Study level

  • Master of Philosophy
  • Honours
  • Vacation research experience scheme


Topic status

We're looking for students to study this topic.

Research centre


Professor Chris Drovandi
ARC Future Fellow
Division / Faculty
Faculty of Science
Dr Kate Saunders
Lecturer in Optimal Ecological Management
Division / Faculty
Faculty of Science

External supervisors

  • Thomas Body, Cricket Australia
  • Charles Evans, QLD Cricket


Identifying talent as early as possible in elite sport is critical. An important component of this is learning about what metrics of performance in lower grades to focus on to help predict performance in the top grade. This project will explore for this research problem for cricket.

Research activities

Students will use statistical analysis and/or machine learning to help reveal what metrics are important is translating strong performance in lower grades to strong performance in higher grades of cricket.

In addition to the QUT supervisors, students will collaborate with data scientists and cricket experts at Cricket Australia and QLD Cricket. Students will have the opportunity to spend 1-2 days per week in the QLD Cricket office.


The expected outcome of the project is a preliminary analysis to identify important metrics for translating strong performance across increasingly competitive grades of cricket.

The project can be developed further in honours or a Master of Philosophy, where the research problem could be more thoroughly explored with more sophisticated statistical and machine learning tools.

Eventually, depending on outcomes, the research could be published in a sports data science journal.

Skills and experience

  • Statistical data analysis and/or machine learning.
  • Programming skills (preferably in R or Python).
  • Interest in sports applications (desirable but not essential).



Contact the supervisor for more information.