Year
2024Credit points
5Campus offering
No unit offerings are currently available for this unitPrerequisites
Nil
Incompatible
EXSC513 Data Analysis and Interpretation for High Performance Sport
Unit rationale, description and aim
The ability to make sound decisions in high performance sport is critical to maximizing performance outcomes. In order to do this, practitioners need specific knowledge and skills in data analysis techniques in addition to the ability to present data in a meaningful way to a variety of audiences. This unit is based on contemporary data analysis techniques focusing on determining practically meaningful differences in athletic performance. A range of approaches will be explored to allow analysis of both individual and group data. The aim of this unit is to provide students with the knowledge, understanding and skills to analyse and interpret sports science data and effectively present the results.
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 |
|---|---|
| LO1 | Understand the importance of using appropriate statistical techniques for making effective decisions in high performance sport. |
| LO2 | Utilise contemporary statistical approaches to analyse individual and group data. |
Content
Topics will include:
- Underlying concepts in data analysis
- Hypothesis Testing
- Probability and p-values
- Measures of reliability and validity
- Methods for analysing group and individual athlete data
- Uncertainty and Confidence Intervals
- Percent differences
- Effect size
- Practical interpretation of data analysis
Learning and teaching strategy and rationale
Learning and teaching strategies include active learning, case-based learning, cooperative learning, and reflective/critical thinking activities. These strategies will provide students with access to required knowledge and understanding of unit content, and opportunities for application of knowledge and understanding for practical skill development in data analysis. These strategies will allow students to meet the aims, learning outcomes and graduate attributes of this learning package. Learning and teaching strategies will reflect respect for the individual as an independent learner. Students will be expected to take responsibility for their learning and to participate actively in the online environment.
Assessment strategy and rationale
In order to best enable students to achieve unit learning outcomes and develop graduate attributes, standards-based assessment is utilised, consistent with University assessment requirements. A two-part assessment strategy is used including a data analysis task to assess student learning of unit content and its application. The assessment tasks for this unit are designed for you to demonstrate your achievement of each learning outcome.
Overview of assessments
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
Data analysis and Interpretation- Part A Enables students to demonstrate their understanding of key concepts in data analysis by analysing performance test data. | 40% | LO1, LO2 |
Data analysis and Interpretation - Part B Enables students to demonstrate their understanding of contemporary data analysis approaches by analysing performance test data. | 60% | LO1, LO2 |
Representative texts and references
Hopkins, W.G. A New View of Statistics. [Web page] 2000. Available from: http://www.sportsci.org/resource/stats/procmixed.html/#indif.
Kyprianou, E., Lolli, L., Haddad, H. A., Di Salvo, V., Varley, M. C., Mendez Villanueva, A., ... & Weston, M. (2019). A novel approach to assessing validity in sports performance research: integrating expert practitioner opinion into the statistical analysis. Science and Medicine in Football, 3(4), 333-338.
Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA's statement on p-values: context, process, and purpose, The American Statistician, DOI: 10.1080/00031305.2016.1154108