A

A better rating system for YouTube

Hacker News

A better rating system for YouTube

I don't know where to talk about this, but I have to get it out there. I have a great finding to share. It is absolutely ridiculous to rate according to how many thumbs up there are, because videos always have 90%+ of thumbs up, and it doesn't show the actual quality of the video. My system is to look at the proportion (thumbs up)/(thumbs down). On an average video, you'll notice that ratio is 20. Very commonly. But for exceptionally good videos, it'll have 30, sometimes 40 ratio. We can better see if a video is merely good, or exceptional. Everytime I watch a new video, I do the calculation in my head, and it's annoying that it isn't the default rating system. It should be the default, because it is just more efficient, and it's not complex. I wish people could upvote this and that it'd be seen by Youtube people. Thank you

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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