Basketball Play Designer

Hacker News

In a company hackathon this weekend, I thought it would be fun to build a basketball play designer. Essentially, you enter some information about the type of play you want, and it generates a play following these requirements. I was imagining it might be useful for stuff like: 1. Designing personalized plays for first-time coaches: "Give me a play that gets my shooting guard an open three-pointer. My shooting guard shoots the best from the right wing, and my point guard has a weak left hand. My center sets great screens, so have my center set an off-ball screen for my shooting guard." 2. Brainstorming novel plays for long-time coaches: "Generate a novel play out of the UCLA set." It is loaded in with an example play, so make sure to hit the buttons to generate new plays with the entered information. The animation generation is based on the play-by-play and is a bit finicky, so you can try to generate new play-by-plays/animations if the first ones don't work well. It's still a bit rough around the edges, but I'd love to know what people think!

Visit product View on Hacker NewsSep 23, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% 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: new, open · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

42%42% 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

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.

Incorrect prediction on native model

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