想做哪种“关键球员统计”？先确认这几件事，我就能直接给你跑起来：

• 联赛与赛季：例如 中超 2024、英超 2023‑24 等
• 数据来源：你是否已有 CSV/Excel？字段里都有什么（进球、助攻、分钟、射门、xG/xA…）？
• 指标偏好：只看进球助攻，还是加上每90、xG/xA、关键传球、转化率、防守贡献等？
• 输出形式：排行榜表格、按队内占比、对比图表，或快速仪表盘（可用 Streamlit）
• 运行环境：本机 Python 可用吗？还是希望我只给出一份可粘贴即用的脚本

如果你已有一份球员逐场或赛季汇总 CSV，下面这段 Python/pandas 脚本可以直接生成“关键球员榜单”和导出的汇总表。默认假设这些列存在：player, team, minutes, goals, assists, shots, key_passes, xg, xa, penalties (可选), npxg (可选), tackles, interceptions, progressive_passes。没有的列自动用 0 代替，你也可以告诉我真实列名我来适配。

# stats_summary.py
import pandas as pd

# 配置：最少出场阈值（分钟）
MIN_MINUTES = 900

# 映射：你的原始列名 -> 标准列名（按需修改）
COLS = {
    "player": "player",
    "team": "team",
    "minutes": "minutes",
    "goals": "goals",
    "assists": "assists",
    "shots": "shots",
    "key_passes": "key_passes",
    "xg": "xg",
    "xa": "xa",
    "penalties": "pens",              # 可选
    "npxg": "npxg",                    # 可选（没有就用 xg 代替）
    "tackles": "tackles",
    "interceptions": "interceptions",
    "progressive_passes": "prog_pass"
}

def load(path):
    df = pd.read_csv(path)
    # 规范化列
    for src, dst in COLS.items():
        if src in df.columns:
            df.rename(columns={src: dst}, inplace=True)
    # 缺失列补零
    for c in ["goals","assists","shots","key_passes","xg","xa","pens","npxg",
              "tackles","interceptions","prog_pass"]:
        if c not in df.columns:
            df[c] = 0
    # 类型清洗
    for c in ["minutes","goals","assists","shots","key_passes","xg","xa","pens","npxg",
              "tackles","interceptions","prog_pass"]:
        df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0)
    df["player"] = df["player"].astype(str)
    df["team"] = df["team"].astype(str)
    return df

def add_derived(df):
    # 每90基数
    per90_base = df["minutes"].clip(lower=1) / 90.0
    # 进攻核心指标
    df["G"] = df["goals"]
    df["A"] = df["assists"]
    df["GA"] = df["G"] + df["A"]
    df["G90"] = (df["G"] / per90_base).round(3)
    df["A90"] = (df["A"] / per90_base).round(3)
    df["GA90"] = (df["GA"] / per90_base).round(3)
    df["xG90"] = (df["xg"] / per90_base).round(3)
    df["xA90"] = (df["xa"] / per90_base).round(3)
    df["xGxA90"] = ((df["xg"] + df["xa"]) / per90_base).round(3)

    # 非点球与转化率
    df["NPG"] = (df["G"] - df["pens"]).clip(lower=0)
    df["NPxG"] = df["npxg"].where(df["npxg"] > 0, df["xg"])  # 若无 npxg，用 xg 兜底
    df["NPG90"] = (df["NPG"] / per90_base).round(3)
    df["NPxG90"] = (df["NPxG"] / per90_base).round(3)
    df["ShotConv"] = (df["G"] / df["shots"].replace(0, pd.NA)).round(3)

    # 组织与推进
    df["KP90"] = (df["key_passes"] / per90_base).round(3)
    df["ProgP90"] = (df["prog_pass"] / per90_base).round(3)

    # 防守工作量（简化）
    df["DefActions90"] = ((df["tackles"] + df["interceptions"]) / per90_base).round(3)

    # 团队占比（以队内总数为分母）
    by_team = df.groupby("team", dropna=False)
    for m in ["G","A","GA","xg","xa","shots","key_passes"]:
        team_total = by_team[m].transform("sum").replace(0, pd.NA)
        df[f"{m}_Share"] = (df[m] / team_total).round(3)

    return df

def leaderboards(df, min_minutes=MIN_MINUTES):
    pool = df[df["minutes"] >= min_minutes].copy()
    boards = {
        "top_GA90": pool.sort_values(["GA90","minutes"], ascending=[False, False]).head(20),
        "top_xGxA90": pool.sort_values(["xGxA90","minutes"], ascending=[False, False]).head(20),
        "overperf_xG": pool.assign(
            delta=(pool["G"] - pool["xg"])
        ).sort_values("delta", ascending=False).head(20),
        "underperf_xG": pool.assign(
            delta=(pool["G"] - pool["xg"])
        ).sort_values("delta", ascending=True).head(20),
        "chance_creators": pool.sort_values(["KP90","xA90"], ascending=[False, False]).head(20),
        "finishers": pool.sort_values(["ShotConv","G90"], ascending=[False, False]).head(20),
        "two_way": pool.sort_values(["GA90","DefActions90"], ascending=[False, False]).head(20),
    }
    return boards

def main(in_path="players.csv", out_summary="player_summary.csv", out_dir="outputs"):
    import os
    os.makedirs(out_dir, exist_ok=True)
    df = load(in_path)
    df = add_derived(df)

    # 导出整表
    cols_export = ["player","team","minutes","G","A","GA","G90","A90","GA90",
                   "xg","xa","xG90","xA90","xGxA90","NPG","NPG90","NPxG","NPxG90",
                   "shots","ShotConv","key_passes","KP90","prog_pass","ProgP90",
                   "tackles","interceptions","DefActions90",
                   "G_Share","A_Share","GA_Share","xg_Share","xa_Share",
                   "shots_Share","key_passes_Share"]
    df[cols_export].to_csv(os.path.join(out_dir, out_summary), index=False)

    # 导出榜单
    boards = leaderboards(df)
    for name, tbl in boards.items():
        tbl_cols = ["player","team","minutes","G","A","GA","GA90","xGxA90",
                    "ShotConv","KP90","DefActions90"]
        tbl[tbl_cols].to_csv(os.path.join(out_dir, f"{name}.csv"), index=False)

    print(f"Done. Summary and leaderboards saved to: {out_dir}/")

if __name__ == "__main__":
    import argparse
    p = argparse.ArgumentParser()
    p.add_argument("--in", dest="in_path", default="players.csv", help="input CSV path")
    p.add_argument("--out-summary", dest="out_summary", default="player_summary.csv")
    p.add_argument("--out-dir", dest="out_dir", default="outputs")
    p.add_argument("--min-minutes", dest="min_minutes", type=int, default=MIN_MINUTES)
    args = p.parse_args()
    MIN_MINUTES = args.min_minutes
    main(args.in_path, args.out_summary, args.out_dir)

用法建议：

• 将你的赛季数据导出为 players.csv（放同目录），运行：python stats_summary.py --in players.csv --out-dir outputs
• 产出文件：
  • outputs/player_summary.csv：带每90、占比等的整表
  • outputs/top_GA90.csv、top_xGxA90.csv 等：关键榜单

告诉我：

• 你的联赛/赛季与原始列名截图或样例前几行
• 需要新增哪些指标（如传入三区、带球推进、对抗成功率、压迫/抢断/反抢等）
• 是否需要一个可视化仪表盘（我可帮你生成一个一键运行的 Streamlit 页面）