GitHub topics: sports-league

Repositories

brownben/munro

League Results. Sorted. Sports league results calculated quick and easily, with results sorting and filtering options

Language: Python - Size: 14.4 MB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 7 - Forks: 1

lnathanreis22/LeagueBot-The-Ultimate-Discord-League-Manager

A feature-rich Discord bot for managing custom sports leagues with teams, players, stats, visual player cards, and interactive commands – all in one!

Language: Python - Size: 1.38 MB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 0 - Forks: 0

KrissaNSE/LeagueBot-The-Ultimate-Discord-League-Manager

A feature-rich Discord bot for managing custom sports leagues with teams, players, stats, visual player cards, and interactive commands – all in one!

Language: Python - Size: 1.38 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

ZIDAN44/Sports-League-Management

The project is designed to efficiently organize multiple sports leagues and It keeps track of all essential league information, match details, team data, player statistics, coaches, and match officials.

Language: Java - Size: 16 MB - Last synced at: 5 months ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

alamgir-ahosain/sports-league-system

The Sports League System is a Laravel-based web application designed to manage teams, players, matches, and standings efficiently. Built with HTML and CSS, it provides an intuitive interface for league management.

Language: Blade - Size: 15.3 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

avanish269/Baseball-Elimination-Problem

Given the standings in a sports league at some point during the season, determine which teams have been mathematically eliminated from winning their division.

Language: C++ - Size: 140 KB - Last synced at: about 1 year ago - Pushed at: about 5 years ago - Stars: 2 - Forks: 0

samyak1999/Sports-Popularity-Forecast

This project is to measure the popularity of each league using the search data collected from Google Trends, which give real-time historical data on search words. With this project, it is also possible to compare and forecast how the sports league are trending with respect to each other using three models — trend plus seasonality regression, Holt-Winters Multiplicative (HWMM), and Seasonal Autoregressive Integrated Moving Average (SARIMA). Businesses interested in advertising or investing with either league may leverage these forecasts for deciding which sports league provides the greater or long-term value.

Language: Jupyter Notebook - Size: 3.37 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 2