People's Choice Award in Fall 2018
by Melony Bennis, Yiwei Fang, and Qinyi Wu
Player chemistry and ball control can easily make or break any team. With that, an emerging trend to be explored and capitalize on within the sports analytics field is using player and ball tracking data in order to improve individual and team performance. This project explores the task of applying and comparing various types of regression models from data collected from professional soccer matches. Specifically, we focus on discovering the fundamental characteristics that make the best players and cohesive teams, serving coaches and management by directly providing ratings for the UVa’s men’s team. This metric could also aid in improving existing game strategies, resulting in a greater number of season wins.