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We drew attention to twenty-four ePortfolio questions in this unit. There were six questions for each of the themes:
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?
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?
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?
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?
The fourth theme in this course considered the ways in which messages are shared with a range of audiences. We:
Our discussions about the visualisation of data included this quote from Maria Popova[1] 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’[2].
The third theme in this course was Performance Monitoring. In this theme we:
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.
In the Pattern Recognition theme (T2), we considered:
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 [1] 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.
This is an OERu course that has been designed to contribute to the community service and outreach mission of the OERu.
In the Introductions theme (T1), we:
The Audiences and Messages (T4) contained within it a discussion of personal learning.
One of our topics within the Introductions theme was Communities of Practice. We mentioned that Etienne Wenger[1][2] 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:
One of the issues that arises from your engagement with this cours 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.
This page brings together the course content as a capstone page for the Sport Informatics and Analytics course. It 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 scaffold for the course.
By the end of your online activity, we hope you have:
In the Introduction to this course, we anticipated that at the completion of this course, you would be able to:
For more information about these learning outcomes see this page.
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.
This page brings together the course content as a capstone page for the Sport Informatics and Analytics course. It 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 scaffold for the course.
By the end of your online activity, we hope you have:
In the Introduction to this course, we anticipated that at the completion of this course, you would be able to:
For more information about these learning outcomes see this page.
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.
We present two examples here for your consideration.
Chris Anderson and David Sally discuss the potential of an analytics approach to association football in their study of The Numbers Game[1].
In the introduction to their book, they write:
The clue to analytics is in the name. To make (those) numbers mean something, to learn something from them, they must be analysed. The key, for those at the vanguard of what some have called a data ‘revolution and what we think of as football’s reformation, is to work out what they need to be counting, and to discover why, exactly, what they are counting counts.[2]
Their book explores the analytics process and raises important empirical and methodological issues for this unit.
Read the introductory chapter in The Numbers Game, Football for Sceptics – The Counter)s) Reformation .
Does their suggestion resonate with your experience of sport?
A storm is gathering in football. It is one that will wash away old certainties and change the game we know and love. It will be a game we view more analytically, more scientifically, where we do not accept what we have always been taught, but where we always ask why. The game will look the same, but the way we think about it will be almost unrecognizable[3].
The second example presented here is the paper written in 1997 by Inderpal Bhandari and his colleagues at the IBM TJ Watson Research Center. The paper is titled Advanced Scout: Data Mining and Knowledge Discovery in NBA Data. In the paper, they report their analysis of data gathered by a software program, Advanced Scout, that “seeks out and discovers interesting patterns in game data”[4]. We have chosen this paper to connect with the spirit of the literature of the time. The editor of the journal within which the paper was accepted was Gregory Piatetsky-Shapiro.
Read the paper here.
This topic explores how we can extract useful information and actionable insights from sport data.
There has been a variety of labels used to characterise processes that extract of useful information from data. These include “data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing”[1].
Gregory Piatetsky-Shapiro [2] introduced the term “knowledge discovery” in a report of a workshop in 1989 that brought together practitioners from “expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning and statistics”[3]. The report of the workshop concluded “knowledge discovery in databases is an idea whose time has come”[4].
William Frawley, Gregory Piatetsky-Shapiro, and Christopher Matheus [5] provided one of the earliest overviews of knowledge discovery in databases in 1992. They defined knowledge discovery in databases (KDD) as:
Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Given a set of facts (data) F, a language L, and some measure of certainty C, we define a pattern as a statement S in L that describes relationships among a subset Fs of F with a certainty c, such that S is simpler (in some sense) than the enumeration of all facts in Fs. A pattern that is interesting (according to a user-imposed interest measure) and certain enough (again according to the user’s criteria)is called knowledge. The output of a program that monitors the set of facts in a database and produces patterns in this sense is discovered knowledge.[6]
They added “Patterns are interesting when they are novel, useful, and non-trivial to compute”[7].
In 1996, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth discussed “an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases”[8]. Their paper distinguishes KDD from data mining. They note:
In our view, KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data[9].
They argue that KDD is a process and data mining is a step within that process. The derivation of useful knowledge from data requires:
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth provide the conceptual and practical foundation for the the KDD process in sport contexts. They propose:
KDD focuses on the overall process of knowledge discovery from data, including how the data are stored and accessed, how algorithms can be scaled to massive data sets and still run efficiently, how results can be interpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported[11].
