Soccer Analytics: The Intersection of Sports Science, Big Data, and Machine Learning

Home >> Projects >> Soccer Analytics Publications    Blog    Bio
Plot of points vs. pressure success percentage.
Collaborators: Houston Dash NWSL, Ballard FC, SportAnalisi.it
Sponsors: Self-funded, independent research, volunteer work.
I am exploring the intersection of machine learning, big data, and sports science in soccer. Quantitative methods can complement the human expertise of coaches, club owners, sports trainers, match analysts, and scouts with objective measures derived from tracking data (positions of the players on the pitch) and event data (shots, goals, save, passes). Machine and statistical learning can be used to predict outcomes from these quantitative measures, and help coaches reason about tactical alternatives and scouts assess the likelihood of success of prospective players. Finally, deep learning techniques power the automatic annotation and analysis soccer matches from digital videos.