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/*增幅降幅排名*/
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) - sum(GuessPrice) as ReceivePrice,
cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) as Rate
From [game_FantasyLog]
WHERE IsJudge=1
GROUP BY UserId,UserName
ORDER BY sum(ReceivePrice) - sum(GuessPrice) ASC
/*正确率错误率排名*/
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) - sum(GuessPrice) as ReceivePrice,
cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) as Rate
From [game_FantasyLog]
WHERE IsJudge=1
GROUP BY UserId,UserName Having count(UserId) >= 5
ORDER BY cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) ASC
/*大手笔排名*/
复制代码 代码如下:
Select top 50 l.UserName,sum(l.GuessPrice),sum(l.ReceivePrice),f.title
From [game_FantasyLog] l left join [game_fantasy] f on l.topicid = f.id
GROUP BY l.TopicId,l.UserName,f.title
ORDER BY sum(l.GuessPrice) DESC
/*冷门场次排名*/
复制代码 代码如下:
Select top 50 f.id,f.title,f.GuessPrice,(select sum(receivePrice) FROM [game_FantasyLog] l where l.topicid = f.id),
cast((select sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / f.GuessTimes FROM [game_FantasyLog] l2 where l2.topicid = f.id) as numeric(4,2))
From [game_Fantasy] f WHERE f.GuessPrice > 1000
ORDER BY (select sum(receivePrice) FROM [game_FantasyLog] l where l.topicid = f.id) ASC
/*冷门场次的命中者*/
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) as ReceivePrice
From [game_FantasyLog] where topicid=29
GROUP BY TopicId,UserName
ORDER BY sum(ReceivePrice) DESC
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) - sum(GuessPrice) as ReceivePrice,
cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) as Rate
From [game_FantasyLog]
WHERE IsJudge=1
GROUP BY UserId,UserName
ORDER BY sum(ReceivePrice) - sum(GuessPrice) ASC
/*正确率错误率排名*/
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) - sum(GuessPrice) as ReceivePrice,
cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) as Rate
From [game_FantasyLog]
WHERE IsJudge=1
GROUP BY UserId,UserName Having count(UserId) >= 5
ORDER BY cast(sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / count(ReceivePrice) * 100 as numeric(4,1)) ASC
/*大手笔排名*/
复制代码 代码如下:
Select top 50 l.UserName,sum(l.GuessPrice),sum(l.ReceivePrice),f.title
From [game_FantasyLog] l left join [game_fantasy] f on l.topicid = f.id
GROUP BY l.TopicId,l.UserName,f.title
ORDER BY sum(l.GuessPrice) DESC
/*冷门场次排名*/
复制代码 代码如下:
Select top 50 f.id,f.title,f.GuessPrice,(select sum(receivePrice) FROM [game_FantasyLog] l where l.topicid = f.id),
cast((select sum(CASE WHEN ReceivePrice>0 THEN 1.0 ELSE 0 END) / f.GuessTimes FROM [game_FantasyLog] l2 where l2.topicid = f.id) as numeric(4,2))
From [game_Fantasy] f WHERE f.GuessPrice > 1000
ORDER BY (select sum(receivePrice) FROM [game_FantasyLog] l where l.topicid = f.id) ASC
/*冷门场次的命中者*/
复制代码 代码如下:
Select top 50 UserName,sum(ReceivePrice) as ReceivePrice
From [game_FantasyLog] where topicid=29
GROUP BY TopicId,UserName
ORDER BY sum(ReceivePrice) DESC
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兴国资源网 Design By www.nnzcdc.com
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RTX 5090要首发 性能要翻倍!三星展示GDDR7显存
三星在GTC上展示了专为下一代游戏GPU设计的GDDR7内存。
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