Study level


Master of Philosophy


Vacation research experience scheme


Science and Engineering Faculty

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.


Professor Chris Drovandi
Division / Faculty
Science and Engineering Faculty
Distinguished Professor Kerrie Mengersen
Professor of Statistics
Division / Faculty
Science and Engineering Faculty
Dr Paul Wu
Senior Lecturer in Statistical Data Science
Division / Faculty
Science and Engineering Faculty


With elite sports becoming more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.

Buoyed by the proliferation of new technologies including wearables and video analytics, data can now be collected at higher levels of precision and frequency across a broader range of categories than ever before. However, making sense of this “big data” to optimise training outcomes and competitive performance has emerged as a key challenge.

Research activities

A series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and coaches/athletes.

Methodological areas revolve around the development and application of modern Bayesian approaches for modelling complex dynamic systems. Bayesian inference provides a systematic and rigorous approach for prediction and estimation of real world problems under uncertainty.

The projects involve:

  • QUT
  • the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS)
  • Centre for Data Science
  • Queensland Academy of Sport
  • Australian Institute of Sport
  • sports organisations and teams.

Research areas include:

Efficient computation, inference and learning of state space models (Dynamic Bayesian Networks) for possibly non-homogeneous systems

This can include the human body in the context of adapting to training and  the susceptibility for injury.

Learning and inference with such models whilst incorporating highly heterogeneous data collected at potentially different frequencies and scales is challenging.

This data includes performance, training, injury data. This can be linked to many other influential factors including physiological, psychological, nutrition and sleep.

Learning and inference of models with intractable likelihoods in sports

There are many problems in sports where it's impractical or infeasible to formulate a likelihood. This might arise due to complex interactions between players in a game or complex physiological-psychological-environmental processes.

Simulation models, such as Agent Based Modelling (ABM), could be used to simulate potential scenarios (e.g. athletes racing in a triathlon, plays being run in basketball) but estimation of ABM parameters are challenging.

One potential solution is the use of approaches that combine simulation with Bayesian inference (e.g. Approximate Bayesian Computation).


The outcomes here are not only academic (e.g. reports, papers, conference presentations), but also include industry engagement and communication, with eventual uptake by the Australian sporting community.

Previous work in swimming was taken up by the national swimming body in preparation for the 2019 World Championships.

Skills and experience

In general, strong quantitative, coding and communication skills are needed. The level of required skills will depend on your study level.

For honours and postgraduate research, the following units may be helpful:

For more information about required skills and experience, please contact the supervisors.


You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round



Contact the supervisor for more information.