Sports Data Analytics: A Case Study of a Collegiate Women's Basketball Team
Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players’ or teams’ performance, independently or in tandem. Using Machine Learning (ML), this talk aims to holistically evaluate player, team, and conference (season) level performances in Division-1 Women’s basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score(GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep,and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. Quantifying and predicting performance at all levels will allow coaches tomonitor athlete readiness and help improve training.
Speaker
Mehul Raval
Professor
School of Engineering and Applied Science
PhD (Pune University)
Research Interests: Computer Vision, Engineering Education, Machine Learning, Remote Sensing, Sports Data Analytics, Intelligent Transportation Systems
Date: Thursday, February 8, 2024
Time: 4:00 PM - 5:00 PM
Venue: Online Via Zoom
