Year
2024Credit points
10Campus offering
Prerequisites
NilIncompatible
EXSC514 Sports Analytics and Visualisation
Unit rationale, description and aim
The use of advanced techniques for data collection, storage, analysis and visualisation, to accurately interpret competition and training information, is essential when working in High Performance Sport. In addition, it is essential to be able to communicate these outcomes in meaningful ways for their implementation by athletes, coaches and support staff to optimise athlete and team performance. The unit addresses specialised statistical, coding and management principles for the collection and analysis of data in field and laboratory settings. The types of data collected in elite sport will be explored, as well as techniques and systems used in storing, analysing and visualising these data, and advanced information literacy skills for summarizing and presenting these data. The aim of the unit is to provide students with evidence-based, ethically-grounded, industry-relevant knowledge and skills in data handling, analysis 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.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Demonstrate advanced knowledge of data collection, management and coding systems used in High Performance Sport | GC1, GC2, GC7, GC9, GC10, GC11 |
| LO2 | Perform specialised technical, statistical and coding skills for analysing, summarising, and reporting data | GC1, GC2, GC7, GC8, GC9, GC10, GC11 |
| LO3 | Interpret, display and communicate data in ways appropriate to different audiences in High Performance Sport, displaying appropriate standards of ethical and technical conduct | GC1, GC2, GC7, GC8, GC9, GC10, GC11 |
Content
Topics will include:
- Organising data, displaying and reporting data
- Advanced software use (e.g., MS Excel) for data management, analysis and reporting, including pivot tables to summarise data
- Automating tasks in specific software (e.g., MS Excel) by using macros
- Data visualisation
- Basic inferential statistics (e.g., Z Scores, Probability, Confidence intervals)
- Effect size
- Magnitudes-based statistics
- Risk ratios
- Linear mixed models
- Cross correlation analysis
- Data cleaning, splitting, syncing and analyzing large data sets
- Assessing change and relationships in performance variables
- Interpretation of data for High Performance Sport practice
- Introduction to R programming
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) is utilised, so that the unit’s content and activities progress students through the learning outcomes and associated assessment tasks. Students are encouraged to contribute to asynchronous discussions. The unit has a deliberate developmental narrative, with each learning outcome and assessment aligned with a specific purpose.
Assessment strategy and rationale
ACU Online
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 is used including:
- Assessment Task 1: an examination to assess student learning of unit content;
- Assessment Task 2: an analysis task to assess the student's ability to organize, analyse and report data, and interpret its application to practice; and
- Assessment Task 3: a written task to assess the student’s ability to analyse, report and communicate data to industry-relevant audiences, displaying appropriate application of accumulated learning through the unit.
Students must achieve a cumulative grade of at least 50% across all assessments.
Overview of assessments
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
Assessment Task 1 Examination Enables students to demonstrate their understanding and application of unit content. Duration: 40 minutes | 20% | LO1, LO2 |
Assessment Task 2 Data Analysis Report: Enables students to demonstrate their application of knowledge and technical skills by analysing and reporting data, and interpreting its application to practice. Format: MS Excel / PowerBI / Tableau | 30% | LO2, LO3 |
Assessment Task 3 Coach Report: Enables students to demonstrate their application of knowledge and skills in analysing and reporting data, and their ability for effective communication. Max Word Limit: 2000 | 50% | LO1, LO2, LO3 |
Representative texts and references
Hopkins, W. (2016). A New View on Statistics. https://www.sportsci.org/resource/stats/index.html
Laha. A.K. (2019). Advances in Analytics and Applications (Laha, ED.; 1st Ed. 2019). Springer Singapore https://doi.org/10.1007/978-981-13-1208-3.
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.
Vincent W & Weir J. (2021) Statistics in Kinesiology (5th Ed.). Champaign IL: Human Kinetics.