Twenty years after the publication of their paper there is still a tendency to regard data mining and KDD as interchangeable terms. During this unit we have used the term analytics as a shorthand for KDD.
Our discussion of analytics used this definition:
The discovery, communication, and implementation of actionable insights derived from structured information in order to improve the quality of decisions and performance in an organization.
As we develop our KDD skills this activity will include unstructured data too. Whatever is included, it will be part of a process that the literature of the 1990s foresaw.
This topic explores how we can extract useful information and actionable insights from sport data.
There has been a variety of labels used to characterise processes that extract of useful information from data. These include “data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing”[1].
Gregory Piatetsky-Shapiro [2] introduced the term “knowledge discovery” in a report of a workshop in 1989 that brought together practitioners from “expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning and statistics”[3]. The report of the workshop concluded “knowledge discovery in databases is an idea whose time has come”[4].
William Frawley, Gregory Piatetsky-Shapiro, and Christopher Matheus [5] provided one of the earliest overviews of knowledge discovery in databases in 1992. They defined knowledge discovery in databases (KDD) as:
Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Given a set of facts (data) F, a language L, and some measure of certainty C, we define a pattern as a statement S in L that describes relationships among a subset Fs of F with a certainty c, such that S is simpler (in some sense) than the enumeration of all facts in Fs. A pattern that is interesting (according to a user-imposed interest measure) and certain enough (again according to the user’s criteria)is called knowledge. The output of a program that monitors the set of facts in a database and produces patterns in this sense is discovered knowledge.[6]
They added “Patterns are interesting when they are novel, useful, and non-trivial to compute”[7].
In 1996, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth discussed “an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases”[8]. Their paper distinguishes KDD from data mining. They note:
In our view, KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data[9].
They argue that KDD is a process and data mining is a step within that process. The derivation of useful knowledge from data requires:
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth provide the conceptual and practical foundation for the the KDD process in sport contexts. They propose:
KDD focuses on the overall process of knowledge discovery from data, including how the data are stored and accessed, how algorithms can be scaled to massive data sets and still run efficiently, how results can be interpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported[11].
Twenty years after the publication of their paper there is still a tendency to regard data mining and KDD as interchangeable terms. During this unit we have used the term analytics as a shorthand for KDD.
Our discussion of analytics used this definition:
The discovery, communication, and implementation of actionable insights derived from structured information in order to improve the quality of decisions and performance in an organization.
As we develop our KDD skills this activity will include unstructured data too. Whatever is included, it will be part of a process that the literature of the 1990s foresaw.
Bill Johnston[1] has outlined the characteristics of thriving online communities. These include:
If you do look at Bill Johnston’s article, do you think his characteristics can be applied to all communities of practice? Are they limited to online presence? How might stewardship occur in non-online settings?
There is a growing interest in the roles communities of practice can play in sport. For example, a University of Ottawa research group has developed a strong interest in these communities.
Diane Culver and Pierre Trudel[1] provided an overview of communities of practice in sport. Their paper stimulated debate about the concept of a community of practice that is summarised here.
Diane Culver and Pierre Trudel joined with Penny Werthner to provide a longitudinal study of a coaches’ community of practice in a youth baseball league.[2]. Rachael Bertram[3] has provided more detail about communities of practice in sport settings in her 2016 Ph.D thesis.
Elsewhere, John Stoszkowski and Dave Collins[4] have reported on the use of online blogs to structure and support informal coach learning. This paper will be of particular interest to anyone who is considering the submission of an ePortfolio for the assessment of this unit. A second paper, co-authored with Cliff Olson offers “insight into student coaches’ perceptions of their use and experiences of structured group blogging for reflection and learning”[5].
Bettina Callary[6] has reported on the creation of a community of practice within a figure skating club. She proposes that her case study:
paves the way to understanding that a CoP can be developed and sustained by coaches when they are in an environment where collaborative coaching and learning is the norm and where coaches entering into the system expect it[7].
If you have an opportunity to look at some of the literature on communities of practice, can you write a reflection about the potential of such communities to support personal learning?
A theme that pervades this course is that we live in a world of connections. Although we might not always be aware of it, we share interests with others and together we have the opportunity to form a community.
