
"""预测下一场比赛"""
if len(self.matches) < 3:
return "数据不足，无法预测"
recent_results = [m['result'] for m in self.matches]
recent_gf = [m['goals_for'] for m in self.matches]
recent_ga = [m['goals_against'] for m in self.matches]
avg_gf = np.mean(recent_gf)
avg_ga = np.mean(recent_ga)
form_score = self.calculate_form_score()
# 简单概率预测
win_prob = 0.3 + (form_score / 100) * 0.3 + (avg_gf - avg_ga) * 0.05
win_prob = max(0.1, min(0.8, win_prob))
draw_prob = 0.2 * (1 - abs(avg_gf - avg_ga) / 3)
return {
'win_probability': win_prob,
'draw_probability': draw_prob,
'lose_probability': 1 - win_prob - draw_prob,
'expected_goals_for': avg_gf,
'expected_goals_against': avg_ga
}
# 使用示例
analyzer = TeamFormAnalyzer("示例球队")
# 模拟添加比赛
matches_data = [
(3, 1, 0.8), # (进球, 失球, 对手强度)
(2, 0, 0.6),
(1, 1, 0.5),
(3, 2, 0.7),
(0, 1, 0.9),
(2, 2, 0.4),
(4, 1, 0.3),
(1, 0, 0.8),
(2, 1, 0.7),
(3, 0, 0.5)
]
for gf, ga, opp in matches_data:
analyzer.add_match(gf, ga, opp)
# 输出分析结果
print(f"球队: {analyzer.team_name}")
print(f"状态评定: {analyzer.get_form_status()}")
print(f"综合评分: {analyzer.calculate_form_score():.2f}")
# 预测下一场
prediction = analyzer.predict_next_outcome()
print("\n下一场比赛预测:")
print(f"胜: {prediction['win_probability']*100:.1f}%")
print(f"平: {prediction['draw_probability']*100:.1f}%")
print(f"负: {prediction['lose_probability']*100:.1f}%")
print(f"预期进球: {prediction['expected_goals_for']:.1f} - 预期失球: {prediction['expected_goals_against']:.1f}")
方法4：简易可视化仪表盘
import dash
from dash import dcc, html
import plotly.graph_objects as go
import pandas as pd
def create_form_dashboard(teams_data, teams_names):
"""创建球队状态仪表盘"""
app = dash.Dash(__name__)
fig = go.Figure()
# 为每支球队添加状态曲线
for i, team_data in enumerate(teams_data):
results = ['胜', '平', '负', '胜', '平', '胜', '负', '胜', '胜', '平']
scores = [3, 1, 0, 3, 1, 3, 0, 3, 3, 1] # 积分
fig.add_trace(go.Scatter(
x=list(range(1, len(scores)+1)),
y=scores,
mode='lines+markers',
name=teams_names[i],
line=dict(width=2)
))
fig.update_layout(
title='球队近期状态对比',
xaxis_title='比赛场次',
yaxis_title='积分',
showlegend=True
)
app.layout = html.Div(children=[
html.H1(children='球队状态分析'),
dcc.Graph(
id='team-form-graph',
figure=fig
),
html.Div(id='team-stats')
])
return app
# 使用示例
teams_data = [
[3, 1, 0, 3, 1, 0, 3, 3, 1, 0],
[0, 3, 1, 0, 3, 1, 0, 3, 1, 3]
]
teams_names = ['主队', '客队']
# app = create_form_dashboard(teams_data, teams_names)
# app.run(debug=True)
使用方法：
- 方法1：适合分析本地保存的比赛数据
- 方法2：通过API获取实时数据
- 方法3：最全面的多维度状态评估系统
- 方法4：可视化的仪表盘展示
这个系统可以帮你：
- 快速评估球队近期胜率
- 分析进攻和防守表现
- 预测下一场比赛结果
- 可视化展示状态变化趋势
需要根据你的具体需求选择合适的方法！
