New Weekly Fantasy Football Site

Hacker News

Hey HN, We're looking for feedback on our weekly fantasy football site, www.firstroundfantasy.com. I'm not sure if there are too many fantasy sports fans here but I figured it couldn't hurt to try. From a technical standpoint, I developed the site on Google App Engine in Java and a fair bit of JS on the front end. The site itself is different from most weekly fantasy formats in that users can keep players on their roster for as long as they want. In most weekly formats, players are drafted prior to each contest. Because players can be maintained indefinitely, the best players in the league cost money (up to $10). For example, you can buy Peyton Manning for $10 and after doing so, you can use him as much as you want - even into next season. We're not funded, if that matters, and I am the only dev on the team so far. I am also an iOS dev and if this site gets any traction, an iOS app will be soon to follow. Let me know what you think and if you have any questions, technical or otherwise. Thanks! PS - the site is NOT mobile optimized so please look at it on your desktop/laptop. If you're on a phone and go to the site, go easy on us. We're working on the mobile version.

Visit product View on Hacker NewsNov 6, 2013

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

81%81% 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: google, user, new · Missing: mac, agents, macos

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

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

49%49% predicted probability of success on TrustMRR, 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

39%39% 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 · Strong signals: soon, users · Missing: plus, platform, intuitive

38%38% 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

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.

Correct prediction on native model

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