Open-source Monocular Python HawkEye for Tennis
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Updated
Feb 14, 2024 - Python
Open-source Monocular Python HawkEye for Tennis
Daily Fantasy Sports lineup optimzer for all popular daily fantasy sports sites
Ultimate Tennis Statistics and Tennis Crystal Ball - Tennis Big Data Analysis and Prediction
网球场馆小程序:网球于19世纪中叶传入中国,从当时的极少数人打网球,到现在网球已经进入到我们普通居民的生活当中,成为现代社会人崇尚的生活方式之。本程序前后端完整代码包括场地通知,网球知识,赛事快讯,场地预约,教练预约,团课预约, 并辅以强大后台管理,可以管理预约名单,导出预约数据Excel等功能,采用腾讯提供的小程序云开发解决方案,无须服务器和域名
A Tennis dataset and models for event detection & commentary generation
A Home Assistant frontend custom card that will display real-time updates for teams tracked with the ha-teamtracker integration. Has custom in-game layouts for football, baseball, basketball, hockey, soccer, golf, tennis, racing, and mma.
Retrieves sports data from a popular sports website as well as from the NCAA website, with support for NBA, WNBA, NFL, NHL, College Football and mens and womens college basketball,
Computer vision and deep learning on tennis video
Using deep learning to perform action recognition in the sport of tennis.
Machine Learning model(specifically log-regression with stochastic gradient descent) for tennis matches prediction. Achieves accuracy of 66% on approx. 125000 matches
Predicts the winner of a tennis match with machine learning
The Tennis Serve Analysis App is a mobile application designed to revolutionize the way tennis players analyze and improve their serves. Leveraging machine learning algorithms and computer vision techniques, the app provides users with personalized feedback of their serves.
🎾 Multi-Agent Proximal Policy Optimization approach to a competitive reinforcement learning problem
Tennis match analysis via computer vision techniques.
functions to manipulate TODS-JSON documents which represent tournaments and leagues; generating draws & etc.
A simple tennis score keeper
Book Paris tennis court (include CAPTCHA bypass)
Predicting the winner of the Tennis match
Hawkeye official app repository. It contains the source code of the AI (developed with OpenCV) and the app itself (developed with t3-stack).
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