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110 sats \ 9 replies \ @siggy47 30 Jul 2023 \ parent \ on: Daily discussion thread
I think we have 4 or 5 committed to join. We're not ambitious enough to even attempt trustless, so someone will be holding the kitty. It's really an experiment. We'll keep the buy in low and just make it a bragging rights thing for the first season. It will be fun. If there's interest (possible sports sub?) in the future maybe we'll get some coding help and do it right.
I was thinking we should just create a survivor pool account. Sats go to the account for entry into the pool. Account provides list of weekly games and then an update after the week's games are over.
Survivor account pays the winner at the end. Any additional sats it got tipped along the way can either go to whoever is administering the account or we can just split amongst whoever participated.
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I had the same idea! Will you do the honors?
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Sure, I don't mind managing it. I would like you input on the rules, buy in, re-buys, tie breakers, deadlines and any other input from your league that may help it run smooth.
Checking notifications today I see we have a few more folks interested. Would be nice if we could get at least 10.
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Great. I will consult with my friend, who's been in the pool for 30 years.
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I have a go library to use the (so far, not documented) SN API which I have written for @hn and you could use
I can enhance it or provide libraries in other languages if you want :)
Not sure how much of a tech person you are though. But it may make some things easier if they are automated.
I would love to join in the pool but I have no idea about sports, haha
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I wish I understood what I read when I clicked that link. Haha. I really need to learn some shit but I run out of patience so quickly when I try to learn to code. Maybe AI assistants will be good for me.
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Are there any sports fans out there who can code, or are these mutually exclusive groups?
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Over the years, I've leveraged a variety of technologies to support my endeavors in the realm of sports betting programming. Python has been my trusty workhorse for most of the data processing, thanks to its simplicity and the robustness of data manipulation libraries like pandas and NumPy.
One of my favorite inventions is a system I call "Beat the Spread" (BTS for short). It's a dynamic AI model that adapts to changes in teams' performance over the season, and factors in various other elements like players' health, morale, weather, and even the subtleties of home field advantage. The final version of BTS uses a stacked ensemble of models, including Random Forests, Support Vector Machines, and Gradient Boosting Machines, with a neural network at the top to combine their predictions.
Recognizing the need for real-time updates, I developed a live tracking system called "GameFlow". It's built on a fast, scalable stream processing platform that gathers data from multiple sources during the game, processes it in real time, and fine-tunes the predictions.
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I like experiments!
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