Year
2024Credit points
5Campus offering
No unit offerings are currently available for this unitPrerequisites
UNMC591 Making Informed Decisions Using Data in High Performance Sport: Key Concepts and Applications
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 | Utilise contemporary statistical approaches to analyse individual and group data |
| LO2 | Interpret and report the outcome of statistical analyses in a way that effectively communicates complex information to a variety of audiences |
Content
Topics will include:
- Methods for analysing group and individual athlete data
- Bayesian Analysis in High Performance Sport
- One-sided Hypothesis Tests
- Introduction to Statistical Modelling in High Performance Sport
- Practical interpretation of data analysis
- Reporting and Communicating Results of Data Analysis to Coaches & Athletes
Learning and teaching strategy and rationale
Learning and teaching strategies include active learning, case-based learning, cooperative learning, web-based learning, and reflective/critical thinking activities, delivered across 12 weeks. 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 aim, learning outcomes and graduate attributes of the unit. 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 “Coach Report” to assess the ability to interpret data appropriately and communicate the outcomes clearly and effectively. The assessment tasks for this unit are designed for you to demonstrate your achievement of each learning outcome. 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 apply skills developed in the unit for the analysis of performance test data | 50% | LO1, LO2 |
Data analysis interpretation and coach report - Part B Enables students to apply skills developed in the unit for the communication of the outcomes of Part A. | 50% | LO1, LO2 |
Representative texts and references
Blume JD, D'Agostino McGowan L, Dupont WD, Greevy RA, Jr. (2018) Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses. PLoS ONE 13(3): e0188299. https://doi.org/10.1371/journal.pone.0188299.
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259-269.
Quintana, D.S., Williams, D.R. Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP. BMC Psychiatry 18, 178 (2018). https://doi.org/10.1186/s12888-018-1761-4.