足球联赛关键球员数据统计（足球联赛核心球员数据概览）

Interpreting football stats request

I see the user is asking about key player data statistics for a football league. It seems like they might want help with computations or presenting those stats. But I realize I need to gather more information. Which league and season are we talking about? What specific stats are they interested in? I should also suggest a plan and mention the possibility of fetching data if that’s allowed. Lastly, I’ll need to propose a template or script to compute stats from a CSV.Providing coding options for player stats

I’m suggesting a Python script using Pandas to compute key player statistics like goals, assists, expected goals (xG), and more. I should ask the user if they have a data file ready. The schema should include attributes like player ID, name, team, position, and various stats, including minutes and contributions per 90 minutes. Since the user seems minimal, I need to clarify and offer options: I could provide a general template or suggest exporting data from FBref. I'll keep it concise!Setting up the compute_stats function

I need to create a function, compute_stats, and keep it minimal for the user. Since there's no specific repository context yet, I can propose writing a script, but I should hold off on shell execution until the user confirms. I’ll also ask a few targeted questions to gather more clarity, like which league or season they’re interested in, data sources, and desired output formats.


I plan to provide a sample of what I can produce, like a ready-to-run notebook or script, with a focus on pandas. I'll craft some concise questions in Chinese to assist the user effectively.Planning steps for data processing

I want to provide a small sample of columns and share a code snippet to help the user. For next steps, if they share a data file or specify the league and season, I can automatically fetch the dataset with permission. However, I need to be cautious about network use and approvals, keeping in mind that scraping is not allowed.

I should suggest using public APIs, like football-data.org with a free key, but I should be clear that complexities exist. It might be simpler if they provide a CSV from FBref or StatsBomb, noting that StatsBomb data is usually JSON. I’ll focus on creating a processing script designed to operate on their own data.

I'll craft the code to read the CSV and ensure minutes are parsed correctly, possibly standardizing "90s."Setting up derived metrics and outputs

I'll assume that minutes will be treated as an integer. For derived metrics, I plan to compute several key statistics:


• per90 factor, using minutes divided by 90.
• Goals per 90 (g_per90) and goal contributions per 90 (ga_per90).
• Expected goals and assists per 90 (xgxa_per90).
• Non-penalty goals (npg) will need a penalties column, or I’ll compute it from goals if absent.
• I'll also calculate shot conversion rate and key passes, progressive passes, and defensive metrics per 90.

To rank players, I’ll create tables for the top performers in terms of ga_per90 with at least 900 minutes played, and those who over or underperform based on xgxa.

I’ll save the results as a CSV. I’ll include a concise code snippet in a Python block and suggest an alternative. If the user wants a dashboard, I can quickly set up a Streamlit app. Let’s finalize the response!

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)

![essi](/assets/images/F82E761E.jpeg)

    # 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）