Sport Informatics and Analytics/Capstone

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This page brings together the course content as a capstone page for the Sport Informatics and Analytics course. It provides an opportunity to reflect on the epistemic culture[1] of the subject field.

The aim of this OERu course has been to explore the intersection of informatics and analytics in sport contexts. We hope you have had an opportunity to visit the four themes that form the framework for the course.

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Your learning pathway in this course

By the end of your online activity, we hope you have:

  • Thought about your personal learning journey in a course that is founded on connectivist principles.
  • Engaged in some pattern recognition activities.
  • Explored diverse approaches to performance monitoring.
  • Considered how you might use augmented information with a range of audiences.

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Learning outcomes

In the Introduction to this course, we anticipated that at the completion of this course, you would 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.

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If you are contemplating the submission of an ePortfolio of your work for credit, we hope you have found sufficient stimulus to reflect on your learning experiences.

Connecting learners with open resources

This is an OERu course that has been designed to contribute to the community service and outreach mission of the OERu.

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In the Introductions theme (T1), we:

  • Explored our approach to open sharing.
  • Introduced people, perspectives, products and processes.
  • Drew attention to the Informatik tradition and its links with sport informatics.
  • Discussed the emergence of sport analytics.

The Audiences and Messages (T4) contained within it a discussion of personal learning.

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Communities of practice

One of our topics within the Introductions theme was Communities of Practice. We mentioned that Etienne Wenger[2][3] has discussed the benefits of communities of practice for learning communities. We shared this quotation:

Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.

We noted too that his definition of communities of practice has three important characteristics:

  • A domain that has an identity defined by a shared interest.
  • A community in which "members engage in joint activities and discussions, help each other, and share information. They build relationships that enable them to learn from each other.
  • A shared practice that is developed through "a shared repertoire of resources: experiences, stories, tools, ways of addressing recurring problems.[4]

One of the issues that arises from your engagement with this course is how you might become part of a vibrant community interested in and possibly passionate about sport informatics and analytics. Sharing ePortfolio content and alerting others through the use of a # such as #UCSIA16 (see also #UCSIA15) is a good way to do this.

Pattern recognition

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In the Pattern recognition theme (T2), we considered:

  • Systematic observation of performance.
  • Supervised learning approaches to data analysis.
  • Connections between performance trends and athlete actions.
  • Use of open source tools such as R to analyse performance and visualise data.

We shared GPS data from an Australian Rules Football team's performance in a competitive game in order to explore the potential of such data to provide interesting and actionable insights. We presented Mladen Jovanović's [5] analysis of the data set as a case study.

This theme included a topic on knowledge discovery in databases (KDD) and used two examples from the sport literature to explore the practice of KDD.

Monitoring performance

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The third theme in this course was Performance Monitoring. In this theme we:

  • Presented some background information about performance monitoring.
  • Discussed the development of wearable technologies to monitor performance.
  • Explored some examples of motion and video tracking technologies.

We took the opportunity to look at the quantification of personal performance ( the quantified self). This gave us the opportunity to consider some of the ethical issues involved in monitoring and surveillance including a discussion about anonymity and confidentiality of data.

Audiences and messages

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The fourth theme in this course considered the ways in which messages are shared with a range of audiences. We:

  • Discussed augmented information in general and feedforward in particular.
  • Explored the visualisation of data.
  • Invited you to think about personal learning environments, and how you might use an ePortfolio to share your formative and summative reflections about the course and your own learning.

Our discussions about the visualisation of data included this quote from Maria Popova[6] about "the intersection of art and algorithm":

Ultimately, data visualization is more than complex software or the prettying up of spreadsheets. It's not innovation for the sake of innovation. It's about the most ancient of social rituals: storytelling. It's about telling the story locked in the data differently, more engagingly, in a way that draws us in, makes our eyes open a little wider and our jaw drop ever so slightly. And as we process it, it can sometimes change our perspective altogether.

