AI-Powered Leagues Scheduler Project Part 2

Numerous barriers exist to participating in sports, and a study by the Canadian Fitness and Lifestyle Research Institute has shown that 12% of respondents cited not having anyone to participate with as an obstacle (Canadian Fitness and Lifestyle Research Institute, 2013). With this project, we aim to leverage algorithms and artificial intelligence (AI) to better connect sports players, utilizing a wide array of data. Our goal is to increase the likelihood that players will remain engaged throughout the season and return for subsequent seasons. To address these challenges, we will deliver AI-Powered Leagues Scheduler Part 2. To address these challenges, AI-Powered Leagues Scheduler Part 2 delivers practical upgrades: refine tournaments with set-based scoring and rotating partners; harden a multi-sport scheduler to support varied formats, team sizes, scoring, and uneven sign-ups while producing balanced match ups with fewer conflicts. These upgrades make it easier to place players, build fair schedules, and keep people coming back. This project goes beyond day-to-day operations by creating a standardized optimization and experimentation platform—algorithms, services, and data pipelines—that permanently upgrades OpenSports’ decision-making for placements, seeding, and scheduling across all clients and sports.

Faculty Supervisor:

Kai Zhuang

Student:

Partner:

OpenSports Inc.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

York University

Program: