[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-wembanyama-stat-page-turns-into-recap-zh":3,"article-related-wembanyama-stat-page-turns-into-recap-zh":36,"series-tools-bdb4f000-f70d-47cd-a1c2-4c7262dcfb9f":86},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":18,"translated_content":10,"views":19,"is_premium":20,"created_at":21,"updated_at":21,"cover_image":11,"published_at":22,"rewrite_status":23,"rewrite_error":10,"rewritten_from_id":24,"slug":25,"category":26,"related_article_id":27,"status":28,"google_indexed_at":29,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":30,"topic_cluster_id":34,"embedding":35,"is_canonical_seed":20},"bdb4f000-f70d-47cd-a1c2-4c7262dcfb9f","Wembanyama 資料頁變成賽後整理","\u003Cp data-speakable=\"summary\">我把 NBA 球員資料頁拆成可直接套用的賽後整理模板，重點是怎麼從一堆數字挑出真正有用的故事。\u003C\u002Fp>\u003Cp>我盯著球員資料頁看了很多年，老實說，常常看得有點火大。數字一堆，身高、體重、順位、近五場、獎項、影片連結全塞在一起，但它不會直接告訴我重點。你如果只是把頁面照抄出來，讀者只會看到一坨資料，根本不知道哪個數字才是這場比賽真正的意思。我想要的不是「更多資訊」，而是把資訊整理成能直接拿去寫稿的東西。\u003C\u002Fp>\u003Cp>這次我拿 \u003Ca href=\"https:\u002F\u002Fwww.nba.com\u002Fplayer\u002F1641705\u002Fvictor-wembanyama\">NBA.com 的 Victor Wembanyama 球員頁\u003C\u002Fa> 來拆，原因很簡單：它就是那種資料給滿、但你還是得自己動腦的來源。問題不在資料少，問題在資料排得像倉庫，不像故事。我把它拆成幾個我自己會先看的層次：發生了什麼、哪些數字有重複性、哪些值得引用、\u003Ca href=\"\u002Fnews\u002Fbree-hall-returns-indiana-fever-player-development-zh\">最後\u003C\u002Fa>怎麼不亂編地寫成一段能交差的賽後整理。\u003C\u002Fp>\u003Cp>真正值錢的不是「找到數據」這件事。誰都能複製一行 box score。真正麻煩的是，你要知道哪些數字能撐起論點、哪些只是背景噪音，還有怎麼包裝到讀者不用自己解碼。\u003C\u002Fp>\u003Ch2>這頁不是文章，是一堆證據\u003C\u002Fh2>\u003Cblockquote>Wembanyama chipped in 39 points (13-18 FG, 3-5 3Pt, 10-12 FT), 15 rebounds, one assist, five blocks and one steal across 37 minutes during Friday's 115-108 victory over Minnesota in Game 3 of the Western Conference Semifinals.\u003C\u002Fblockquote>\u003Cp>我先講白話：這種來源不是整理好的故事，它只是把證據丟給你。NBA 球員頁會給一行比賽摘要、個人資料、近五場、新聞動態，這些東西夠你寫賽後整理，但前提是你不能把它當成資料垃圾場。你得把它看成證據清單。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779170691970-7pm6.png\" alt=\"Wembanyama 資料頁變成賽後整理\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>我很喜歡這類來源，因為它逼你有紀律。你不能亂掰。頁面說 39 分、15 籃板、5 火鍋、1 抄截，那就是故事核心。頁面說 37 分鐘，那也重要，因為它告訴你工作量。頁面說這是季後賽第二輪第 3 戰，而且球隊以 115 比 108 贏球，那上下文也到位了。你不用加戲，你要做的是把這些東西串起來。\u003C\u002Fp>\u003Cp>我自己拆這種頁面時，會先問三個問題：\u003C\u002Fp>\u003Cul>\u003Cli>這場最重要的單一數字是什麼？\u003C\u002Fli>\u003Cli>哪個上下文會改變這個數字的意義？\u003C\u002Fli>\u003Cli>哪一塊可以直接忽略，不影響重點？\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這篇的答案其實很明顯。標題不是「Wembanyama 得分很多」，而是「他在季後賽贏球戰裡打出一場攻守兩端都很兇的比賽」。這才是我會先寫的句子。\u003C\u002Fp>\u003Cp>實操寫法：先抓最新一場的摘要句，再回頭看近五場確認這是不是一場孤例。不要先看獎項、傳記或影片，先看最新結果，因為讀者通常只想知道「這場到底發生什麼事」。\u003C\u002Fp>\u003Ch2>box score 本身就是論點，不用硬拗形容詞\u003C\u002Fh2>\u003Cp>球員頁最容易害人寫過頭。我也踩過雷。看到一條很漂亮的數據，就想多補兩段，結果把原本很乾淨的重點寫爛。這場的 39 分、15 籃板、5 火鍋，就是整篇的核心，不是裝飾品，是證據。\u003C\u002Fp>\u003Cp>如果要翻成比較像人話的版本，我會這樣看：Wembanyama 不只是得分而已，他還抓板、護框，而且效率不差。18 投 13 中，代表他不是亂槍打鳥；三分球 5 投 3 中，代表他不是只在禁區附近混；罰球 12 投 10 中，代表他有持續製造犯規壓力。這是一場完整的進攻表現，不是某一節突然爆掉。\u003C\u002Fp>\u003Cp>防守端更不用說。季後賽拿 5 火鍋加 1 抄截，這不是「數字好看」而已，這是實際改變對手出手選擇。這種身材跟 timing 會讓對方不敢照原本的節奏攻框。你如果只寫「表現出色」，那就太偷懶了。你要寫的是：他怎麼出色，出色在哪裡，讓誰不舒服。\u003C\u002Fp>\u003Cp>我以前寫賽後整理常常犯一個毛病，就是太快下結論，什麼「統治級表現」講完就算。這很空。比較好的寫法是把表現拆成幾塊：得分效率、籃板控制、護框威嚇、罰球壓力。這樣讀者才知道你不是在喊口號。\u003C\u002Fp>\u003Cp>實操寫法：\u003C\u002Fp>\u003Cul>\u003Cli>先寫 stat line，不要先下形容詞。\u003C\u002Fli>\u003Cli>把 stat line 拆成進攻、防守、上下文三層。\u003C\u002Fli>\u003Cli>用投籃分布去證明這些分數怎麼來的。\u003C\u002Fli>\u003C\u002Ful>\u003Cp>如果是我來寫，我會直接下這句：Wembanyama 在季後賽第 3 戰交出 39 分、15 籃板、5 火鍋和 1 抄截，且以高效率投籃幫助\u003Ca href=\"\u002Fnews\u002Fspurs-vs-timberwolves-game-5-takeaways-zh-tw-zh\">馬刺\u003C\u002Fa>擊敗灰狼。這句就夠了，其他廢話先收起來。\u003C\u002Fp>\u003Ch2>近五場不是陪襯，是趨勢證明\u003C\u002Fh2>\u003Cp>近五場表格的價值，是讓我不會把單場當成神蹟。這頁顯示他在 5 月 8 日對灰狼拿 39 分，5 月 12 日對金塊拿 27 分和 17 籃板，5 月 15 日拿 19 分，5 月 18 日對雷霆則是 41 分和 24 籃板。這不是隨機波動，這是季後賽一路高輸出。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779170694396-3q11.png\" alt=\"Wembanyama 資料頁變成賽後整理\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>我喜歡這種欄位，因為它剛好夠你看出趨勢，但又不會逼你瞎掰故事。你不需要硬寫什麼「持續進化」或「進入新層級」這種空話。數字自己就在講：高使用率、高影響力，而且籃板數特別誇張。\u003C\u002Fp>\u003Cp>表格裡還有一個很重要的對照。5 月 10 日那場輸球，他只打 12 分鐘拿 4 分。這提醒我，一場低迷或被限制的比賽，不等於系列賽整體走向。很多人寫稿會只挑最大那條數字，然後假裝其他日期不存在。那樣寫出來很像粉絲帳號，不像整理者。\u003C\u002Fp>\u003Cp>比較好的做法，是拿近五場去支撐今天這場比賽，而不是取代今天這場比賽。賽後整理先講今天發生什麼，近況表格再補一句：這不是偶發，這是連續輸出。這樣論點比較穩。\u003C\u002Fp>\u003Cp>實操寫法：把近五場表格拿來回答「這場表現是不是一個趨勢的一部分？」如果是，就用一句話講趨勢；如果不是，就不要硬湊。趨勢語言不要亂貼在單場上。\u003C\u002Fp>\u003Ch2>球員資料只拿會改變意義的那幾項\u003C\u002Fh2>\u003Cp>球員頁很愛把背景塞滿，讓你覺得每個欄位都重要。這頁告訴我 Wembanyama 身高 7 呎 4 吋、235 磅、22 歲、來自法國、2023 年選秀狀元，還拿過一堆獎。這些不是廢話，但也不是每一項都值得寫進賽後整理。\u003C\u002Fp>\u003Cp>我通常只挑會改變讀者理解的資訊。這篇最有用的是年齡、身高、順位、經驗。