EXSC317 - Data Analytics in Sport

Year

Credit points

Campus offering

Prerequisites

EXSC224 Mechanical Bases of Exercise and EXSC122 Research and Ethics in Exercise Science

Unit rationale, description and aim

The use of appropriate techniques for data collection, storage, analysis and visualisation to accurately interpret competition and training information, and the communication of these outcomes in ways that can be implemented by athletes, coaches and support staff to optimise sporting performance, is essential for a professional working in high performance sport. This unit addresses 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 ways of summarizing and presenting these data to stakeholders. The unit aims to provide students with ethically-grounded, industry-relevant knowledge and skills in data handling, analysis and reporting, and the ability to interpret and present findings in a meaningful way to a variety of audiences.

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.

On successful completion of this unit, students should be able to:

LO1 - Demonstrate knowledge of data collection, management and coding systems used in high performance sport (GA5)

LO2 - Apply technical, statistical and coding skills for analysing, summarizing and reporting data (GA5, GA8) 

LO3 - Interpret, display and communicate data in ways appropriate to different audiences, displaying appropriate standards of ethical and technical conduct (G5, GA8, GA9, GA10) 

Graduate attributes

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA9 - demonstrate effective communication in oral and written English language and visual media 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include: 

• Organising data, displaying and reporting data 
• MS Excel for data management, analysis and reporting 
• Basic inferential statistics 
• Z Scores 
• Probability 
• Confidence intervals 
• Effect size 
• Magnitudes-based statistics 
• Risk ratios 
• Assessing change and relationships in performance variables  
• Data visualisation 
• Interpretation of data for high performance sport practice 
• Pivot tables to summarise data 
• Automating tasks in MS Excel using macros 
• R programming – introduction, importing data 

Learning and teaching strategy and rationale

Learning and teaching strategies include active learning, web-based learning, case-based learning, and reflective/critical thinking activities, which will be delivered online over 12 weeks. This range of strategies will provide students with appropriate access to required knowledge and understanding of unit content. These strategies will allow students to meet the aim, learning outcomes and graduate attributes of the unit, as well as professional practice standards. 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 within group activities and tutorial sessions. 

The unit is delivered in three distinct online modalities. Firstly, recorded brief lectures containing the foundational knowledge of inferential statistics specific to sport and exercise are provided. This foundational knowledge is assessed using an online theoretical exam. Secondly, recorded practical modules are provided that build on the foundational knowledge to develop students’ practical data analysis and visualisation skills. Student are required to demonstrate that they have completed each of these modules in an online workbook. Thirdly, online activities are provided to the students to develop specific skills required to complete the graded assessment tasks for the unit.  

Assessment strategy and rationale

In order to best enable students to demonstrate unit learning outcomes and develop graduate attributes, standards-based assessment is utilised, consistent with University assessment requirements. 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

Hopkins W. (2016). New View on Statistics. http://www.sportsci.org/resource/stats/index.html 

Vincent W and Weir J. (2012) Statistics in Kinesiology (4th Ed.). Champaign IL: Human Kinetics.