Profitable iOS Football Predictions App – $10.8K Annual Profit, Zero Ad Spend

Acquire.com

Unlock the opportunity to own a high-margin, subscription-based mobile app in the growing sports predictions niche. This iOS app delivers accurate, AI-driven football match predictions sourced from an advanced API, factoring in team form, player stats, and historical performance. Designed for accessibility and ease of use, the app offers two free daily predictions, with premium users enjoying unlimited access via monthly or yearly subscriptions. With zero ad reliance, the experience remains ...

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Actual performance

Did not reach leaderboard

Traction signals

Asking price$30,000

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

94%94% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, month, monthly · Missing: mobile apps, personal, entrepreneurs

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

36%36% predicted probability of success on AppSumo, 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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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