近10年欧冠门将零封率：拉亚54%第1，布冯47%第2，奥纳纳上榜（欧冠近十年门将零封率排行：拉亚54%登顶，布冯47%居次，奥纳纳在列）

发布时间：2026-02-10

Considering dataset analysis

I want to see if I can compute using an open dataset, but I need network access and approval to search the web. Instead of jumping straight to running a network call, it might be better to get permission first. I’ll keep things concise, providing context about clean sheet rates, cautioning about sample size, and different era/team variations. I can also summarize key players like Raya, Buffon, and Onana, and offer options like plotting charts or providing SQL/Python snippets for calculations. Let's lay out those next steps!Preparing response options

First, I’ll acknowledge the headline and ask what the user is interested in: source verification, a full list, visualization, or code to compute clean sheet rates. I want to include a quick context and any caveats.

quot

If they choose calculation, I'll give a Python snippet to compute the clean sheet rate from a dataset, with relevant columns like keeper, match_id, goals conceded, focusing on the last 10 years of UCL data. I’ll present this in a straightforward format. I also want to offer a one-liner in Chinese for clarity! Let’s get this response ready.

这是个有意思的榜单标题。你想要我：

核验数据并给出来源/完整榜单，
解释零封率计算口径和样本门槛，
直接给你一段代码，从你的比赛数据里算出近10年欧冠门将零封率并导出榜单？

快速提示

零封率=零封场次/出场场次（通常只算首发或≥X分钟），时间窗为最近10赛季或最近10个自然年，且仅限欧冠正赛。
要设置最少出场门槛（如≥10或≥15场），否则像拉亚这类欧冠样本较小的门将会因小样本偏高。
队伍防守强弱、淘汰赛对手强度、是否含加时等口径都会影响结果；需要提前约定。

如果你有原始数据，我可以本地算。下面是一段可直接用的 pandas 脚本：给它一份逐场数据 CSV，就能按“近10年欧冠”出门将零封率榜单。

code

# file: ucl_clean_sheet_rates.py
import pandas as pd
from datetime import datetime, timedelta

# 配置
INPUT_CSV = "matches.csv"  # 需要的列：keeper, date, competition, is_ucl, goals_conceded, started(可选), minutes(可选)
MIN_STARTS = 10            # 最少首发场次门槛
YEARS = 10

def is_clean_sheet(gc):
    try:
        return int(gc) == 0
    except:
        return False

def main():
    df = pd.read_csv(INPUT_CSV)

    # 统一字段
    if "is_ucl" in df.columns:
        ucl = df["is_ucl"] == True
    else:
        # 如果没有显式标记，则用 competition 文本包含规则（自行按你的数据调整）
        ucl = df["competition"].str.contains("UEFA Champions League", case=False, na=False)

    # 时间窗：最近10年
    df["date"] = pd.to_datetime(df["date"])
    cutoff = pd.Timestamp(datetime.now() - timedelta(days=365*YEARS))
    df = df[ucl & (df["date"] >= cutoff)].copy()

    # 出场口径：默认按 started 或 minutes>0 过滤
    if "started" in df.columns:
        played = df["started"].fillna(False)
        df = df[played]
    elif "minutes" in df.columns:
        df = df[df["minutes"].fillna(0) > 0]

    # 计算零封
    df["clean_sheet"] = df["goals_conceded"].apply(is_clean_sheet)

    grp = df.groupby("keeper").agg(
        matches=("clean_sheet", "size"),
        clean_sheets=("clean_sheet", "sum")
    ).reset_index()

    grp = grp[grp["matches"] >= MIN_STARTS].copy()
    grp["cs_rate"] = grp["clean_sheets"] / grp["matches"]

    grp = grp.sort_values(["cs_rate","matches","clean_sheets"], ascending=[False, False, False])

    # 输出
    grp["cs_rate_pct"] = (grp["cs_rate"] * 100).round(1)
    cols = ["keeper","matches","clean_sheets","cs_rate_pct"]
    grp[cols].to_csv("ucl_clean_sheet_rates_last10y.csv", index=False)
    print(grp.head(20)[cols].to_string(index=False))

if __name__ == "__main__":
    main()

需要的话把你的 matches.csv 的列名/口径告诉我，我帮你对上字段并跑一版；或者我去查公开数据源做一次核验并给出完整前十（需要你确认是否要我联网检索）。