Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

Computer Vision app designed to analyze basketball shots from video footage using YOLOv11 finetuned with a custom dataset.

A collection of literature on the use of computational intelligence methods in sports

"Predicting Ball Location From Optical Tracking Data" - contains data analysis, model development and testing

A minimalistic toolbox for extracting features from sports activity files written in Python

Official implementation of TrackNetV5

This project was developed in Java, using the Java Swing library to create an intuitive and friendly user interface. Its main purpose is to assist in the management of statistics on volleyball matches and players, simplifying the process of collecting, analyzing and visualizing essential data for the performance of teams and athletes.

Metrics to analyze table tennis games

No description provided.

The app enables booking football fields, organizing challenges, and connecting with gyms, trainers, and sports services. It also offers reservations for swimming pools, photoshoot locations, and sports gear purchases, with order tracking and delivery. A version for venue owners and trainers

🏓 30th Place Solution for AI CUP 2025 | An machine learning project for the AI CUP 2025 (Top 3% finish). Features robust player-centric validation and domain-driven feature engineering to achieve strong generalization on private data.

A simple fitness tracker app with material you design support.

In this project, we created an 'expected goals' metric to help us assess a team's performance rather than the actual number of goals scored. We merged this metric with the calculation of a team's offensive and defensive ratings, which are updated after every game, to create a classification model that predicts the outcome of future matches, as well as a regression model that predicts the score of future games Our models outperform existing traditional techniques and achieve similar accuracy to betting sites' models.

Various machine learning approaches for soccer prediction focusing on Ensemble learning algorithms as a method to obtain the optimal prediction

Bachelor Thesis application backend

Statistical analysis of elite chess ahead of the 2020/21 FIDE Candidates

A tool designed to help users uncover trends, activity distribution, and valuable insights from anything happening on Kuyy, a platform where people can discover and join various sports activities like tennis, padel, and more. Perfect for anyone who wants quick, data-driven understanding of what’s popular and where the action is.

Desktop app for comparing F1 driver performance based on weather conditions. Includes Ktor for networking, Koin for dependency injection and Compose Multiplatform for UI.

ERA-5 climate data pipeline for WBGT index calculations (extract ZIP-.nc, merge, DataFrame export)

No description provided.

Extract video chapter timestamps and title screens using template matching and OCR - perfect for sports, podcasts, and content creation

Conducted a Machine Learning Project to predict the winner of the 2025-2026 LaLiga, Premier League, Serie A and Bundesliga Season.

Streamlit app to practice implied probability, EV, and Kelly sizing. Includes Trainer drills + CSV backtesting with bankroll curve.

[CVPRW 2023] Original code for "Visualizing Skiers' Trajectories in Monocular Videos" presented at the CVsports workshop at CVPR 2023.

A sophisticated Python application that provides real-time NFL kicker statistics and performance analysis with an intuitive graphical interface.

This repo hosts the source code of tncad articles with a private motivation. Those include Sport performance and Web analytics in Python and Scala.

An arbitrage calculator for 2 opposing odds on a bookie or bookies—on bets.

Desarrollo de una aplicación de registro de entrenamientos de tiro con arco

CLI & Python library to calculate sports bet hedging scenarios — guaranteed profit regardless of outcome

A custom Airbyte connector to fetch football data from the Football-Data.org API. It allows users to retrieve match results, league tables, and player statistics for specific leagues, making it a versatile tool for football data analysis.