這四項就夠了，因為它們能解釋為什麼一個 22 歲的大個子在季後賽打出 39 分、15 籃板，會讓人覺得很誇張。你不需要把整個履歷表搬進來。\u003C\u002Fp>\u003Cp>但我也不會把背景完全丟掉。背景資料的功能是幫你定錨，不是搶戲。它可以讓讀者知道這不是「普通年輕球員爆一場」，而是「一個狀元大個子在季後賽持續交出超齡數據」。差別很大。\u003C\u002Fp>\u003Cp>我自己的習慣是把背景壓成一句話，不超過一行。像這樣：這位 22 歲、來自法國的狀元中鋒，最近的季後賽數據看起來完全不像新秀。這樣就夠了，再多就像在重講選秀夜。\u003C\u002Fp>\u003Cp>實操寫法：\u003C\u002Fp>\u003Cul>\u003Cli>只用年齡、身高、順位、經驗這類會影響理解的資訊。\u003C\u002Fli>\u003Cli>不要把獎項清單整串貼上去。\u003C\u002Fli>\u003Cli>背景句要短，讓比賽內容站在前面。\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>新聞動態是在告訴你，頁面自己認為重點是什麼\u003C\u002Fh2>\u003Cp>新聞區塊很好用，前提是你知道它只是指標，不是成品。這頁一直在推高分比賽、系列賽關鍵場次、還有一些爭議訊息，像是西區決賽 41 分、24 籃板、\u003Ca href=\"\u002Fnews\u002Fwembanyama-game-5-revenge-test-wolves-zh\">Game\u003C\u002Fa> 6 拿 19 分、Game 5 拿 27 分、Game 4 被驅逐，後面又補了一個不會被禁賽的說明。這整串其實就是這段季後賽的骨架。\u003C\u002Fp>\u003Cp>我不是要你把新聞區塊直接抄進文章，這樣太懶了。但它很適合拿來判斷「這個頁面到底在強調什麼」。如果同一類型的比賽一直被推上來，那代表這個球員正在季後賽跑一條很明確的敘事線，不是單純的熱手。\u003C\u002Fp>\u003Cp>我以前常用這招快速判讀來源：看版面怎麼排，就知道它在暗示你什麼。當然，你還是要回頭驗證數字，但你可以先用它來校正摘要的方向。這頁很明顯就是在說：Wembanyama 的季後賽輸出是主線，而 Game 3 只是其中一章。\u003C\u002Fp>\u003Cp>實操寫法：先掃新聞區塊有沒有重複主題。如果同一種比賽型態或同一種數據形狀一直出現，就在你的整理裡提一次；如果新聞區塊很雜，就直接忽略，回到 box score。\u003C\u002Fp>\u003Ch2>真正好用的寫法，其實很無聊\u003C\u002Fh2>\u003Cp>把頁面外殼拆掉後，賽後整理的公式其實很簡單。它不需要聰明，反而需要穩。我要的是一個能尊重來源、又像人話的順序。\u003C\u002Fp>\u003Cp>我通常會照這個順序寫：\u003C\u002Fp>\u003Cul>\u003Cli>先講比賽結果和 stat line。\u003C\u002Fli>\u003Cli>再講這條 stat line 為什麼重要。\u003C\u002Fli>\u003Cli>補一個近況趨勢。\u003C\u002Fli>\u003Cli>必要時加一個背景資料。\u003C\u002Fli>\u003Cli>講完就收，不要一直重複。\u003C\u002Fli>\u003C\u002Ful>\u003Cp>就這樣。不要假裝每一場爆發都代表什麼宿命，也不要把每個數字都寫成史詩。來源頁面已經給你足夠材料，真正的工作是選擇與排序，不是硬塞更多字。\u003C\u002Fp>\u003Cp>如果我要編輯這篇，我會先問三個問題：發生什麼事？為什麼重要？這是不是更大趨勢的一部分？NBA 球員頁只要你抓對欄位，這三個問題都能回答。抓錯欄位，就會寫成一篇很長但沒重點的東西。\u003C\u002Fp>\u003Cp>實操寫法：第一版先從摘要句直接寫，第二版再用近五場補一個趨勢句。如果你還想一直加內容，通常就是已經偏離來源了。\u003C\u002Fp>\u003Ch2>可抄的模板\u003C\u002Fh2>\u003Cpre>\u003Ccode># [球員姓名] 把資料頁寫成賽後整理\n\n[球員姓名] 在 [球隊／比賽結果／上下文] 交出 [stat line]，這條數據本身就把重點講完了： [得分效率]、[籃板]、[防守影響]、以及 [上場時間或工作量]。\n\n白話講就是：[用一句話解釋這場表現的真正意義]。\n\n## 來源寫了什麼\n- 結果：[球隊] 以 [比數] 擊敗／不敵 [對手]\n- 數據：[得分] 分、[籃板] 個、[助攻] 次、[火鍋] 次、[抄截] 次\n- 投籃：[FG]、[三分]、[罰球]\n- 上場時間：[分鐘]\n\n## 為什麼重要\n[用白話說明這場表現跟比賽上下文的關係，不要只寫「表現出色」]\n\n## 趨勢在哪裡\n[用近五場或近期比賽紀錄，說明這是不是連續輸出的一部分。只挑能支持論點的數字。]\n\n## 背景補充\n[如果背景資料有幫助，就用一句話補：年齡、身高、順位、經驗或國籍。]\n\n## 可直接貼上的賽後整理段落\n[球員姓名] 在 [球隊] 對上 [對手] 的 [勝／敗] 中繳出 [stat line]，同時用 [進攻細節] 和 [防守細節] 撐起整場比賽，且在 [分鐘數] 內維持高效率。這場表現也延續了他最近的 [趨勢]，而背景資料更讓這份數據顯得誇張： [一句短的背景定錨]。\n\n## 編輯原則\n如果某個數字不能回答「發生什麼事」或「為什麼重要」，就不要放進去。\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>這份模板故意寫得很樸素，因為樸素才好用。它可以直接套進任何球員、任何 stat line、任何結果，照樣能產出一篇讀得懂的賽後整理。你不需要每次都重新發明格式。\u003C\u002Fp>\u003Cp>對這種來源來說，模板特別有效，因為 NBA 球員頁本來就把原料端上桌了。你真正要做的是挑選和排序。很多人會跳過這一步，然後抱怨自己的稿子又長又散。我看過太多次了，真的不是資料不夠，是你沒整理。\u003C\u002Fp>\u003Cp>來源致謝：原始資料來自 \u003Ca href=\"https:\u002F\u002Fwww.nba.com\u002Fplayer\u002F1641705\u002Fvictor-wembanyama\">NBA.com 的 Victor Wembanyama 球員頁\u003C\u002Fa>。我把它重組成適合\u003Ca href=\"\u002Ftag\u002F台灣開發者\">台灣開發者\u003C\u002Fa>閱讀的寫作模板，文中的拆解邏輯與模板文字主要是我自己的整理與改寫。\u003C\u002Fp>\u003Cp>如果你要回頭查原始脈絡，可以先看 \u003Ca href=\"https:\u002F\u002Fwww.nba.com\u002F\">NBA.com\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.nba.com\u002Fspurs\">聖安東尼奧馬刺官方頁\u003C\u002Fa>，還有 \u003Ca href=\"https:\u002F\u002Fwww.nba.com\u002Fplayer\u002F1641705\u002Fvictor-wembanyama\">Wembanyama 的官方球員頁\u003C\u002Fa>。我這篇是衍生整理，不是原始報導。\u003C\u002Fp>","我把 NBA 球員資料頁拆成可直接套用的賽後整理模板，重點是怎麼從一堆數字挑出真正有用的故事。","www.nba.com","https:\u002F\u002Fwww.nba.com\u002Fplayer\u002F1641705\u002Fvictor-wembanyama",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779170691970-7pm6.png",[13,14,15,16,17],"NBA 資料頁","賽後整理","box score","近五場趨勢","寫作模板","zh",2,false,"2026-05-19T06:04:22.282386+00:00","2026-05-19T06:04:22.121+00:00","done","469ef367-4b6c-48d6-bea0-001b60bd182b","wembanyama-stat-page-turns-into-recap-zh","tools","b2087857-a6d4-4119-8c15-f126e692c909","published","2026-05-19T09:00:33.262+00:00",[31,32,33],"先抓最新一場摘要，再用近五場確認是不是趨勢。","stat line 本身就是論點，不要先堆形容詞。","背景資料只留會改變讀者理解的那幾項。","c3c88dd2-a940-438a-b359-0e5a24562273","[-0.007073104,0.0072400905,0.0033405835,-0.08500133,0.017802496,0.00502637,-0.012318344,0.0075003235,0.008946144,-0.005341404,-0.018809665,-0.020602996,-0.013601885,0.004255761,0.104168616,0.015784157,0.03059293,-0.009165834,0.0014822602,0.017906928,0.035317246,0.028445182,0.024214087,-0.024118505,0.0029639998,-0.016473865,0.009310384,0.019542294,0.031525183,0.010112637,0.028152416,0.03588185,0.037980698,-0.008633968,-0.0045103366,0.011856338,0.01801777,-0.0044589113,-0.025062913,0.008775141,-0.003326997,0.009485832,0.006804508,-0.015710616,0.002875821,0.014416