Course name: Sport Informatics and Analytics

This course explores the links between informatics and analytics in sports contexts.

Course metrics

• Notional Learning Hours: 150 hours
• Duration: learner defined
• Assessment: formative and summative assessment of an ePortfolio.
• Formal credit option: yes
• Course: a stand alone course
• Level: Masters

What is it about?

This is an OERu course in Sport Informatics and Analytics. It is a Masters level course.

The content shared here was developed by staff at the University of Canberra, Australia, for an open, online course in February 2015. It was included in the University of Canberra’s Master of High Performance Sport for the first cohort of students in the 2015 academic year. The content of the course is updated continuously.

What will you learn?

This course explores the links between informatics and analytics in sports contexts at a time of growing interest in the observation and analysis of sport performance. The course looks at four themes. The learning outcomes for this course can be found here.

What is involved?

This course offers opportunities to follow the content in a linear or non-linear way. We acknowledge that as a learner you will make decision about your interests within the course content. We provide a range of resources to support your learning journey.

We have planned the course with Jo Ito’s observation in mind: “education is what people do to you and learning is what you do to yourself”.^[1]

You can find a course outline here.

What prerequisites?

This course is designed for people who work in sport performance environments or who aspire to do so. We anticipate that the content here might be of interest to a wider audience too for whom sport is an important part of their lives. Although we introduce some basic ideas around statistics and visualisation, no prior knowledge is anticipated in the planning of the course content.

How long does the course take?

The study time for this course is estimated to be 150 hours. However, we anticipate learners will take as long as they need to satisfy their interests in this course.

What credit options are available?

The University of Canberra offers an opportunity for students to gain a credit for this course through the submission of an electronic portfolio (ePortfolio). Information about the ePortfolio requirements for this course can be found on this page.

Course outline

There are fifteen topics in this course. These are planned within the context of four themes (T1, T2, T3 and T4). These topics can be studied as weekly activities or in much more intensive periods of study. Note that topics 5, 10 and 15 relate to the ePortfolio assessment of this course.

1. Introductions (T1).
2. Pattern recognition (T2).
3. Performance monitoring (T3).
4. Audiences and messages (T4).
5. Compile and submit Introduction ePortfolio.
6. Ethical issues (T1).
7. The quantified self (T3).
8. Using R (T2).
9. Visualising data (T4).
10. Compile and submit Development ePortfolio.
11. Feedforward (T4).
12. Communities of Practice (T1).
13. Knowledge Discovery (T2).
14. Informatics and Analytics (T1, T2, T3, T4).
15. Refine, complete and submit ePortfolio.

Learning outcomes

We anticipate that at the completion of this course, you will be able to:

• Demonstrate disciplined and critical insights into the observation, recording and analysis of performance in sport training and competition environments.

• Apply knowledge of better practice in sport informatics and analytics to your own sport contexts.

• Reflect critically on the use of sport informatics and analytics in order to anticipate and develop opportunities to transform your own and others’ performances.

For more information about these learning outcomes see this page.

ePortfolio

This course is assessed through the submission of an ePortfolio. The ePortfolio carries 100% of the marks available in this course.

For information about the ePortfolio requirements for this course see this page.

Web resources

A list of recommended and suggested readings for this course can be found here.

Reference