Longitudinal modelling and analysis of elite swimming performance

Study level


Topic status

We're looking for students to study this topic.


Associate Professor Chris Drovandi
Associate Professor
Division / Faculty
Science and Engineering Faculty
Distinguished Professor Kerrie Mengersen
Australian Laureate Fellow
Division / Faculty
Science and Engineering Faculty
Dr Steven Psaltis
Postdoctoral Research Fellow
Division / Faculty
Science and Engineering Faculty
Dr Helen Thompson
Senior Lecturer in Statistics
Division / Faculty
Science and Engineering Faculty
Dr Paul Wu
Lecturer in Statistical Data Science
Division / Faculty
Science and Engineering Faculty


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

For the 2018 Commonwealth Games on the Gold Coast, 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 end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport (QAS).

This project focuses on longitudinal analysis of swimming data, including race analysis over the course of a season and benchmarking of swimmer anaerobic performance. Such analysis provides feedback for athletes and coaches to optimise their training.

Research activities

Research activities in this project will include:

  • exploring the literature and learn about sports statistics modelling and analysis
  • engaging with industry partners (QAS) to learn about practical sports perspectives and data
  • implementation and analysis of models in a statistical software package
  • report writing and presentation.


Expected outcomes of this project include a final data analysis report and a presentation.

If extended into an Honours project, a journal article could arise from this work.

Skills and experience

You should:

  • have strong capabilities in statistical modelling and statistical programming
  • be comfortable with using software packages like R and MATLAB.

Background knowledge from the unit MXB241 Probability and Stochastic Modelling 2, MXB242 Regression and Design and MXB341 Statistical Inference is desirable but not necessary.



Contact the supervisor for more information.