227,-0.006844429,-0.058783293,0.013070944,0.025457012,0.01405021,-0.009061634,-0.0043453537,-0.16613895,0.007726502,0.017220562,-0.020854104,0.010144767,-0.0102128135,0.015292557,-0.018683968,0.02032212,-0.0078002787,-0.026938247,-0.013775306,-0.013929765,0.027657898,-0.018119795,-0.03269401,-0.0045785573,-0.015134304,-0.018482413,-0.045130055,0.0032450364,0.014026905,-0.047122158,0.009996326,0.0019050584,-0.011539212,0.004029965,0.000681397,-0.0022586111,0.011043332,0.016749734,0.021850277,0.003998237,-0.008263887,0.016362818,0.0139252255,0.014508177,0.0015108384,0.03286513,0.004326648,-0.0069124824,0.012037865,0.002036459,0.004199871,-0.004630347,0.010817697,-0.021567367,-0.0013404904,-0.012785783,-0.017918456,-0.00530749,-0.015013163,0.04723648,-0.001585013,-0.010082461,0.013904104,-0.0110535715,0.0074123894,0.0033764166,0.009373238,0.009369492,-0.0009456382,-0.13571534,-0.003913154,0.025911087,0.013739461,0.015765946,-0.0024969385,-0.013730257,0.0012078723,0.034917086,-0.0046620723,-0.011006012,0.013472176,0.012767412,-0.0045598685,-0.021392554,-0.010196241,0.009141402,-0.040643536,0.0024524482,0.003028459,0.004936992,0.00087293,-0.015854616,-0.0066693574,-0.016817946,0.0019490505,0.029416678,0.0026889048,0.008539908,-0.029408528,-0.030013962,-0.02285388,0.031006131,-0.044598766,-0.010507993,0.003275782,-0.023415692,-0.038208794,0.0156160835,0.035591632,-0.009245503,-0.011543676,0.0030612587,-0.0027659389,0.023814166,-0.0008554759,0.016493183,-0.014289045,0.034108885,-0.00425125,0.049172238,-0.023079924,0.00094741525,0.019277336,0.004253406,-0.014272432,0.004240016,-0.0073773465,-0.011738615,0.0029270148,-0.00866527,0.011034236,-0.00463197,-0.010757838,-0.018775865,0.035077136,-0.025588222,-0.001399271,0.026533911,-0.006564787,0.013565501,-0.0023113457,0.029647855,0.015851907,-0.012647549,-0.024469694,0.013139836,0.031457093,-0.003218749,0.011007933,-0.027378663,-0.01654972,-0.007602251,0.01260129,0.020847201,-0.0039501744,-0.0012825185,-0.005369393,-0.015352662,0.01323633,-0.019860618,-0.019425439,-0.009306552,0.018206306,-0.0043920097,-0.0035062432,0.006134492,0.01731339,0.008513446,-0.016235806,-0.014013751,0.010015058,0.013354995,0.015031517,-0.029108228,-0.008116701,0.011655828,0.003721803,-0.00043481964,0.0070010326,-0.