Popular GitHub Repositories for Sports Betting

Cool experiments at the intersection of Computer Vision and Sports ??

Python AutoML for Trading Systems and Sports Betting

Collection of sports betting AI tools.

Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)

Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.

Predicting soccer matches outcomes with machine learning as time series.

高性能足球数据分析工具包：抓取数据、模拟比赛、排名球队，并更明智地下注 | pena.lt/y/blog 提供支持

PyTorch code for ICPR 2020 paper "DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting"

The most largest prediction market - Polymarket copy trading bot: Copy master's trades and positions by using this bot

Using AI to predict the outcomes of NBA games.

Tennis AI to predict the winner in ATP matches with 3 different models and a web UI

TeamTrack: An Algorithm and Benchmark Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos

This project is designed to display how we can utilize deep learning methods for Sports Data Analytics.

DeepShot是一个机器学习模型，它利用高级的球队数据和滚动平均值来预测NBA比赛结果。它结合了历史表现趋势和比赛背景数据，从而提供高度准确的胜率预测（66.45%）。

[NeurIPS'25] MLLM-CompBench evaluates the comparative reasoning of MLLMs with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. CompBench covers diverse visual domains, including animals, fashion, sports, and scenes

CompBench evaluates the comparative reasoning of multimodal large language models (MLLMs) with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. CompBench covers diverse visual domains, including animals, fashion, sports, and scenes.

A data-driven approach to predicting football match outcomes using advanced machine learning techniques. This project integrates various algorithms to forecast game results, providing insights for sports betting, team performance analysis, and sports enthusiasts.

An advanced machine learning model utilizes a Random Forest Regressor to generate betting recommendations for Major League Baseball (MLB) games.

Document classification into four defined categories (World, Sports, Business, Sci/Tech). Text Pre-processing using NLTK. Trained with different models ranging from Naïve Bayes to Convolutional Neural Network (CNN) and RCNN.

Estimate power output given GPS data of a bike ride.