Ball not in play

Ray Williams’ book Rugby for Beginners was published in 1973. I first read it as a postgraduate physical education student at Loughborough College. By coincidence, Ray had been a student there too. Both of us were from North Wales.

Years later when I got to know Ray, I was able to explain how important the book was to me in my development as a player, teacher and coach.

The cover of Ray Williams' book.

Huw Richards (link) wrote about Ray’s career and noted his appointment as the Welsh Rugby Union’s first national coaching advisor. In that role he “drove that transformation through his promotion of conferences, teach-ins and courses which gave Wales more than 300 qualified coaches by the mid-1970s”.

I was fortunate to be one of those coaches and delighted in late night conversations with Ray in the bar of the National Sports Centre at Sofia Gardens in Cardiff. It was like being with the Oracle at Delphi.

One conversation became quite heated. I asked Ray about a line in his book that suggested “no player has the ball in his hands for more than one minute” in a game. The essence of Ray’s argument was that each player had a responsibility to support the ball (one of Ray’s game principles).

Even at my time playing rugby at Loughborough, I was sure I did not have the ball in my hands for that amount of time. I suggested to Ray that I ought to investigate what time the ball was in play and not in play.

I did follow up on this for the much of the next two decades. My operational definition of ball in play time was when the game was started and restarted by the referee either by a whistle or when the play was put back into play. Ball out of play was measured by a referee’s whistle or when the ball visibly left the field of play or was waiting the restart of the game.

It took some time to stabilise the recording of ball in play time. I monitored ball in play time from live broadcasts. One of my first successful attempts was on 16 January 1982, in what was then the Five Nations rugby tournament. Scotland played England at Murrayfield in a game refereed by Ken Rowlands (Wales).

  • The first half game time was 42 minutes and 33 seconds. The ball was in play for 10 minutes 28 seconds.
  • The second half game time was 44 minutes. The ball was in play for 13 minutes 10 seconds.
  • In the whole game, the ball in play time was approximately 27% of the available time.

It took me a further three years to develop a template to record each passage of ball in play in real time in addition to the other data I was collecting with hand notation. From this time on I termed passages of ball in play activity cycles.

My record of the Scotland v Wales game played on 2 March 1985 (video link) was:

For the first time, I was able to have a detailed account of game play. I recorded 97 distinct activity cycles (49 first half, 48 second half). Scotland had 52 of these (25 first half, 27 second half) and Wales 45 (24 first half, 21 second half). The game was refereed by Rene Hourquet of France. Wales won 25 points to 21 points.

The activity cycles were:

My record of the 97 activity cycles indicates a total ball in play time of 25 minutes 46 seconds (12 minutes 01 seconds first half, 13 minutes 45 seconds second half). Scotland had 13 minutes 20 seconds of ball possession and Wales 12 minutes 26 seconds.

I shared these data with Ray and we corresponded about the implications of such data for coaching and playing. I continued to share my data with him and he in turn passed it on to colleagues in coaching.

I have returned to these data this week as I researched the concept of dwell time (link). I was delighted to discover that Herbert Levinson (1983) was undertaking similar real-observations of performance … in the context of transit travel times. He concluded “transit performance should be improved by keeping the number of stopping places to a minimum”. That sounds like a fascinating pedagogical insight for rugby union.


Discovering Ma

A picture of ripples created by raindrops

By chance, I heard someone talking about ma today. It was a conversation about minimalism and architecture. Ma is a Japanese term.

The Unique Japan web site (link) observes:

Ma is something that relates to all aspects of life. It has been described as a pause in time, an interval or emptiness in space. Ma is the fundamental time and space life needs to grow. If we have no time, if our space is restricted, we cannot grow. How we spend our time and shape the space we live in directly impacts our progress. These principles are universal, when applied effectively they enhance the way we think and how we engage with our surroundings.

Ma is “the space between the edges, between the beginning and the end, the space and time in which we experience life” (link).

Another web site defines ma as “the emptiness full of possibilities, like a promise yet to be fulfilled” (link).

In exploring ma, I am mindful of the lack of any equivalent in the English language.

I am attracted to ma, as it helps me make sense of how we might use space and time in game playing to transform our experiences. And perhaps to move to what Yoko Akama (2015) conceptualises as “between-ness as a way of becoming with” (link).

I am keen to explore the pedagogical aspects of this. I sense that this a relationship between coach and athlete, teacher and student that has a profound effect on everyone involved.

Photo Credit

Photo by Caroline Grondin on Unsplash

Discussing data

A tilt-shift photography of HTML codes

Three posts popped up recently that explored our understanding of data.

In a recent post, Cassie Kozyrkov proposes “we need to learn to be irreverently pragmatic about data” (link).

She observes:

Take a moment to realize how glorious it is to have a universal system of writing that stores numbers better than our brains do. When we record data, we produce an unfaithful corruption of our richly perceived realities, but after that we can transfer uncorrupted copies of the result to other members of our species with perfect fidelity. Writing is amazing! Little bits of mind and memory that get to live outside our bodies.

Cassie notes that when we analyse data, we are accessing someone else’s memories. If we regard ourselves as data analysts then we are engaged in the discipline of making data useful (an in doing so make decisions about analytics, statistics and machine learning). We can demystify data and talk simply about what we do, how we do it, and what we share.

After reading Cassie’s post, I followed up with Nick Barrowman’s (2018) Why Data Is Never Raw (link). He points out:

A curious fact about our data-obsessed era is that we’re often not entirely sure what we even mean by “data”: Elementary particles of knowledge? Digital records? Pure information? Sometimes when we refer to “the data,” we mean the results of an analysis or the evidence concerning a certain question. On other occasions we intend “data” to signify something like “reliable evidence” …

Like Cassie, Nick cautions against “the near-magical thinking about data”. He notes:

How data are construed, recorded, and collected is the result of human decisions — decisions about what exactly to measure, when and where to do so, and by what methods. Inevitably, what gets measured and recorded has an impact on the conclusions that are drawn.

He adds:

We tend to think of data as the raw material of evidence. Just as many substances, like sugar or oil, are transformed from a raw state to a processed state, data is subjected to a series of transformations before it can be put to use. Thus a distinction is sometimes made between “raw” data and processed data, with “raw data” often seen as a kind of ground truth

Nick argues that when people use the term raw data “they usually mean that for their purposes the data provides a starting point for drawing conclusions”. (Original emphasis) He adds:

the context of data — why it was collected, how it was collected, and how it was transformed — is always relevant. There is, then, no such thing as context-free data, and thus data cannot manifest the kind of perfect objectivity that is sometimes imagined

By coincidence, I was reading Will Koehrsen’s suggestions (link) for a non-technical reading list for data science that starts with this introduction:

we can never reduce the world to mere numbers and algorithms. When it comes down to it, decisions are made by humans, and being an effective data scientist means understanding both people and data

I thought all three posts were excellent nudges to enhance our reflexive practice. They reminded me also of EH Carr’s (1961) discussion of historical ‘facts’. He noted that far from being self-evident, historians give facts their significance and do so selectively. They are in effect “a selective system of cognitive orientations”.

Photo Credit

Photo by Markus Spiske on Unsplash