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Products that do what DeepShot – NBA game predictor with 71% accuracy using ML and stats does
Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee:…
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Introducing DeepShot: An NBA Game Prediction Model Hey devs, sports fans, and data nerds! After weeks of work, I'm excited to share DeepShot – an advanced NBA game predictor powered by historical data from Basketball Reference, machine learning, and a clean NiceGUI-powered web interface. What it does: DeepShot uses team-level rolling averages (including Exponentially Weighted Moving Averages) and an Elo rating system to accurately predict NBA game outcomes. All predictions are visualized in real time through a sleek, responsive UI. Key Features: Data-Driven Predictions using past performance…
2025 · github.com
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Hey everyone, OP here. Wanted to share a bit more about Hooper — I started building it with a good friend of mine six months ago. We play a lot of pickup together and were arguing about who has a better jump shot and ended up hacking together an app to settle it The way Hooper works is you can record yourself using the app and ideally a tripod (optional). The app will track everyone, whether its a solo practice, a 3v3, or a 5v5. We think there’s a lot of stuff out there for basketball drills but what we really wanted Hooper to be for is actual game play. That means, it can do things like…
2024 · hooper.gg
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2019 · github.com
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2016 · github.com
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Jul 2026 · papers.ssrn.com
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I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com
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Hello! So, I am experimenting with new approaches to automated machine learning, where you don't need anything besides either data or prompt This is a first platform from my auto ml serie called Łukasiewicz (Jan Łukasiewicz was a logician who created Polish notation) I much appreciate your feedback and if you want to try it out, I created a -75% off promo code "HACKERNEWS" on credits, so it hopefully will cover at least some of computing expenses, but more importantly will let you play with the platform at almost no cost Thank you!
2024 · lukasiewicz.tmlc.pl
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2015 · sandbox.github.io
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2020 · github.com
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Your reels are mid. We will tell you exactly why.
Mar 2026
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I created a bot which analyses Euro 2024 betting odds and performs some basic analysis based on the recent form of each team and provides advice on which odds are under/over-priced by email to all subscribers. The intelligence of the bot is admittedly rather basic but this simple app shows how easy it is to reliably scrape and parse structured data with AI and perform insightful analysis. The initial version of the bot, shown in the notebook took about 15 minutes to build. Disclosure: this bot is built on our new graph based #nocode platform NodeScript.dev, which we've recently brought…
2024 · notebooks.nodescript.dev
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