Unit rationale, description and aim

The ability to make sound decisions in High Performance Sport is critical to maximising 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 for 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 data of relevance to sports science and athletic performance and effectively present the results.

2026 10

Campus offering

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  • Term Mode
  • ACU Term 1Online Unscheduled
  • ACU Term 3Online Unscheduled

Prerequisites

Nil

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.

Explain the importance of using appropriate statis...

Learning Outcome 01

Explain the importance of using appropriate statistical techniques for making effective decisions in High Performance Sport
Relevant Graduate Capabilities: GC1, GC2, GC7, GC8, GC9, GC10, GC11

Utilise contemporary statistical approaches to ana...

Learning Outcome 02

Utilise contemporary statistical approaches to analyse individual and group data
Relevant Graduate Capabilities: GC1, GC2, GC7, GC8, GC9, GC10, GC11

Interpret and report the outcome of statistical an...

Learning Outcome 03

Interpret and report the outcome of statistical analyses in a way that effectively communicates complex information to a variety of audiences
Relevant Graduate Capabilities: GC1, GC2, GC7, GC8, GC9, GC10, GC11

Content

Topics will include: 

  • Hypothesis Testing including p values and their limitations
  • Understanding and calculating measures of Reliability and Validity
  • Methods for determining practically important differences between groups
  • Methods for determining practically important differences in individuals
  • Advanced statistical approaches in High Performance Sport
  • Reporting and presenting outcomes to coaches and athletes 

Assessment strategy and rationale

The assessment strategy used allows for the progressive development of knowledge and skills relevant to data analysis, interpretation and communication which are necessary for the student to be able to demonstrate achievement of learning outcomes.

To become effective at interpreting and communicating data analysis results, students must first develop comprehensive knowledge, skills and understanding of the statistical approaches used to analyse and interpret de-identified data collected within a High Performance Sport environment. A range of assessment strategies are used where data analysis tasks are purposefully designed to assess student learning of unit content and its application.

The first assessment task requires students to use statistical approaches to analyse performance test data and interpret the results, being critical skills underpinning performance assessment and intervention.

The final task a “Coach Report” has been designed to assess the student's underpinning knowledge and skills in analysing and interpreting data appropriately as well as the more complex requirement of communicating the outcomes clearly and effectively to in a method required within the High Performance Sport environment.

To pass the unit, students must demonstrate achievement of every unit learning outcome, and obtain a minimum mark of 50% in graded units.

Overview of assessments

To pass the unit, students must demonstrate achievement of every unit learning outcome, and obtain a minimum mark of 50% in graded units.

Assessment 1 Data analysis and interpretation ta...

Assessment 1

Data analysis and interpretation task

Enables students to demonstrate their understanding of statistical approaches by analysing performance test data and interpreting the results. 

Weighting

40%

Learning Outcomes LO1, LO2

Assessment 2 Data analysis interpretation and co...

Assessment 2

Data analysis interpretation and coach report  

Enables students to apply skills developed in the unit for the analysis of data and communication of the outcomes. 

Weighting

60%

Learning Outcomes LO2, LO3

Learning and teaching strategy and rationale

ACU Online

This unit uses active learning, case-based learning, cooperative learning, web-based learning, and reflective/critical thinking activities. These strategies support students in their exploration of required knowledge as they develop an in-depth understanding of unit content. Opportunities are provided for application of knowledge and understanding for practical skill development in data analysis. Students are encouraged to contribute to asynchronous discussions. These strategies will allow students to meet the aim, learning outcomes and graduate capabilities 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. 

Representative texts and references

Representative texts and references

Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: Problems, prevalence, and an alternative. The Journal of Wildlife Management, 64(4), 912–923. https://doi.org/10.2307/3803199.

Buchheit, M. (2018). Magnitudes matter more than Beetroot Juice. Sport Performance & Science Reports. https://sportperfsci.com/magnitudes-matter-more-than-beetroot-juice/.

Cormack, S. J., Newton, R. U., McGulgan, M. R., & Doyle, T. L. A. (2006). Reliability of measures obtained during single and repeated countermovement jumps. International Journal of Sports Physiology and Performance, 3(2), 131–144. https://doi.org/10.1123/ijspp.3.2.131.

Dinsdale, A., & Myers, T. (2016). Interpreting statistical tests for training interventions. Professional Strength & Conditioning : The Journal of the UK Strength and Conditioning Association, 41, 7–16.

Gastin, P. B., Meyer, D., & Robinson, D. (2013). Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. Journal of Strength and Conditioning Research, 27(9), 2518–2526. https://doi.org/10.1519/JSC.0b013e31827fd600.

Hopkins, W. G. (2000). A new view of statistics. Internet Society for Sport Science. http://www.sportsci.org/resource/stats/.

Ryan, S., Pacecca, E., Tebble, J., Hocking, J., Kempton, T., & Coutts, A. J. (2020). Match running performance in the Australian Football League is improved with training. International Journal of Sports Physiology and Performance, 15(4), 457–463. https://doi.org/10.1123/ijspp.2019-0060.

Sheppard, J. M., Cormack, S., Taylor, K.-L., McGuigan, M. R., & Newton, R. U. (2008). Assessing the force-velocity characteristics of the leg extensors in well-trained athletes: The incremental load power profile. Journal of Strength and Conditioning Research, 22(4), 1320–1326. https://doi.org/10.1519/JSC.0b013e31816d671b.

Sullivan, G. M., & Feinn, R. (2012). Using effect size-or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1.

Tanner, R. K., & Gore, C. J. (Eds.). (2013). Physiological tests for elite athletes (2nd ed.). Human Kinetics.

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