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Benchmarking elite athletes using multilevel models

Study level

Honours

Faculty/Lead unit

Science and Engineering Faculty

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Supervisors

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

Overview

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 benchmarking the performance of elite athletes such as rowers, swimmers and cyclists, using training and race data. Benchmarking is critical for evaluating the progress an athlete is making and their competitiveness with peers.

Multilevel modelling and related methods provide a structure for capturing hierarchies and dependencies such as athletes within a team and training sessions within a program.

Research activities

Research activities for the project 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.

Outcomes

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 the use of software packages such as R and MATLAB.

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

Keywords

Contact

Contact the supervisor for more information.