I have had some time to think about meta issues in performance analysis and analytics. I think this is an exciting and transformational time for this epistemological and ontological domain.
My three posts, written today flowed from a friend’s email in which my friend employed in an institute of sport observed that “the biggest challenge is how we develop and mentor these data people”.
The first post (link) discussed the concept of a sticky campus as a “a digitally-enabled space” and “a learning environment designed to give students everything they need for collaborative and solitary study, and to promote active learning”.
The second post looked at the verbs we use to describe what we do (link). This was prompted by a conjunction of my friend’s email and news of the IBM’s AI Ladder (link). The artificial intelligence ladder has four characteristics: collect; organise; analyse; and infuse.
A third post uses a lens of a critical friend to explore pedagogies and practices in performance analysis and analytics. It uses a seminal paper by Arthur Costa and Bena Kallick (1993) (link) to explore the trusted relationships that grow between friend and analyst. I am particularly interested in the role of a friend is an advocate for success (link). A key point in this post is the investment required by leaders in these learning opportunities “funds should be focused on providing high-quality professional learning experiences”.
In conversations about people involved in data analysis, one of my colleagues in an institute of sport observed that “the biggest challenge is how we develop and mentor these people”.
I see this as a critical issue as sport expands its data science portfolios. It has encouraged me to think about the verbs we use to describe our work in data.
When I first started in performance analysis in pre-digital days, we aspired to:
Guillermo Martinez Arastey (2018), amongst others, has described how this role has changed in a digital era (link). It has meant for me that performance analysts are connected and I saw this at first hand when I met Darrell and Adam in Cardiff (link).
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.
It is these “mental simulations of future events” that strike me as very important as we consider the verbs that guide us through a dynamic domain opening up before us.
A few weeks ago, a friend was asked to present about datafication in performance analysis. This set me off thinking about the processes I had heard about and seen.
I started off revisiting Viktor Mayer-Schonberger and Kenneth Cukier’s 2013 book Big data: A revolution that will transform how we live, work, and think (link). In it they discussed at length Matthew Maury‘s career in the U.S. Navy’s Depot of Charts and Instruments. In their words “He saw patterns everywhere”. They added (2013:75) “He had a number of ‘computers’ – the job title of those who calculated data. He aggregated data. He looked for patterns and more efficient routes and sea-lanes.”
I liked their consideration of data in the light of Matthew’s journey all those years ago. They noted:
He was among the first to realise that “there is a special value in a huge corpus of data that is lacking in smaller amounts – a core tenet of big data”.
Astounding that it was done with pencil and paper and highlights “the degree to which the use of data predates digitization”.
Data refers to “a description of something that allows it to be recorded, analyzed, and reorganized”.
To datafy a phenomenon is “to put it in a quantified format so that it can be tabulated and analyzed”.
We built the building blocks for datafication many centuries before the dawn of the digital age.
I thought this account resonated powerfully with Simon Eaves’ accounts (2015, link; 2017a, link; 2017b, link) of Henry Chadwick (link) and baseball. Both are stories of digital pioneers. Simon notes that perhaps as early as 1858, Henry tried to record and analyse as “a first step towards a sport performance analysis to assess relative merits”.
I do think reading these authors about Matthew and Henry together gives real feel for what was occurring in the nineteenth century in the United States of America … at the dawn of what has been a remarkable process.
I hope to write more about this process and provide more background to datafication as the centuries pass by.