I Was There, an iOS app for tracking your attended MLB games

Hacker News

Hey HN, here to show off something for the baseball enthusiasts among us. I attend a lot of games, and I would always wonder things like "who have I seen hit the most homers?" and "who have I seen throw the most strikeouts?" and other questions along those lines. Instead of just making a spreadsheet or something just for myself, I decided it'd be a fun idea for an app, and now here we are. You open the app and do two things: - Select your favorite team - Select the games you've attended from the Schedule and from there, you can start exploring your stats. You can see overall and team-specific stat rollups, which ballparks you have and have not attended, sortable player-level tables, and highlights for the most extraordinary moments you've witnessed. It's my first app I've developed from scratch and taken all the way to launching, so I'm pretty excited! I'm trying to keep it simple and low overhead, so it's free with no ads or in-app purchases or anything, and no account is required or even supported. CloudKit integration is available for saving your data. One caveat is that there's currently no data for the active MLB season. I am sourcing the data from Retrosheet, who updates with the previous season's data in January, so for now I will be following the same cadence. Please give it a try and let me know how it goes!

Visit product View on Hacker NewsAug 21, 2025

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

91%91% 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: open · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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: ios, way · Missing: mobile apps, personal, entrepreneurs

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

20%20% 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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