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Hooper – AI-driven stats and highlights for basketball play

Hacker News

Hooper – AI-driven stats and highlights for basketball play

Hey everyone, OP here. Wanted to share a bit more about Hooper — I started building it with a good friend of mine six months ago. We play a lot of pickup together and were arguing about who has a better jump shot and ended up hacking together an app to settle it The way Hooper works is you can record yourself using the app and ideally a tripod (optional). The app will track everyone, whether its a solo practice, a 3v3, or a 5v5. We think there’s a lot of stuff out there for basketball drills but what we really wanted Hooper to be for is actual game play. That means, it can do things like track multiple players, sync two half court recordings, and differentiate 2s vs 3s. Once you finish recording, it’ll process for a bit and then ask you to tag yourself (and optionally other players). Then it spits back out a few things: you can watch the full footage as well as a clipped version that's only the interesting plays; you can see highlights and offensive box stats for every player. When you sign up, you get a Hooper profile that tracks your overall stats across the sessions. You can also do things like build a mixtape from your highlights for insta. You can friend other players on Hooper to see their profile & comment on their games (also add us “grub” and “kangexpress”!) We’ve been in a closed beta for about 3 months now, fixing bugs and getting things to work with a set of early adopters. We are now starting our open beta ! If anyone here wants to try it out, you can just download the app on https://www.hooper.gg/?utm_campaign=h1 . We are early in our journey, and lots of improvements to be made in the next few months, but we would love your feedback and ideas!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: recordings, using, open · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

Correct prediction on native model

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