Scorekeeping App (For Pickleball Users)

Hacker News

When I started playing tennis, and later did some pickleball as well, I was overwhelmed by all the things I had to remember, all at once. I needed to watch out for technique, rules of the game, where my partner and opponents are, tactical moves to make, the wind direction, how (un)cool I was looking, ... One thing I realized quite early on, is that keeping track of the score of a match, en who is serving from which side of the court was rather easy to put in an algorithm. It is easy to explain, but hard to keep track of with all other things in your mind. So as a programmer I was set out to fix this. I recently got a hold of a smartwatch (as a gift from my employer), so that was the go-to tool. So after some iterations, and scope creep all over the place, I have now released an app to take the score keeping out of your hands and onto your wrist. Let me present to you all: PickleballWithFriends. All apps are linked on my landing page http://www.pickleballwithfriends.com You can run it on Apple watch, Google wearOS watches and some Garmin activity watches as well. If you drop me some email at admin AT pickleballwithfriends DOT com with your username and telling me you found me on this site, I will give you free access to the entire thing for a year. It only is 1.99 (+taxes) anyway, but it would be nice to have some reviewing eyes and exposure. Best regards.

Visit product View on Hacker NewsFeb 13, 2025

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

86%86% 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: apple, google, apps · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, 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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, google, users · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

46%46% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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