Zygmunt Bauman[1] has explored the characteristics of a community. He suggests:
Words have meanings: some words, however, also have a ‘feel’. The word ‘community’ is one of them. It feels good: whatever the word ‘community’ may mean, it is good ‘to have a community’, ‘to be in a community’[2]
Etienne Wenger[3][4] has discussed the benefits of communities of practice for learning communities. He proposes:
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.
His definition of communities of practice has three important characteristics:
Etienne Wenger, Nancy White and John Smith[6] have discussed how such communities might flourish in digital habitats. They identify the role technology stewards can play in such flourishing. Technology stewards are:
people with enough experience of the workings of a community to understand its technology needs, and enough experience with or interests in technology to take leadership in addressing those needs.[7]
A theme that pervades this course is that we live in a world of connections. Although we might not always be aware of it, we share interests with others and together we have the opportunity to form a community.
Zygmunt Bauman[1] has explored the characteristics of a community. He suggests:
Words have meanings: some words, however, also have a ‘feel’. The word ‘community’ is one of them. It feels good: whatever the word ‘community’ may mean, it is good ‘to have a community’, ‘to be in a community’[2]
Etienne Wenger[3][4] has discussed the benefits of communities of practice for learning communities. He proposes:
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.
His definition of communities of practice has three important characteristics:
Etienne Wenger, Nancy White and John Smith[6] have discussed how such communities might flourish in digital habitats. They identify the role technology stewards can play in such flourishing. Technology stewards are:
people with enough experience of the workings of a community to understand its technology needs, and enough experience with or interests in technology to take leadership in addressing those needs.[7]
Have a look at the discussion of feedback and feedforward here. In what ways do you think feedforward differs from feedback?
In Peter Dowrick’s work, feedforward uses video to model behaviour. His Ph.D research led him to define self-modeling as:
the behavioral change that results from the repeated observation of oneself on videotapes that show only desired target behaviors.[1]
He researched the potential of video self-modeling for four decades. Keith Lyons[2][3] has provided a review of Peter Dowrick’s research.
Feedforward need not be restricted to video self-modeling. Peter Dowrick’s review of self modeling[4] noted:
The most rapid learning by humans can be achieved by mental simulations of future events, based on reconfigured preexisting component skills. These reconsiderations of learning from the future, emphasizing learning from oneself, have coincided with developments in neurocognitive theories of mirror neurons and mental time travel.
This ‘learning from oneself’ does raise important pedagogical issues that can be overlooked if a focus is placed solely on feedback. It continues a discussion started by Richard Schmidt[5] about augmented information.
The use of feedforward on physical education and sport settings has the potential to transform learning environments. Cojanu Florin[6] and Harrison Kingston[7] provide examples of how feedforward might impact on explorations of learning.
This topic has been included in this course to explore how feedforward might offer an alternative way to share messages with audiences.
Peter Dowrick[1] has led the discussion of feedforward (‘learning forward’[2]) in sport settings.
This topic has been included in this course to explore how feedforward might offer an alternative way to share messages with audiences.
Peter Dowrick[1] has led the discussion of feedforward (‘learning forward’[2]) in sport settings.
As you explore the visualisation options you have, you might like to follow up on some of these links
How might these kinds of approaches inform your work?
The mind map for this topic shares a variety of links to data visualisation and storytelling.
The tools we have to share our data with audiences are available in open formats and commercial products. (An example of the use of an open format was shared in the case study of R in this unit.)
Trina Chiasson, Dyanna Gregory and their colleagues[1] have provided a comprehensive guide to preparing and visualising information. They have shared the source code for their work on Github. They note:
Data come in all different shapes, sizes, and flavors. There’s no one-size-fits-all solution to collecting, understanding, and visualizing information. Some people spend years studying the topic through statistics, mathematics, design, and computer science. And many people want a bit of extra help getting started.
.
The awareness that “no-one-size-fits-all” has led to some fascinating discussions about the aesthetics of visualisation. Stephen Few[2], David McCandless[3], Alberto Cairo[4][5], Gregor Aisch[6] and Giorgia Lupi[7], amongst others, have explored the ways in which we visualise data stories.
There is more information about visualisation discussions here. An updating list of readings in visualisation can be found here.
At some point in the sport analytics process we share our findings with an audience.
