FantasyDawg – Your Fantasy Football League Recap

Hacker News

I'm a huge sports fan and long-time fantasy football player. I recently launched FantasyDawg, a custom Year in Review website for your Sleeper league! Simply input your Sleeper League ID or username, and we'll generate a custom dashboard packed with fun stats, analytics, and visualizations about your season. (If your league doesn't use Sleeper, let me know which platform you use). Also, give the "Season Wrapped" feature a try. We'll generate a graphic, Spotify Wrapped-style, to easily share with your leaguemates or on social media! My goal is to build _the_ Analytics Hub for the fantasy football community - a tool that adds some fun to your season, for any platform, league type, or scoring system. If there's anything you've ever wished you could add to your fantasy experience, let me know! Check it out at https://www.fantasydawg.com

Visit product View on Hacker NewsDec 31, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

66%66% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on Product Hunt, 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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

15%15% 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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