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An attempt to make IMDB _more_ honest (using user reviews)

Hacker News

An attempt to make IMDB _more_ honest (using user reviews)

While working on our main business SaidThat (saidthat.com) our team noticed that IMDb's star ratings consistently seemed to disagree with other major review sites (RottenTomatoes, MetaCritic, TMDb, etc). We found that IMDb only tended to match those other scores when you took the simple average of the user text review ratings as opposed to the normal star rating. To show case this we built a fun little demo site that will quickly calculate the simple average for you and then show you how "inflated" the quoted IMDb star rating is. You can do this for any movie or tv show! We fully recognize there are numerous legit reasons for these discrepancies (we wrote a whole page about it on the website honestimdb.com/about), but we will leave forming an opinion up to you. It's just interesting to note the difference seems more extreme and frequent on Amazon (who owns IMDb) originals...

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos
58%58% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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