Audiences and Messages

Introduction

At some point in the sport analytics process we share our findings with an audience.

Wolfgang Iser (1978) 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". Bobby Kasthuri (2017) provides an example of how a message might be shared with five different audiences.

In the introduction to their e-book on Data and Design (2014), Trina Chiasson and her colleagues at Infoactive note Maria Popova's (2009) description of data visualisation at "the intersection of art and algorithm". Maria adds:

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 this topic we look at two approaches to sharing stories that draw others in and open their eyes a little wider:

Feedforward
Visualisation

Inevitably this sharing connects with our personal learning environments.

Each of these topics is covered in detail in this mindmap.

Feedforward

There is a substantial literature about the role of feedback in sport performance contexts. (See, for example: Troy Moles, Alex Auberach & Trent Petrie, 2017; Israel Halperin et al, 2016; Emma Kidman, Matthew D'Souza & Surya Singh, 2016; John van der Kamp et al, 2015; Roland Sigrist et al, 2013; Georg Rauter et al, 2013; Dana Maslovat and Ian Franks, 2008; Gabrielle Wulf, 2007; Gabriele Wulf et al, 2002; Richard Schmidt and Gabrielle Wolf, 1997; Douglas Young and Richard Schmidt, 1992; Richard Schmidt, 1991.)

There is comparatively little written about feedforward. Peter Dowrick has led the discussion of feedforward since the publication of his PhD thesis in 1976 (Self modelling: A videotape training technique for disturbed and disabled children).

A summary blog post (Lyons, 2014) links Peter Dowrick's work on feedforward and self-modelling to recent research in neurocognitive theories of mirror neurons and mental time travel.

In a 2012 paper, Peter Dowrick observes:

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.

Lisa Moody's (2014) thesis is titled 'The Effects of Feedforward Self-modeling on Self-efficacy, Music Performance Anxiety, and Music Performance in Anxious Adolescent Musicians'. Her supervisor was Diane Ste-Marie. One of Diane's papers (2011) discusses self-modeling in gymnastics.

We hope that you will explore the potential of feedforward as you consider why, how and when you share your analytics story with your implicit reader, viewer or listener. You might, in the process, engage in reflection on the feedforward potential of neural networks (Hornik, Stinchcombe & White, 1989).

Visualisation

The availability of computer generated, interactive, graphical representations of data is transforming the ways to share stories with audiences.

The mindmap for this topic extend's Chaomei's overview. It includes links to Edward Tufte, Stephen Few, Gregor Aisch, Jean-Luc DuMont, Sharon Lin, Trina Chiasson, Steve Crandall, David Strom, Olliver Dyens, Holly McKendry, Andy Kirk, Sandra Rendgen, and Visualoop.

Hans Rosling has demonstrated how powerful storytelling with data can be.

Other observers who raise important issues in functionality and aesthetics in data visualisation include Stephen Few (Information Dashboard Design), David McCandless (Information is Beautiful and Knowledge is Beautiful), and Alberto Cairo (The Functional Art ).

The Visualoop site carries 100 of the best data visualisations of 2014 and 40 of the year's keynotes. One of the keynotes is by Giorgi Lupi on The New Aesthetic of Data Narrative. Visualoop's collection of video presentations for 2015 can be found here.

Visualoop was purchased by Infogr.am in 2014. Infogr.am is one of the many companies that provides a free data visualisation tool as well as fee paying services.

The availability of free visualisation services such as Infogr.am, Tableau Public, Qlik, Silk and Flourish is providing those new to information visualisation with an excellent sandpit to develop the "response-inviting structures" discussed by Wolfgang Iser for other kinds of stories. Open source opportunities are provided by R and D3. Shiny provides a web application framework for R that provides opportunities for interactive data presentation (see, this Tourism example from New Zealand).

Broadcast companies and newspapers are making extensive use of information visualisation and building teams to develop interactive digital storytelling.

Some examples are:

Australian Broadcasting Corporation (World Cup Goals, the Melbourne Cup)
The Guardian (England v Australia Rivalry)
The New York Times (After Snow Fall, Sochi Winter Olympics)

Some of the innovations in the broadcast space are giving sport opportunities to develop how information is shared. This is an example from Coach Paint.

