EXSC517 - Visual Optimisation in High Performance Sport

Year

Credit points

Campus offering

Prerequisites

Nil

Incompatible

EXSC514 Sports Analytics and Visualisation

Teaching organisation

150 hours of focused learning

Unit rationale, description and aim

The use of advanced techniques for data storage and visualisation, to accurately interpret competition and training performance information, is essential when working in High Performance Sport. In addition, it is crucial to be able to communicate information gleaned from data analysis in meaningful ways for their implementation by athletes, coaches and support staff to optimise athlete and team performance. The unit addresses specialised coding and visualisation principles for the analysis and interpretation of data in field and laboratory settings. The types of data collected in elite sport will be explored, and the different techniques and systems used to develop optimal visualisation will be explored. The aim of the unit is to provide students with evidence-based, ethically grounded, industry relevant knowledge and skills in data handling and reporting, to effectively communicate ideas and outcomes to specialist and non-specialist stakeholders within the multiple sub disciplines found in High Performance Sport settings.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Content

Students will be taught how to visualise data in accordance with data type. This will involve theoretical components (i.e. what visualisations are effective for certain data types) as well as practical instruction (i.e. how to generate plots in R coding language) in accordance with the grammar of graphics principles.

• Visualising single or multiple distributions
• Visualising linear, non-linear univariate and multivariate relationships
• Visualising amounts
• Visualising proportions
• Visualising variability and uncertainty
• Visualising questionnaire response data
• Visualising angles
• Visualisation networks
• Visualising qualitative data
• Visualising paired comparisons
• Visualising coordinate/spatial data
• Interactive visualisation
• Illustrations
• Principle of visualisation: colour, shape and composition theory

Learning and teaching strategy and rationale

ACU Online

The learning and teaching strategy in this unit has been designed to support learning in the online environment, to meet the aim, learning outcomes and graduate attributes of the unit, and reflect respect for the individual as an independent learner. A range of approaches (e.g., active learning, web-based learning, case-based learning, reflective/critical thinking activities) are utilised, so that the unit’s content and activities progress students through the learning outcomes and associated assessment tasks. That is, the unit has a deliberate developmental narrative, with each learning outcome and assessment aligned with a specific purpose.

This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.

Assessment strategy and rationale

To best enable students to demonstrate the achievement of unit learning outcomes and develop graduate attributes, standards-based assessment is utilised, consistent with University assessment requirements. The assessment strategy in this unit has been designed to support learning as well as to assess it. It is sequenced so that the progression through the assessment matches the progression of learners through the learning outcomes. That is, it has a deliberate developmental narrative. Each assessment item is therefore also aligned with a specific purpose. A range of assessment strategies are used including: an examination to assess student learning of unit content; an analysis task to assess student’s ability to organize, analyse and report data, and interpret its application to practice; and a written task to assess student’s ability to analyse, report and communicate data to industry-relevant audiences, displaying appropriate application of accumulated learning through the unit.

Overview of assessments

Representative texts and references

Ozgur, C., Colliau, T., Rogers, G. and Hughes, Z., 2017. MatLab vs. Python vs. R. Journal of data Science, 15(3), pp.355-371.

Rivas, P., 2020. Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python. Packt Publishing Ltd.

Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. New York: Springer.

Wilke, C. 2020. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. 1st ed. CA: O’Reilly Media.