Wolfgang Iser[1] suggested that
when we produce a story to share we should think carefully about how we construct the story and imagine the recipients of the story. He notes that any story has “a network of response-inviting structures” that enable the reader or the listener “to grasp the text”.
The availability of video platforms has extended the reach of such stories.
More recently, Maria Popova[2] has looked at the impact digital platforms have had on the way data are shared. She notes that at “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.
At some point in the sport analytics process we share our findings with an audience.
Wolfgang Iser[1] suggested that
when we produce a story to share we should think carefully about how we construct the story and imagine the recipients of the story. He notes that any story has “a network of response-inviting structures” that enable the reader or the listener “to grasp the text”.
The availability of video platforms has extended the reach of such stories.
More recently, Maria Popova[2] has looked at the impact digital platforms have had on the way data are shared. She notes that at “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.
In 2015, Mladen Jovanović [1] analysed the data shared in the Pattern Recognition introduction in this unit. He cleaned the data, produced a new .csv file and shared his analysis of the data. He provides a step by step guide that uses a variety of visualisations to bring raw data file to life.
You can find information about people using R in sport contexts here. Note the use of Github to share data and code. See, for example Scottish Hill Races data. A more recent example is Andy Field’s analysis of a football referee’s behaviour.
You can find a variety of resources that introduce R here. See, for example, the list of manuals shared by the R Development Core Team that includes a guide to use R for statistical analysis and graphics. There is also a link to a comprehensive set of guides to using R prepared by R-bloggers.
This topic develops issues raised in Theme 2 of this course Pattern Recognition.
R is a programming language and a software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.[1]
Kurt Hornik and Friedrich Leisch[2] introduce R in the first edition of the R Newsletter. The R Core Team provide a brief background report about R in the newsletter[3]
You can find a detailed description of R on this Wikipedia page.
This topic develops issues raised in Theme 2 of this course Pattern Recognition.
R is a programming language and a software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.[1]
Kurt Hornik and Friedrich Leisch[2] introduce R in the first edition of the R Newsletter. The R Core Team provide a brief background report about R in the newsletter[3]
You can find a detailed description of R on this Wikipedia page.
The Wikipedia page on the Quantified Self provides a detailed overview of the emergence and development of the Quantified Self movement.
It includes references to Gary Wolf. His overview [1] of the emergence of technologies to make self-tracking more accessible includes this observation:
In the past, the methods of quantitative assessment were laborious and arcane. You had to take measurements manually and record them in a log; you had to enter data into spreadsheets and perform operations using unfriendly software; you had to build graphs to tease understanding out of the numbers. Now much of the data-gathering can be automated, and the record-keeping and analysis can be delegated to a host of simple Web apps. The makes it possible to know oneself in a new way.
Kevin Kelly[2] suggested in his preliminary discussion of the Quantified Self:
Unless something can be measured, it cannot be improved. So we are on a quest to collect as many personal tools that will assist us in quantifiable measurement of ourselves. We welcome tools that help us see and understand bodies and minds so that we can figure out what humans are here for.
Some contributors to the discussions about the Quantified Self have characterised self-tracking as Personal Informatics.[3][4][5]
Deborah Lupton[6][7] has explored the interface of critical social research and human-computer interaction (HCI) in personal informatics.
A body of literature has now been established of research that has sought to investigate the social, cultural and political dimensions of self-tracking, nearly all of which has come out in the last few years. This literature complements an established literature in human-computer interaction research (HCI), first into lifelogging and then into self-tracking (or personal informatics/analytics, as HCI researchers often call it).
Ben Williamson[8] has used the term ‘algorithmic skin’ to discuss “the ways that health-tracking produces a body encased in an ‘algorithmic skin’, connected to a wider ‘networked cognitive system’”.
Deborah Lupton and Ben Williamson are examples of a reflexive approach to “data-led and algorithmically mediated understandings of the body”[9]
This course looks explicitly at ethical issues related to monitoring and surveillance. Andrew Manley and Shaun Williams [10] observe
Coaches often voice a humanistic approach to their practice; however, with an increased reliance on surveillance and data capture, it may be that we are visibly witnessing the caricature of the elite coach morphing into the modern-day technocrat engineer. And while it can appear to drive performance, at such an unrelenting pace it may not be sustainable in the long run”.
