Coordinators: David Sumpter ([email protected]), Solomon Gizaw
TinyML is emerging as a rapidly growing field of machine learning, with the potential to bring about a new era of AI implementation by leveraging big data analytics. Applied on measurement devices placed in shoes, worn on the body or placed in sporting equipment such as balls, TinyML can be used to understand and predict the movements of athletes.
In addition, computer imaging can be used to track players in team sports. And long-term records of, for example, running times during training and competition for long distance runners give data on development of performance. We will work together with African teams and athletes to develop an AI driven approach to performance.
This will allow us to identify talent and allow us to look at key differences and similarities between, for example, European and African football. We will also develop an African football analytics hub, starting with a one week intensive workshop in Ethiopia, and then continuing with a football analytics community, where data scientists work together to collect and analyze data. Similar efforts will be applied in long distance running, and then, in the longer term, other sports.