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>龙珠直播>关于我们>荣誉概览>2025>中共龙珠直播联合支部委员会获评北京市“先进律师事务所党组织”光荣称号 多位优秀党员获得表彰

这个结论有可观性,尤其对手大多是曼城、利物浦、切尔西这类高压+控场的球队,皇马在阵地战里更难把机会转成高xG,更多依赖过

发布时间:2025-07-01

其对手大\n

Analyzing football statistics

I need to respond as Codex CLI to a user query in Chinese about football statistics. The user is making a statement about Real Madrid's performance in the Champions League against Premier League teams. To clarify, I might want to ask a follow-up question or provide some relevant analysis. It would be good to look into expected goals (xG) variance and matchups.

If I can, I could break down the last four seasons, discussing sample sizes and tactical reasons involved, or ask for the source.Clarifying xG analysis

I think I should be careful about providing precise stats here to avoid any inaccuracies. I might ask the user for a link or screenshot if they have a data source for the information. I could discuss why Real Madrid's expected goals (xG) might be lower against Premier League teams, especially top clubs like Man City and Liverpool.

While I could provide a table, it might be better to summarize with bullet points. I could also propose pulling match-by-match xG from sources like FBref or Understat. Since the user wrote in Chinese, I need to respond in Chinese to maintain clarity.Crafting xG Analysis Response

I want to provide a quick reaction and agree that Premier League teams often suppress Real Madrid's expected goals (xG). Real’s playing style tends to have lower xG with high conversion rates, relying on individual quality during critical moments and effective counter-attacks.

Next steps could include: asking the user for a season range and specific criteria to verify and create contrast charts, or offering to use Understat/FBref API scripts to compile data automatically.

建议明\n

I’ll keep it concise with bullet points instead of running code.Keeping it concise and casual

I think it's best to keep the formatting light and go with bullet points to match the user's casual style in Chinese. Keeping it concise is key! I could suggest a quick approach for computing stats using a Python snippet with requests to Understat's unofficial endpoints. However, since there’s no network, it might be better not to propose any coding right now. I can always offer to do it later if that’s something they want!

这个结论有可观性,尤其对手大多是曼城、利物浦、切尔西这类高压+控场的球队,皇马在阵地战里更难把机会转成高xG,更多依赖过渡、二次进攻和定位球的瞬间质量。

要把这话坐实,建议明确口径后核验:

情境比\n

  • 定义窗口:你说的“四年”是指20-21到23-24,还是含24-25?只算欧冠淘汰赛,还是包含小组赛?
  • 指标选择:用非点球xG(npxG)更干净;看“场均”和“总量”两种;是否把加时分开算。
  • 对照组:把皇马对英超的场均npxG,和对非英超对手、以及联赛平均对手做对比。
  • 比赛情境:比分效应(领先后收缩)和主客场会显著拉低xG,最好加控制或至少分层看。

经验判断上,最近几次和曼城的两回合普遍是皇马xG落后(但转化率、个体质量和门将表现弥补),对利物浦也常见一主一客强弱分明、xG被压制的半场。把这些集中起来看,“对英超场均xG更低”很可能成立。

需要的话我可以帮你把数据抓下来做个小表和图:

也常见\n

  1. 你偏好数据源:Understat 还是 FBref(StatsBomb)?
  2. 赛季范围与口径:只算淘汰赛、用非点球xG、含/不含加时?确认后我会汇总每场并给出对比结论与图。

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