ぴくとはうす
@p1kt
Gaming2.5K
Video Count1.2B
Video View1.3M
Subscriber#663
Japan Rank#43,568
Global RankGeneral and Gaming ranking performance
General
Global
Japan
Gaming
Global
Japan
Total Videos
Content Library
Total Views
Lifetime Views
Subscribers
Community Size
Engagement Rate
Audience Interaction
Top performer by views
怖すぎて滅 #マイクラ
1.8M
Views
34K
Likes
127
Comments
Opportunity for growth
『大量のぴくと』を追加してドロップ3000倍の世界でサバイバル【マイクラリレー2025】
0
Views
265
Likes
22
Comments
Analysis of 43 long-form videos
公開1日で削除されたホラーMODがヤバすぎる【マインクラフト】
• Long videos make up 86% of total content
• Average performance: Lower than channel average
• Most common duration: Long (10-30 min)
Upload patterns and optimal timing insights
Content distribution across 9 main categories
Key performance indicators and trends
Video Game Culture dominates with 49% of content
Role-playing Video Game has highest avg views
Action-adventure Game shows strongest growth
Most used tags across 372 tag instances
15 tags in category
Comprehensive tag metrics and ROI analysis
| Tag | Usage | Avg Views | Engagement | Growth | ROI Score |
|---|---|---|---|---|---|
| #ぴくとTop 1 | 50 | 130.3K | 2.94% | -53% | 31/100 |
| #ぴくとはうすTop 2 | 50 | 130.3K | 2.94% | -53% | 31/100 |
| #ゲーム実況Top 3 | 48 | 131K | 2.73% | -53% | 29/100 |
| #ゲーム | 48 | 131K | 2.73% | -53% | 29/100 |
| #実況 | 28 | 167.7K | 2.73% | -64% | 29/100 |
| #ピクトハウス | 28 | 167.7K | 2.73% | -64% | 29/100 |
| #ピクト | 28 | 167.7K | 2.73% | -64% | 29/100 |
| #マイクラ | 21 | 172.4K | 2.79% | -65% | 30/100 |
| #マインクラフト | 19 | 181.9K | 2.80% | -67% | 30/100 |
| #mod | 14 | 83.1K | 3.76% | -27% | 38/100 |
| #広告 | 13 | 101.5K | 2.27% | +0% | 24/100 |
| #セルサバイバー | 8 | 114.9K | 2.03% | +0% | 21/100 |
| #ホラー | 7 | 124K | 3.26% | +0% | 34/100 |
| #寿司ハウス | 5 | 36.6K | 4.71% | +0% | 47/100 |
| #the sushi house | 5 | 36.6K | 4.71% | +0% | 47/100 |
Strategic recommendations and performance highlights
Top 5 by view count
Top 5 by like count
Top 5 by comment count
Top 5 by engagement rate
Peak performance metrics across all categories
Top performers across multiple categories
Performance analysis and recommendations