Predicting injury in football using pitch quality, player’s function, player’s age and match intensity: A case study of the 2017 African Cup of Nations
Football is the most popular sport in the world, and many individuals have taken advantage of it to earn a living and improve upon their standards of living. Injuries are also unfortunate incidents that occur in daily life and in sports, which affect an individual’s ability to make good use of his sporting talent to earn a living for himself and his family. In this project, modifiable risk factors that affect a player’s likelihood of getting an injury are identified, and their individual contributions to injury of a player is assessed. A predictive model for determining important risk factors for determining injuries in football is generated using the identified risk factors: pitch quality, match intensity, player function and player age.
Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Management Information Systems, April 2017
football, sports injury prediction, data mining, African Cup of Nations (AFCON)