Article "ばんばんざい" in Japanese Wikipedia has 42.5 points for quality (as of August 1, 2024). The article contains 76 references and 21 sections. The article also contains quality flaw template, which reduce quality score.
In July 2024 the article "ばんばんざい" was edited by 4 authors in Japanese Wikipedia (6211th place) and written by 4 authors in all languages.
Since the creation of article "ばんばんざい", its content was written by 56 registered users of Japanese Wikipedia and edited by 56 registered Wikipedia users in all languages.
The article is cited 30 times in Japanese Wikipedia and cited 30 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from August 1, 2024 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #614 01.2022 |
Global: #24634 01.2022 |
On 12 November 2024 in multilingual Wikipedia, Internet users most often read articles on the following topics: Donald Trump, Marco Rubio, Elon Musk, Song Jae-lim, 2024 United States presidential election, Megan Fox, Melania Trump, Gladiator II, ChatGPT, deaths in 2024.
In Japanese Wikipedia the most popular articles on that day were: 福田和子, 玉木雄一郎, 松山刑務所事件, 松山ホステス殺害事件, 八代亜紀, 小泉深雪, 穐田誉輝, 岩城直也, 九里亜蓮, Perfume.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from August, 2024. When calculating current popularity and AI of articles data from July 2024 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2023... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(27.59+28.36+0+67.74+100)-2.24$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
