A four decade journey in performance analysis and analytics

The end of a calendar year is a good time to reflect on learning journeys. This December, I have been thinking back over four decades.

My fascination with performance analysis and analytics started with my role as a teacher of physical education and as a young coach in the 1970s. In 1977, I started to take responsibility for coaching club rugby union. My role models were Tony Gray, Jim Greenwood, Ray Williams and John Dawes. I think my roots in applied performance analysis were set then. Thereafter, whatever work I did in analysis was focussed on supporting coaches and athletes.

A decade later, in 1987, I was starting the write up of my part-time PhD at the University of Surrey. I had spent three years observing the teaching of physical education in two schools and was immersed in the ethnographic literature. My supervisor introduced me to the work of Miller Mair and from that time I have been keen to explore performance analysis and analytics as storytelling and story sharing.

These are the kinds of things I learned from Miller:

Our worlds are structured in metaphor and images. We can only tell stories from conjured images of what we suppose we are and what we suppose we know, within the language and assumptions of our place and time.

Every telling (whether in psychotherapy, science, the market place or the lovers bed) is a composition with personal intentions. Every telling is partial, suffused with personal interest.

Every telling has to be in some manner and style. Even when we seek to be plain and blunt we are using stylistic devices for signifying plainness and bluntness.

Science has tried to be ‘the story to end all stories’, or a story trying its hardest not to seem like a story at all, but the way things are. Every group has its own sanctioned ways of telling for different purposes and contexts, its ways of listening, ways of evaluating. The ‘hard’ approaches to science have their own ways of telling set up in such a way as to seem and claim to be above and separate from mere telling, beyond any contamination in the telling itself.

But stories are partial and political. We all have vested interests in our psychological and other tellings.

My thesis ended up being a collection of stories. Two of them are:

Do people who have lost their voice have to do it?

Anush and basketball fever

I had started doing some hand notations of rugby and lacrosse whilst I was at St Mary’s College at Strawberry Hill (1978-1986) and had been using VHS video recordings of performance. By the time my PhD was submitted, I had written a book about the use of video in sport.

The next decade took me into the digital era but strongly connected to storytelling (Are we all performance analysts? (1998)). I was fortunate to spend this decade at the Cardiff Institute of Higher Education (later UWIC and subsequently Cardiff Met).

I was a guest at the Sports Coach conference in Melbourne in November 1998.

I took with me  one of the first digital cameras and an early example of a portable analysis system that had been developed by Tony Kirkbride. My presentation to the conference is archived on Slideshare.

These are two of slides I shared at the conference:


I provided an example of my own use of digital stills in coaching as I tried out the new technology.

A decade later, I was at the Australian Institute of Sport (AIS) in Canberra. I rode the wave of digital technologies and was part of a decade that saw the full-fledged software as a service era and the emergence of a video repository for online and near line access on demand. In 2005, I had the opportunity to develop a proposal for funding for three analytics positions. We were also using Australia’s supercomputers and high speed infrastructure to share large amounts of swimming video and data (up to 1tb). By 2007, we had started a data analytics project for the Beijing Olympics that contributed to a gold medal performance and had developed a stable machine learning approach to support cricket coaches in the Ashes Series.

The opportunities I had at the AIS gave me wonderful freedom to explore and champion disruption in the support an institute could offer to the daily training and competition environments.  I was fortunate that my line managers were able to see beyond the narrow limits of videography and coding and embrace a digital age that celebrated knowledge discovery in databases.

Much of my work in the decade following the AIS innovations has been spent exploring open access sharing. In 2017, my focus has been to use the plethora of online education platforms to continue my own learning and to share resources with others in a world that now sees the sharing, aggregating and curating of digital artifacts as a normal activity.

My most recent learning experiences have been with R and ggplot 2.

Reflecting on this four decade journey from analogue to digital experiences has been a fascinating experience. It was helped by a presentation I gave in Ireland earlier this year, Are We There Yet?

What excites me is how present day researchers are using technologies and creating innovative communities of practice that are accelerating our understanding of performance.

