Spatio-temporal analytics on soccer game data

• Main Author: Chew, Clarence Kai Wei
• Other Authors: Cheng Long
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/150351
• Institution: Nanyang Technological University

The rise of machine learning in today’s world brought about a change towards using data and artificial intelligence to improve professional football. Many teams look towards utilising such technology in order to understand their football team in a relatively new manner, giving them insightful information in the tactical aspects of footballing formations. From these data, teams are able to gain an edge over their opponent, and often this is critical in determining the match outcomes. As most technologies on football analytics are commercialized and unavailable to the public, the explores alternative ways to understand a limited set of football tracking data before converting the data into meaningful tactical information which a football team can benefit from. A web application will be developed to visualize the data with ease. The research on formation visualisation of football tracking data showed promising signs of greater understanding development towards using machine learning in the current football context.