Mathematics Monster

Mathematics Monster ✓

YouTube @mathematicsmonster Knowledge 📍 Japan
Visit YouTube 💰 Sponsor Mathematics Mon… 🤝 Collab with Mathematics Mon… 🎤 Book Event Appearance ⚔ Compare ☆ Watch Own this channel?
Subscribers
55.4K
🔥 Buzz rank
#4,117 ▲62
Total Views
12.74M
Videos
835
Channel age
11 yrs
Est. monthly earnings
$42 – $81
How we estimate
~10.7K recent monthly views × an estimated ad RPM of $3.92–$7.56 per 1,000 views — adjusted for Knowledge content and a JP audience. Yearly ≈ $504 – $972. Excludes sponsorships, memberships & merch. Full methodology →

Known for

ベクトル４：ベクトル・図形・座標の選択①《京都大2000年》12.3K views ベクトル４：ベクトル・図形・座標の選択①《京都大2000年》 確率１：確率の本質①《京都大2012年文系》10.9K views 確率１：確率の本質①《京都大2012年文系》 ベクトル５：ベクトル・図形・座標の選択②《一橋大1986年》9.8K views ベクトル５：ベクトル・図形・座標の選択②《一橋大1986年》 ベクトル10：垂線とベクトルの成分表示・外積の利用《東京理科大》8.5K views ベクトル10：垂線とベクトルの成分表示・外積の利用《東京理科大》

▶️ Featured Video

12.3K views
ベクトル４：ベクトル・図形・座標の選択①《京都大2000年》

📈 Performance

Typical video
4.8K–6.4K views
Median 5.4K — the reliable floor a brand can expect, not just the headline average.
Upload rhythm
0.4× / week
Most active on Fridays — consistent output keeps an engaged, sponsor-ready audience.
5.7K
Avg views / video
99
Avg likes
15
Avg comments
2.02%
Engagement rate
0.4
Uploads / week
15.3K
Lifetime avg / video
230
Views / subscriber
Engagement rate = (likes + comments) ÷ views across recent videos.

💡 Did you know?

🗓️Joined YouTube in 2015 — 11 years ago.
🏆Ranked #7,691 among all influencers on SocialDB.
🎯Top Knowledge influencer — #455 in the niche.
👁️Averages 5.7K views per video.
📤Posts about 0.4× per week.
💬2.02% engagement rate.
🚀Gained 100 subscribers in the last 7 days.
📍Based in Japan.

🛡️ Audience credibility

56/100 Fair
  • Est. 73% real, active audience · Medium fake-follower risk.
  • Healthy view-through — roughly 10% of subscribers watch each video.
  • 2.02% engagement — below the ~5% typical for this size.
Est. authentic audience
73%
Fake-follower risk
Medium

Heuristic estimate from view-through, engagement vs size and recent growth — a screening signal, not a guarantee.

👥 Audience demographics

Estimated

Modeled from this creator's niche, platform & region — a planning estimate, not connected analytics.

Gender split
Male 49% Female 51%
Age · peak 18-24
13-1711%
18-2430%
25-3429%
35-4419%
45+11%
Top geographies
  • Japan52%
  • United States14%
  • United Kingdom6%
  • India5%
Likely interests
EntertainmentLifestyleTrending

💚 Brand affinity

Estimated

Brands Mathematics Monster's audience is most likely to respond to — from detected sponsorships in this niche.

Keeps Nebula Coinbase Robinhood Shopify Brilliant NordVPN Audible Incogni Squarespace

About

東京大・京都大・東工大・一橋大・早稲田大・慶応大・北海道大・東北大・筑波大・大阪大・東京医科歯科大・名古屋大・九州大・横浜国立大などの一流大学志望の受験生に向けた数学の授業です。 ホームページから体系的に全講座無料で受講できます。問題の印刷もホームページより可能です。

📸 Gallery

確率９：n数の和と倍数の確率《一橋大2013年》 確率８：n数の積と倍数の確率②《千葉大2012年》 確率７：n数の積と倍数の確率①《京都大1992年》 確率６：複合命題・ドーナツ型の確率《北海道大2008年文系》 確率５：余事象の確率《北海道大2006年》 確率４：取り出す順番と順列《北里大獣医学部2007年》 確率３：並べ方と集合《東北大1999年》 確率２：確率の本質②《一橋大2003年》

Recent videos

確率９：n数の和と倍数の確率《一橋大2013年》
1.5K views · 1 month ago
確率８：n数の積と倍数の確率②《千葉大2012年》
2.3K views · 1 month ago
確率７：n数の積と倍数の確率①《京都大1992年》
2.3K views · 2 months ago
確率６：複合命題・ドーナツ型の確率《北海道大2008年文系》
2.3K views · 3 months ago
確率５：余事象の確率《北海道大2006年》
2.7K views · 3 months ago
確率４：取り出す順番と順列《北里大獣医学部2007年》
3.3K views · 4 months ago
確率３：並べ方と集合《東北大1999年》
5.1K views · 4 months ago
確率２：確率の本質②《一橋大2003年》
4.8K views · 4 months ago
確率１：確率の本質①《京都大2012年文系》
10.9K views · 4 months ago
ベクトル24：空間ベクトルの応用④《名古屋大1996年》
4.8K views · 5 months ago
ベクトル23：空間ベクトルの応用③《東京大1993年文系》
4.1K views · 5 months ago
ベクトル22：空間ベクトルの応用②《京都大2011年》
5.2K views · 6 months ago

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

Let's Study With Me Let's Study With Me 63% math karat math karat 61% Wawa Sensei Wawa Sensei 58% KEiSoN / WEB制作 × クリエイティブ × AI の研究室 KEiSoN / WEB制作 × クリエイティブ × AI の研究室 55% MathAbyss MathAbyss 55% Universe in 60 seconds Universe in 60 seconds 55%

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