Us

Using bots and maths to beat the sportsbooks

Hacker News

Using bots and maths to beat the sportsbooks

I have recently developed a bot capable of simultaneously scanning over 140+ sportsbooks around the globe. It analyzes millions of odds in real time and uses this data to find overpriced odds. The logic behind it is straightforward: it uses odds from all sportsbooks to calculate the probabilities of an event occurring. Then, with simple maths it determines if the expected value is positive, meaning the potential payout exceeds the risk. Unfortunately, sportsbooks limit your accounts once they realize that you are profitable and not a losing customer. So I have already been limited at more than 15 sportsbooks, but in the process I have made a substantial amount of money. Some months closing at over $5,000 worth of profit. The bot supports most sportsbooks from the US, UK, EU, Canada and Latin America and finds hundreds of these opportunities per day and sends them to a Discord channel. Beyond finding +EV bets (positive expected value), it can also identify arbitrage opportunities (surebets), allowing us to bet on all the outcomes of an event for a guaranteed profit. I’d love to hear your thoughts about it!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
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.
TrustMRRFits verified-revenue profile · Strong signals: month, profitable · Missing: mobile apps, ios, personal
57%57% 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
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
45%45% 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: using · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, profitable · 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 · Strong signals: real time · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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