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I built an open-source analytics platform that replaces your $50/mo subscription. Self-hosted. SQLite. Deploy on a $4 VPS in 5 minutes. 📊 Real-time visitor tracking 💳 Stripe conversion tracking (no webhooks) 🤝 Built-in affiliate system 🌍 Cloudflare geo data 🗄️ Zero config database No monthly fees. No data sharing....

278,208 views • 2 months ago •via X (Twitter)

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9 repos that mass replace a $150,000/year NBA analytics department. all free. all open source. -> replaces Second Spectrum and SportVU YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. court keypoint detection builds a tactical top-down map. speed, distance, passes - all from a TV feed. no sensors. -> replaces paid NBA prediction services ($100/mo) XGBoost + Neural Net. moneyline and totals. Kelly Criterion sizing. 69% accuracy. pulls odds from FanDuel/DraftKings automatically. the most starred NBA betting repo on GitHub. -> replaces an entire quant sports desk 5-model ensemble: XGBoost + PyTorch MLP + Ridge + Lasso + baseline. Optuna-tuned hyperparameters. SQLite database with box scores, play-by-play, betting lines, injury reports. production-grade. -> replaces manual daily prediction workflows XGBoost/LightGBM with GitHub Actions automation. scrapes new data, retrains models, outputs daily win probabilities. set it and forget it. -> replaces ELO subscription services custom ELO + Ridge + XGBoost + Neural Networks ensemble. full data scraping pipeline. comprehensive visualizations. FiveThirtyEight-style ratings from scratch. -> replaces Four Factors analytics dashboards ELO rating system + Four Factors + PCA dimensionality reduction. detailed comparison of 10+ models. honest 65.3% accuracy - because that's what real NBA prediction looks like. -> replaces computer vision analytics platforms ($500/mo) YOLO player/ball tracking. automatic team assignment. court keypoints. pass and interception detection. speed and distance. full tactical view. modular architecture. -> replaces shot tracking hardware YOLOv8 detects ball and hoop in real-time. linear regression predicts trajectory. registers makes and misses automatically. works on any video feed. -> replaces paid sports data subscriptions ($300/mo) official Python client for NBA. com API. box scores, play-by-play, shot charts, player tracking. 40+ years of data. zero cost. the foundation every NBA ML project is built on. like + bookmark you'll need this when you build your first NBA prediction bot

zostaff

100,820 views • 1 month ago