Sports Analytics – Leveraging Raw GPS Data for Optimizing Soccer Players’ Performance
Sports Analytics – Leveraging Raw GPS Data for Optimizing Soccer Players’ Performance

Abstract: 

The ultimate goal in any sport is to win. Coaches and athletes must be striving to be able to compete at peak performance. The field of sport science is a rapidly growing field that looks to address the questions that come with trying to help athletes be at their best when it matters most. This takes a strong understanding of exercise physiology as well as data science in order to try and find answers to the questions that arise. Some questions may be how hard does an athlete need to train on a given day? Or how does a practice session compare to a game in terms of physical demand? These are the types of questions that we try to answer using HPCC Systems with our data at NC State University Strength and Conditioning. From data ingestion and processing with HPCC Systems, visualization of data, and the beginnings of predictive analysis, see how HPCC Systems can be used to optimize the information gained for raw gps data with our soccer teams and how it provides insight to support us with developing training programs to improve sport performance.

Bio: 

Christopher Connelly has a Masters in Exercise Science and Nutrition from Sacred Heart University. He has been working with NC State Strength and Conditioning for over two years where he has built a platform for athlete data monitoring in python. He has formerly worked with the NC Courage professional soccer team doing GPS and data monitoring. His graduate work was primarily in biomechanics and movement analysis where he did data analysis with 3D movement testing and electromyography. He has his CSCS certification with the NSCA as well as level 1 coach with USA weightlifting. Chris took part in the HPCC Systems summer internship program working on a project for cleaning and analysis of collegiate soccer GPS data in HPCC Systems.