{"repo":"kyleskom/NBA-Machine-Learning-Sports-Betting","free":true,"listed":false,"github":"https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting","clone":"git clone https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting.git","description":"NBA sports betting using machine learning","language":"Python","stars":1693,"topics":["python","tensorflow","keras","nba","nba-analytics","nba-prediction","sports-betting","sports","sports-analytics","data-science"],"license":null,"category":"machine-learning","readme_excerpt":"NBA Sports Betting Using Machine Learning Overview This project predicts NBA game winners and totals (over/under) using team stats and sportsbook odds. It pulls team data from 2007-08 through the current season, builds matchup features, and runs trained models to estimate win probabilities and totals outcomes. It also outputs expected value and optional Kelly Criterion stake sizing. Features - Moneyline and totals predictions (XGBoost and Neural Net models). - Expected value calculation and optional Kelly Criterion sizing. - Odds ingest from supported sportsbooks or manual input. - Data processing pipeline and model training scripts. - Flask web app for browsing outputs. How it works 1. Collect stats and odds : Get Data pulls daily team stats from NBA endpoints and stores them in SQLite. Get Odds Data pulls sportsbook odds and scores from SBR and stores them in a separate SQLite DB. 2. Build game features : Create Games merges team stats, odds, scores, and days-rest into a training dataset. 3. Train models : XGBoost/NN scripts in src/Train-Models fit moneyline and totals models. 4. Predict today : main.py fetches today’s schedule, builds matchup features, loads trained models, and prints predictions, expected value, and optional Kelly Criterion sizing. Requirements - Python 3.11 - Packages: Tensorflow, XGBoost, NumPy, Pandas, Colorama, Tqdm, Requests, Scikit-learn Install dependencies: Quick start Odds will be fetched automatically when -odds is provided. Supported books: fan","default_branch":null,"files":null,"tree":[],"storefront":"/r/kyleskom","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/kyleskom/NBA-Machine-Learning-Sports-Betting/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}