Do you share their concerns?
Carla Dellasera, Yong Gao and Lynda Ransdell[1] have provided a narrative qualitative review of “IT’s emerging impact in sport settings”. Their paper reviews thirty-nine publications and has eighty-three references. As part of your investigation of the quantified self, can you read this paper and reflect on the benefits of finding an authoritative meta review? Are there any issues in learning about research activity without accessing the primary source?
Technological innovation has made it increasingly possible to monitor performance (other-tracking) in unobtrusive ways. The growth in personal fitness and well-being devices has made it possible to track one’s own performance (self-tracking). Shona Halson (2014) provides an overview of a range of approaches that combine other and self tracking measures of training load. This overview contributes to the discussion of the use made in quantifying performance of objective and subjective measures of well-being [1].
Some of the diverse resources to inform our discussions about the Quantified Self can be found in this mind map.
This topic develops issues raised in Theme 3 of this course Performance Monitoring. You can find some links to recommended and suggested reading on this topic here.
There are two sub-topics discussed here.
This topic develops issues raised in Theme 3 of this course Performance Monitoring. You can find some links to recommended and suggested reading on this topic here.
There are two sub-topics discussed here.
If you are compiling an ePortfolio for this course, it is likely that you will be addressing some of these ethical issues in your own practice. You might want to consider this activity as a trigger for your own reflections about how we observe and monitor performance in training and competition.
Innovations in monitoring technologies are giving rise to what Robin James [1] refers to as “contemporary algorithmic culture”.
What are your thoughts on the interface between the overt monitoring of performance and the privacy that each individual might expect in a digital world?
Perhaps this 20 minute video presentation by Maciej Ceglowski might be a good place to start.
The study of informatics and analytics offers opportunities to explore on our shared epistemic culture.
Karin Cestina[1] says of an epistemic culture:
Everyone knows what science is about: it is about knowledge, the ‘objective’ and perhaps ‘true’ representation of the world as it really is. The problem is that no one is quite sure how scientists and other experts arrive at this knowledge. The notion of epistemic culture is designed to capture these interiorised processes of knowledge creation. It refers to those sets of practices, arrangements and mechanisms bound together by necessity, affinity and historical coincidence which, in a given area of professional expertise, make up how we know what we know. Epistemic cultures are cultures of creating and warranting knowledge.
She adds that “the focus in an epistemic culture approach is on the construction of the machineries of knowledge construction” [2] (our emphasis).
In this topic, there are three examples of the ethical issues raised by sport informatics and analytics: philosophy; socio-cultural; and pegadagogy.
This course offers opportunities to reflect on some of the ethical issues raised by the use of informatics and analytics in sport.
One of the three aims that guide this course is “To contribute to discussions about the epistemological foundations of sport informatics and analytics and the flourishing of ethical practice in the observation, recording and analysis of performance in play, games and sport”.
This is a time of rapid expansion of informatics and analytics as fields of study and as practical activities. We hope that discussions about the quantification of performance will lead to reflections about the ethical framework within which we will work as our digital world is transformed by technological innovation.
In this topic you will have the opportunity to consider:
This course offers opportunities to reflect on some of the ethical issues raised by the use of informatics and analytics in sport.
One of the three aims that guide this course is “To contribute to discussions about the epistemological foundations of sport informatics and analytics and the flourishing of ethical practice in the observation, recording and analysis of performance in play, games and sport”.
This is a time of rapid expansion of informatics and analytics as fields of study and as practical activities. We hope that discussions about the quantification of performance will lead to reflections about the ethical framework within which we will work as our digital world is transformed by technological innovation.
In this topic you will have the opportunity to consider:
As you work your way through this theme and compile your ePortfolio, you might like to consider these six questions.
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?
In this video, Carrie Graf, former Coach of the University of Canberra Capitals, and the University of Canberra Coach in Residence in 2015, discusses how she shares information with her teams.
Theme 4 of this course discusses explicitly the audiences for sport informatics and analytics and the messages shared with them. This theme:
Theme 4 of this course discusses explicitly the audiences for sport informatics and analytics and the messages shared with them. This theme:
As you work your way through this theme and compile your ePortfolio, you might like to consider these six questions.
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?
