Article “おサイフいっぱいクイズ! QQQのQ” in Japanese Wikipedia has 11.5 points for quality (as of July 1, 2025). The article contains 3 references and 12 sections. The article also contains templates indicating quality issues, therefore its score was reduced by 0.61 points.
Since the creation of article “おサイフいっぱいクイズ! QQQのQ”, its content was written by 44 registered users of Japanese Wikipedia and edited by 44 registered Wikipedia users in all languages.
The article is cited 110 times in Japanese Wikipedia and cited 110 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 July 1, 2025 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #30173 04.2014 |
Global: #331465 04.2014 |
| Best Rank | Japanese: #1137 07.2004 |
Global: #32213 07.2004 |
On 9 December 2025 in multilingual Wikipedia, Internet users most often read articles on the following topics: Stranger Things, 2026 FIFA World Cup, Lando Norris, Millie Bobby Brown, Sean Combs, It: Welcome to Derry, ChatGPT, Zootopia 2, deaths in 2025, Cassie Ventura.
In Japanese Wikipedia the most popular articles on that day were: 堂上隼人, 濱田太貴, 辰見鴻之介, 中村獅童 (2代目), 松浦慶斗, 井上広大, 知野直人, 平沼翔太, 佐藤直樹 (野球), 濱将乃介.
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 July, 2025. When calculating current popularity and AI of articles data from June 2025 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2025... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(10.08+1.08+0+38.71+10.76)-0.61$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
