你想要哪种“统计”？给我几个关键信息，我就按你需要的口径产出或直接给你脚本跑出结果。

请确认：

• 赛季范围：例如 2024-25（或多赛季对比？）
• 指标：基础（场次、进球、胜平负、主客场、场均进球、BTTS、Over2.5）还是含高级（xG、射门、牌数、角球、控球）？
• 输出形式：一张汇总表、分轮走势图、CSV/Excel、还是交互看板？
• 数据来源偏好：官方 API（football-data.org，需免费 API key），或网页数据（FBref/Understat，含xG）？你是否已有原始数据文件？

如果你想立刻跑一个“基础指标”的五大联赛赛季汇总，下面这份 Python 脚本可直接用 football-data.org（无需事件级明细，不能算牌数/xG，但胜平负/进球类足够稳定）。

说明

• 覆盖联赛：英超 PL、西甲 PD、德甲 BL1、意甲 SA、法甲 FL1
• 指标：总场次、总进球、场均进球、主/平/客占比、主/客场均进球、BTTS%（两队均进球）、Over2.5%
• 需要环境变量：FOOTBALL_DATA_API_KEY（在 football-data.org 的个人设置里创建）
• 输入参数：赛季起始年（如 2024 表示 2024-25 赛季）

代码

# file: five_leagues_stats.py
import os
import sys
import time
import math
import requests
import pandas as pd

API_BASE = "https://api.football-data.org/v4"
COMP_CODES = {
    "PL": "Premier League",
    "PD": "La Liga",
    "BL1": "Bundesliga",
    "SA": "Serie A",
    "FL1": "Ligue 1",
}

# Simple rate-limit helper for free tier
def api_get(path, params=None, token=None):
    headers = {"X-Auth-Token": token} if token else {}
    url = f"{API_BASE}{path}"
    r = requests.get(url, headers=headers, params=params, timeout=30)
    if r.status_code == 429:
        # too many requests; backoff a bit
        time.sleep(12)
        r = requests.get(url, headers=headers, params=params, timeout=30)
    r.raise_for_status()
    return r.json()

def fetch_matches(competition_code, season_year, token):
    # season_year is the starting year, e.g. 2024 for 2024-25
    data = api_get(
        "/matches",
        params={"competitions": competition_code, "season": season_year, "status": "FINISHED"},
        token=token,
    )
    return data.get("matches", [])

def compute_metrics(matches):
    # Convert to simple rows
    rows = []
    for m in matches:
        if m.get("status") != "FINISHED":
            continue
        home = m["homeTeam"]["name"]
        away = m["awayTeam"]["name"]
        hg = m["score"]["fullTime"]["home"] or 0
        ag = m["score"]["fullTime"]["away"] or 0
        rows.append((home, away, hg, ag))
    df = pd.DataFrame(rows, columns=["home", "away", "hg", "ag"])
    if df.empty:
        return {
            "matches": 0,
            "goals": 0,
            "gpg": 0.0,
            "home_win%": 0.0,
            "draw%": 0.0,
            "away_win%": 0.0,
            "home_gpg": 0.0,
            "away_gpg": 0.0,
            "BTTS%": 0.0,
            "Over2.5%": 0.0,
        }
    # Outcomes
    home_w = (df["hg"] > df["ag"]).sum()
    draw = (df["hg"] == df["ag"]).sum()
    away_w = (df["hg"] < df["ag"]).sum()
    n = len(df)

    goals = int(df["hg"].sum() + df["ag"].sum())
    gpg = goals / n

    home_gpg = df["hg"].mean()
    away_gpg = df["ag"].mean()

    btts = ((df["hg"] > 0) & (df["ag"] > 0)).mean()
    over25 = ((df["hg"] + df["ag"]) > 2).mean()

    return {
        "matches": int(n),
        "goals": goals,
        "gpg": round(gpg, 3),
        "home_win%": round(home_w / n * 100, 2),
        "draw%": round(draw / n * 100, 2),
        "away_win%": round(away_w / n * 100, 2),
        "home_gpg": round(home_gpg, 3),
        "away_gpg": round(away_gpg, 3),
        "BTTS%": round(btts * 100, 2),
        "Over2.5%": round(over25 * 100, 2),
    }

def main():
    if len(sys.argv) < 2:
        print("Usage: python five_leagues_stats.py <season_start_year>")
        print("Example: python five_leagues_stats.py 2024  # for 2024-25 season")
        sys.exit(1)
    season = int(sys.argv[1])
    token = os.getenv("FOOTBALL_DATA_API_KEY")
    if not token:
        print("ERROR: set environment variable FOOTBALL_DATA_API_KEY")
        sys.exit(2)

    records = []
    for code, name in COMP_CODES.items():
        try:
            matches = fetch_matches(code, season, token)
            metrics = compute_metrics(matches)
            metrics["league"] = name
            metrics["code"] = code
            metrics["season"] = f"{season}-{str(season+1)[-2:]}"
            records.append(metrics)
            # light pacing to be friendly to API
            time.sleep(1.2)
        except requests.HTTPError as e:
            print(f"Failed {name}: {e}")
        except Exception as e:
            print(f"Error {name}: {e}")

    if not records:
        print("No data collected.")
        sys.exit(0)

    cols = [
        "league", "season", "matches", "goals", "gpg",
        "home_win%", "draw%", "away_win%",
        "home_gpg", "away_gpg", "BTTS%", "Over2.5%", "code"
    ]
    df = pd.DataFrame(records)[cols].sort_values(["season", "league"])
    print(df.to_string(index=False))

    out = f"five_leagues_{season}.csv"
    df.to_csv(out, index=False)
    print(f"\nSaved: {out}")

if __name__ == "__main__":
    main()

使用步骤

• 申请/获取 API key 并设置环境变量：
  • macOS/Linux: export FOOTBALL_DATA_API_KEY='你的key'
  • Windows PowerShell: $env:FOOTBALL_DATA_API_KEY='你的key'
• 运行：python five_leagues_stats.py 2024
• 产出：控制台打印汇总表，并生成 five_leagues_2024.csv

想要更高级指标（xG、射门、控球、牌数）的话：

• 选项1：用 Understat（含xG）抓取赛季比赛并计算同样的统计与xG版统计。
• 选项2：爬取 FBref（更多团队/比赛层字段，含射门/角球/牌数），稳定性略受页面结构影响。
• 选项3：如果你有内部数据表或CSV，我给你写一版直接读取并计算统计的脚本，避免对外依赖。

告诉我你的赛季与指标需求，我可以直接帮你跑并给你最终表格/图。