In this video, Jocelyn Mara discusses how she monitors athlete performance. Jocelyn is a graduate of the University of Canberra and was a Teaching Fellow in the Department of Sport and Exercise at the University in 2015. She has been a postgraduate scholar in performance analysis at the Australian Institute of Sport. Her PhD research included working with the Canberra United football team.
Performance monitoring is included as Theme 3 in this course. This is a time of rapid change on the quantification of one’s own as well as others’ performances.
This theme:
Performance monitoring is included as Theme 3 in this course. This is a time of rapid change on the quantification of one’s own as well as others’ performances.
This theme:
As you work your way through this theme and compile your ePortfolio, you might like to consider these six questions.
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?
Take some time to explore the range of resources for this theme. You might like to start with a summary of five papers on pattern recognition.
Background
An Australian Rules football team shared with us a whole game GPS data set from a game played in the 2014 season.
The data can be found here.
The team that provided the data won the game and scored the same number of points each of the four quarters of the game.
The scores by quarter in the game were:
Questions
The resources to support this theme include:
In this video, Melissa Breen discusses the impact of pattern recognition data on her performance as an elite athlete. Melissa was the University of Canberra’s first athlete in residence in 2014.
The conceptualisation and operationalisation of pattern recognition are foundations of sport informatics and analytics. This theme (theme 2 of the course):
We present a data set for you to analyse in this theme.
The conceptualisation and operationalisation of pattern recognition are foundations of sport informatics and analytics. This theme (theme 2 of the course):
We present a data set for you to analyse in this theme.
As you work your way through this theme you might like to consider these six questions.
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?
The theme overview provides a framework to our approach to Sport Informatics and Analytics.
There is a Google Slides presentation.
There is a mind map for this theme.
There is more background information about Informatics and Analytics on this Google Site.
There are some Video suggestions. (See slides 2-4)
Daniel Link’s (2009) presentation Interdisciplinarity in Sport Informatics.
There are some additional resources here.
This is the first theme in our course. We are keen to share with you our approach to open sharing and introduce some of the ideas central to a mind shift from “Not invented here” to “proudly borrowed from there“[1].
This theme:
This is the first theme in our course. We are keen to share with you our approach to open sharing and introduce some of the ideas central to a mind shift from “Not invented here” to “proudly borrowed from there“[1].
This theme:
This is the first theme in our course. We are keen to share with you our approach to open sharing and introduce some of the ideas central to a mind shift from “Not invented here” to “proudly borrowed from there“[1].
This theme:
List links to web-resources here.
If you choose to compile an ePortfolio for this course, you might find these questions helpful in reflecting on your learning pathway. There are six questions for each of the four themes in the course.
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?
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?
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?
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?
The course is offered in two modes or learner pathways.
The first pathway shares the content as a personal learning opportunity. This mode is not assessed and is learner-directed.
The second pathway is assessed through the submission of an electronic portfolio (ePortfolio). This requires registration with the University of Canberra. Note that whilst this pathway is also learner-directed, there are some important formative assessment points in the course.
There is more information about the assessment for this course here.
The aim of this OERu course is to explore the intersection of informatics and analytics in sport contexts.
We anticipate that at the completion of this course, you will be able to:
This theme introduces you to informatics and analytics and locates them within the study of sport performance. It discusses a connectivist approach to learning in an open, online course. For more information about T1 see this page.
This theme explores a range of options available to you in the analysis of sport performance. It includes a discussion of the use of R in data analysis. For more information about T2 see this page.
This theme explores the development and use of technologies to monitor performance. It discusses ethical issues relating to the quantification of the self. For more information about T3 see this page.
This theme considers some of the issues relating to the visualisation and sharing of data. It includes a discussion of digital storytelling. There is a discussion of the use of feedforward to share data with a range of audiences. For more information about T4 see this page.
Course name: Sport Informatics and Analytics
This course explores the links between informatics and analytics in sports contexts.
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.
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.
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.
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.
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.
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.
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.
We anticipate that at the completion of this course, you will be able to:
For more information about these learning outcomes see this page.
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.
A list of recommended and suggested readings for this course can be found here.
Course name: Sport Informatics and Analytics
This course explores the links between informatics and analytics in sports contexts.
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.
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.
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.
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.
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.
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.
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.
We anticipate that at the completion of this course, you will be able to:
For more information about these learning outcomes see this page.
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.
A list of recommended and suggested readings for this course can be found here.
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