0039424365,-0.008988482,-0.03523005,-0.019305052,0.004454981,0.0053252648,0.030220663,-0.012458898,-0.019061636,0.007956225,-0.0047321273,-0.010389768,0.036759548,-0.011806585,0.015061348,-0.017081134,-0.0076358216,-0.013323067,0.020974126,0.033434667,-0.0011499097,-0.0030453173,-0.004574394,0.0049653794,0.002325763,-0.0045910827,-0.010926462,0.026013028,-0.0047472185,0.0059046973,0.0046947915,-0.01802814,0.0015289468,-0.019238478,0.0063720634,-0.012712003,-0.031132532,-0.01822307,-0.028715251,-0.0015912174,-0.001320894,-0.008952314,0.019637933,-0.017534653,-0.027507432,0.023887413,0.037054,-0.0032919704,-0.028695669,0.00707167,-0.0044701323,-0.061182003,0.010159165,0.020788105,-0.032690458,-0.00638665,0.04112535,0.026763402,0.013943163,-0.004854489,-0.0019047024,0.009829919,-0.011293829,0.013769301,-0.0044590365,-0.0033469712,0.0019103335,0.011338051,-0.015376545,-0.015217085,-0.012245328,-0.011682594,-0.005950479,-0.010093949,-0.0033574349,0.011334922,0.020969132,0.021226482,0.052094687,0.010366791,0.013471968,-0.018928273,-0.011516941,0.021520983,-0.013337925,-0.012352789,-0.02404475,0.016234716,-0.0024760766,-0.004937256,-0.034944214,0.03204719,0.0067624603,-0.007862236,0.011579023,-0.013009783,-0.009269358,-0.0065654055,0.0033266053,0.0113134505,0.017081743,-0.015530811,-0.0069560683,0.0093792835,-0.0041144593,0.023595696,0.031797603,0.002213821,-0.021790076,0.024636732,-0.01981128,0.010592108,-0.02459396,-0.015666502,-0.018798621,-0.032926157,-0.038784362,-0.0092742415,0.014693546,-0.0067743054,0.017981043,0.0048151696,-0.017534293,-0.017583901,-0.044804223,0.008453857,0.03371096,-0.000418618,-0.01591686,-0.019348832,0.009330527,-0.014211395,0.014864537,0.0128485365,0.0054404708,0.0114374915,-0.012786219,-0.0051876665,0.01074212,0.006974142,-0.0011813706,0.009153992,0.013243974,-0.010827496,0.025261879,-0.032067254,-0.028650435,0.015673568,0.011717233,-0.0016060261,-0.0016285798,0.008421112,-0.0016611373,0.023222215,0.009964401,0.0046204063,0.021883652,-0.0009067125,-0.021886948,-0.02787262,0.02966539,0.021558419,-0.00028458674,0.008116233,0.02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e":47,"language":48},"wembanyama-stat-page-turns-into-recap-en","Wembanyama’s 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