We noted the importance of storytelling in the sharing of sport analytics data. We included feedforward in our discussions and included a topic to explore the conceptual and practical implications of 'learning forward'[7].

Twenty-four questions

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Your ePortfolio

We drew attention to twenty-four ePortfolio questions in this unit. There were six questions for each of the themes:

Theme 1 ( Introductions)

Q1. What are your thoughts about a non-linear course in which you are the driver of your own learning pathway?

Q2. How might we help you to connect with others on the course?

Q3. Do you have any resources you would like to recommend for inclusion in the course?

Q4. Is there a difference between Informatik and Informatics?

Q5. What distinguishes Sport Analytics from Sport Informatics?

Q6. What is the relationship between Informatik, Informatics, Analytics and Performance Analysis?

Theme 2 ( Pattern recognition)

Q 7. What is systematic about ‘systematic’ observation?

Q 8. Do we need to concern ourselves about the reliability and validity of data?

Q 9. Why is it important to de-identify performance data?

Q10. What did you discover in the shared dataset?

Q11. What have you learned about supervised learning approaches?

Q12. What are your thoughts about how we relate patterns of performance to moments of performance within games?

Theme 3 ( Performance monitoring)

Q13. What aspects of this topic are of particular interest to you?

Q14. What criteria would you use to decide which wearable technologies to use?

Q15. Were there any aspects of the work of universities, centres, and sport you found informative?

Q16. Have you used any video tracking technology or video monitoring data?

Q17. Do you have any concerns about the validity and reliability of data gathered from wearable technologies or video tracking?

Q18. Are there any ethical issues involved in monitoring data collected in the ways discussed in this theme?

Theme 4 ( Audiences and messages)

Q19. Is ‘augmented information’ a helpful description of the ways you share information?

Q20. Does ‘feedforward’ have any place in your work?

Q21. Do you have any experience of using infographics?

Q22. Are there any visualisation approaches that you recommend?

Q23. Is the concept of a personal learning environment helpful in your practice?

Q24. Can you visualise your personal learning environment?

Thank you

We hope you have enjoyed the content of this OERu course. This is a wiki and the content is updated and changed continuously. The bottom of each page indicates when the page was last modified.

We would be delighted to receive any thoughts you have about the course and any content you think we should include.

Thank you for finding us.

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Contexts for your work

We have a final prompt to share with you as you reflect on the course and your practice. In 1983, Donald Schon wrote:

In the varied topography of professional practice, there is a high, hard ground overlooking a swamp. On the high ground, manageable problems lend themselves to solutions through the use of research­ based theory and technique. In the swampy lowlands, problems are messy and confusing and incapable of technical solution. The irony of this situation is that the problems of the high ground tend to be relatively unimportant to individuals or society at large, however great their technical interest may be, while in the swamp lie the problems of greatest human concern. The practitioner is confronted with a choice. Shall he remain on the high ground where he can solve relatively unimportant problems according to his standards of rigor, or shall he descend to the swamp of important problems where he cannot be rigorous in any way he knows how to describe.[8]

We wondered if this topography of professional practice might be of interest to you as you define your role as an analyst.


  1. Cestina, Karen (1999). "Culture in global knowledge societies: knowledge cultures and epistemic cultures". p. 363.
  2. Wenger, Etienne (1998). Communities of practice: learning, meaning, and identity. Cambridge: Cambridge University Press.
  3. Wenger, Etienne (June 2006). "Communities of practice a brief introduction". Retrieved 29 February 2016.
  4. Wenger, Etienne (June 2006). "Communities of practice a brief introduction". Retrieved 29 February 2016.
  5. Jovanović, Mladen (13 March 2015). "AFL Data Analysis Report". Retrieved 26 March 2016.
  6. Popova, Maria (12 August 2009). "Data Visualization: Stories for the Information Age". Retrieved 27 February 2016.
  7. Couros, George (23 February 2016). Retrieved 29 February 2016.
  8. Schon, Donald (1983). The Reflective Practitioner. New York: Basic Books. p. 42.