An iOS App for Crowdsourcing Fantasy Football Advice

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Hey everyone. Last year me and a friend started a service for crowd-sourcing fantasy football matchups (called Who Do I Start? for NFL Fantasy Football Leagues). The season went alright. I got the idea on opening kickoff last year, took a few days to think things through, then we hacked together a prototype and had it on the App Store for week 4 of the regular season. We ended the season with about 10 000 users, 25 000 matchups, and 270 000 votes on those matchups. Yesterday was the start of the 2014 season, and in the past 3 weeks we've doubled our userbase. We've currently got 1100 matchups created (since yesterday), and already have 90 000 votes. It's looking like we're going to far eclipse what we did last year. We ran into a bunch of scalability issues yesterday, and have since moved to Linode. Things seem to be working much better. We ended up being 3rd in accuracy results unofficially, according to one of the leaders in the market: http://www.fantasypros.com/nfl/accuracy/#2013 (our accuracy was 60.70%). App Store link: https://itunes.apple.com/us/app/who-do-i-start-for-fantasy/id715044633 PS: We've heard of some users on iOS 8 having issues, so forgive us if that's your case!

Visit product View on Hacker NewsSep 3, 2014

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, 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.

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

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

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

30%30% 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 · 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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