Does data visualisation have an impact on audiences?

In his discussion of data visualisation, Enrico Bertini (2013) asks "are we having any real impact in people's life other than telling theme beautiful stories/". Andy Kirk, also writing in 2013, in his discussion of visualisation observes "You can only do your best to put forward a visualisation that most effectively serves the needs of the subject matter, the context and the audience you’re reaching. Beyond that, the ultimate success is out of your hands". The absence of success stories about data visualisation was also a topic Stephen Few explored in 2009. He writes "Even though we have plenty of evidence from years of research to support the tremendous potential of data visualization, we are lacking in specific accounts that confirm beneficial outcomes in the real world, either empirically in the form of measured results or anecdotally".

You might like to have a look at Andy Kirk's (2013) discussion of criteria for success in visualisation. He considers the distinction between exploratory and explanatory visualisations:

Exploratory visualisations create an interface into a dataset or subject matter. They do not propose a single narrative, nor actively draw out key insights or headlines. Instead, they facilitate the user exploring the data, letting them unearth their own insights: findings they consider relevant or interesting.

Explanatory visualisations are focused, editorially driven works that aim to surface key findings. Whilst they may contain several different dimensions of analysis this doesn’t mean they are exploratory in the sense of facilitating broad manipulation of the variables being displayed. It is in these types of visualisation that we would most associate the function of storytelling with data, often attributed to how they are structured (Edward Segel & Jeffrey Heer, 2010).

Aesthetics

As we gain access to more and more opportunities to visualise data, we have an opportunity to contemplate the aesthetics of visualisation. Edward Tufte has been a catalyst for conversations about presentation. His work is part of a dynamic community of practice that is exploring new opportunities in storytelling and sharing.

This discussion gives us the opportunity to engage in conversations stimulated by artists. See, for example, this 7 minute video about The Art of Data Visualisation.

You might like this longer video (17 minutes) by David McCandless from 2012. It contains a discussion of his Mountains out of Molehills graphic.

The Association for Computational Creativity shares a comprehensive bibliography about the intersection of artificial intelligence, cognitive psychology, philosophy and the arts.

For discussions about colour choices in visualisations you might like to have a look at: Gregor Aisch (2011); Andrew Price (2014); Andy Kirk (2015); Lisa Rost (2016); and Nathan Yau (2017).

For a discussion about reusable data visualisations see Elijah Meeks' (2017) Semiotic.

Personal Learning Environments

Each of us has developed a personal learning environment PLE). This is Martin Weller's (2007) visualisation of his PLE:

Source: The Ed Techie

Graham Attwell (2014) points out:

It is no longer enough to be computer literate. Learners need to be able to direct and manage their own learning, formal and informal, regardless of form and source. In conjunction with More Knowledgeable Others (Vygotsky, 1978) they need to scaffold their own learning and to develop a personal knowledge base. At the same time as the dominance of official accreditation wanes, they need to be able to record and present their learning achievement. Personal Learning Environments are merely tools to allow this to happen.

Beth Kanter (2014) provides examples of how to visualise the network of contacts you have in your personal learning environment.

The use of a range of platforms and approaches in this Sport Informatics and Analytics course has aimed to offer you options in your PLE. Matt Bower (2015) provides a detailed account of some of the web 2.0 technologies available.

Source: A Typology of Web 2.0 Learning Technologies (Matt Bower, 2015)

This may extend to your development of an ePortfolio of your experiences in your PLE. You can find more information about an ePortfolio here.

How we share stories says a great deal about who we are. I have shared the story of the open course on a variety of platforms. In doing so I have made a number of assumptions about you as an implicit reader. I do so with a personal commitment to the ideas central to a domain of one's own (Audrey Watters, 2017).

At the end of the course there is an opportunity to reflect on your practice and consider how you might develop your interest in Sport Informatics and Analytics. It is your personal journey but hopefully as a result of this course you have connections with others who are sharing the journey in different places and times.