Clarifying user request
I need to respond to the user about their sentence in Chinese, which translates to a headline about efficient forwards in European leagues. Since they didn’t ask a specific question, it’s unclear what they want—maybe details, a summary, or specific stats. I should ask a clarifying question to see if they want an article or analysis. However, since I can't browse real-time information, I should inquire about the source or date of the ranking they're interested in.Gathering user preferences
I should provide possible interpretations of the user’s request. They might want a quick news summary, a data table, or comparisons related to scoring efficiency per 90 minutes. I can offer to draft a post or create an infographic, but I need to ask which league or season they’re interested in. I should be concise and explain metrics like goals per 90, shots per goal, and conversion rate. Then I can propose a method to compute efficiency using a Python snippet if they share their dataset. That could be really useful!Suggesting player names
I can suggest names known for their scoring efficiency, like Haaland, Kane, Mbappe, Lautaro Martinez, and Victor Osimhen. Serhou Guirassy had an impressive start as a dark horse in the 2023/24 season, and Artem Dovbyk was the top scorer in La Liga that year. However, since it's now 2025, I shouldn't assume too much. I should ask the user if they want details on the dark horse, a top 10 list, or help crafting content. Also, I could offer to create an infographic once they provide specifics.Proposing steps for user assistance
I want to start by asking the user which outlet and season they’re referencing. If they want me to draft a post, I can create a 120-word Chinese news brief and a placeholder for the top 10 players. I could also offer a Python snippet to compute efficiency rankings if they provide a CSV with necessary stats like goals and shots. However, I need to ensure I have approval to access any data.
I’ll start my Chinese response with a quick reaction asking for their source or timeframe, then outline common efficiency metrics, and ask if they want me to calculate a leaderboard based on their data along with the code snippet.Defining efficiency calculations
I want to compute various efficiency metrics such as non-penalty goals (npG), goals per 90 minutes, shots per 90, and conversion rates. I could define an efficiency score as a composite of z-scores or a weighted sum with certain metrics. But it might be better to keep things simpler by just showing code that computes the ranking and prints out the top 15 players.
Also, I could offer to draft a news copy with placeholders for specific players, but I should avoid making assumptions about names. I can provide options like a quick news article, data recalculation from a CSV, or 10 headline options.
I’ll organize my final response in Chinese with concise bullet points and include a short social media template outlining the title, introduction, key points, and extras while ensuring it's within formatting constraints. Let’s get writing!
想跟进这条吗？给我来源链接或榜单截图/时间区间（赛季/统计口径），我可以还原前10名、关键数据和简短解读。也可以直接按你的数据算一版“效率榜”。
你可能关心的效率口径
- 非点球进球/90（npG/90）、进球/90、分钟/球
- 转化率（进球/射门）、射正率（射正/射门）
- 非点球进球−xG per 90（把握机会度）
- 触球进禁区/射门（若有）
- 样本门槛：≥900分钟，位置为前锋
需要我用数据复算？把CSV/Excel丢给我（列里有球员、球队、分钟、进球、点球、射门、射正、xG 就行）。下面是一段可直接跑的脚本，会产出“效率综合分”与榜单。
# file: rank_forward_efficiency.py
import pandas as pd
import numpy as np
# 读取你的数据文件
# 期望列：Player,Squad,Min,Gls,PK,Sh,SoT,xG,Pos（列名大小写可不同）
df = pd.read_csv("players_stats.csv")
# 统一列名
cols = {c.lower(): c for c in df.columns}
def get(col, default=0):
for k in cols:
if k == col.lower():
return df[cols[k]].fillna(default)
return pd.Series([default]*len(df))
df_out = pd.DataFrame()
df_out["Player"] = get("Player")
df_out["Squad"] = get("Squad")
df_out["Pos"] = get("Pos", "")
mins = get("Min").astype(float)
gls = get("Gls").astype(float)
pk = get("PK").astype(float)
sh = get("Sh").astype(float)
sot = get("SoT").astype(float)
xg = get("xG").astype(float)
# 基础衍生
mp90 = mins / 90.0
npG = gls - pk
df_out["Min"] = mins
df_out["Gls"] = gls
df_out["npG"] = npG
df_out["Sh"] = sh
df_out["SoT"] = sot
df_out["xG"] = xg
# 速率与比率
safe = lambda s: s.replace([np.inf, -np.inf], np.nan).fillna(0)
df_out["G/90"] = safe(gls / mp90)
df_out["npG/90"] = safe(npG / mp90)
df_out["Sh/90"] = safe(sh / mp90)
df_out["Conv%"] = safe(gls / sh) # 进球/射门
df_out["SoT%"] = safe(sot / sh) # 射正/射门
df_out["Min/G"] = safe(mins / gls).replace(0, np.nan).fillna(np.inf)
df_out["(npG-xG)/90"]= safe((npG - xg) / mp90)
# 过滤样本：分钟门槛与前锋位置（如果没有位置列就仅按分钟过滤）
is_fw = df_out["Pos"].str.contains("FW|ST|CF", case=False, na=False)
mask = (mins >= 900) & ( (is_fw) | (df_out["Pos"] == "") )
df_f = df_out.loc[mask].copy()
# Z-score 标准化并加权综合
def z(s):
s = s.replace([np.inf, -np.inf], np.nan)
return (s - s.mean()) / (s.std(ddof=0) + 1e-9)
z_npG90 = z(df_f["npG/90"])
z_conv = z(df_f["Conv%"])
z_sot = z(df_f["SoT%"])
z_npGxG90 = z(df_f["(npG-xG)/90"])
# 权重可按需调整
score = 0.45*z_npG90 + 0.25*z_conv + 0.15*z_sot + 0.15*z_npGxG90
df_f["EffScore"] = score
# 排序与输出
cols_show = ["Player","Squad","Min","Gls","npG","G/90","npG/90","Conv%","SoT%","(npG-xG)/90","EffScore"]
ranked = df_f.sort_values("EffScore", ascending=False)[cols_show].reset_index(drop=True)
print(ranked.head(15).to_string(index=False, formatters={
"Conv%": lambda v: f"{v:.2%}",
"SoT%": lambda v: f"{v:.2%}",
"G/90": lambda v: f"{v:.2f}",
"npG/90":lambda v: f"{v:.2f}",
"(npG-xG)/90": lambda v: f"{v:.3f}",
"EffScore": lambda v: f"{v:.3f}",
}))
你想要哪个方向？
- 我来写一篇150字新闻速递＋前三名解读（需要你提供榜单或来源）
- 用你的CSV复算并给出前10榜单＋雷达维度解释
- 做社媒海报文案/标题包（10个候选，含“黑马登顶”角度）