When I started my learning journey in the 1970s, it was possible to aspire to be a polymath in performance analysis and analytics. Four decades on the flourishing of all forms of enquiry make that much more difficult.

I am relishing the next decade and am wondering what my experience will be when I look back from 2027.

I am hopeful it will be more like:


Photo Credits

Interesting sidelines (scsmith4, CC BY-NC-ND 2.0)

Miller Mair (Constructivist Psychology)

2008 McLaren Park CX Tilt Shift (Steven Woo, CC BY-NC-SA 2.0)

Geen hulp voor Giusto Cerutti (Nationaal Archief, no known copyright restrictions)

Roots, branches … networks

Thanks to my wife Sue’s reading interests, I was alerted to an article in a United Kingdom newspaper about Judi Dench.

In the article, Judi Dench talks about her love of trees. Over three decades she has transformed her garden into a woodland. She started planting trees to remember a relative or friend. Her fascination with trees led her to spend time at Kew Gardens learning about the annual life cycle of trees … “and how they communicate via vast underground networks”.

In the article, Judi is quoted:

Beneath our feet is a huge network. Not only can they send messages but they can share food and water …

Her story resonates strongly with me. I was fortunate to spend a year as a forestry worker in 1973-1974. This gave me a love of trees and connected me with the rhythms of a year in woodlands. Many years later, when we moved to Mongarlowe in New South Wales, we started to plant trees to remember family and friends.

News of Judi’s love of trees came as I was waiting for the next installment of a Friday Letter from Abbotstown.

The letter is, I believe, a good example of connecting roots and branches in a community of practice. Like the trees planted in Mongarlowe, these are tentative steps to establish an ecology of sharing.

This ecology is nourished by sharing openly as well as in hidden ways of personal connectedness. I have in mind this kind of connections for groups that might share:

This looks like a tree in winter but is taken from page 372 of A manual of practical medical electricity : the Röntgen rays and Finsen light (1902).  The image combines two discoveries that became important contributions to medicine. Wilhelm Röntgen won the first Nobel prize for Physics in 1901. Niels Finsen was from the Faroe Isles and won the Nobel prize for Medicine and Physiology.

I do think that open sharing makes possible not only the sharing of familiar practice but also creates opportunities for creative leaps of the imagination.


Jacquie Tran shared this link with me after reading my post. A great example of a network connection.

Actionable insights: sport analytics



A post by Mary Hamilton (2017) about her time at The Guardian has sent me off thinking about actionable insights in sport analytics.

In her article, Mary shares thirteen lessons from her time as executive editor for audience at The Guardian. Three of the thirteen had a particular resonance with me.

Insight 1 is ‘Data isn’t magic, it’s what you do with it that counts‘. She notes “We make better decisions when we’re better informed, and all data is is information”. She adds that developing an in-house data resource, Ophan, “It’s not just about putting numbers into the hands of editorial people — it’s explicitly about getting them to change the way they make decisions, and to make them better”.

Insight 11 is ‘Radical transparency helps people work with complexity‘. Mary observes “In a fast-moving environment where everything is constantly changing … you have no way of knowing what someone else might need to know in order to do their job well. The only way to deal with this is to be a conduit for information, and not bottle anything up or hide it unless it’s genuinely confidential”.

Insight 13 is ‘What you say matters far less than what you do‘. Mary’s take is “This should be obvious, but it probably isn’t. It doesn’t matter what you say you want, it’s what you do to make it happen that makes a difference in the world”.


Mary’s thirteenth insight underscores the importance of action. In another context, Adam Cooper (2012) proposes:

Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data.

He adds “‘actionable‘ indicates that analytics is concerned with the potential for practical action rather than either theoretical description or mere reporting”.

Some of the key contributors to the sports analytics literature have focused on action.

In 2011, Ben Alamar and Vijay Mehrotra defined sport analytics as:

the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.

Three year later, Chris Anderson defined sports analytics as:

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.

In 2016, Bill Gerrard observed “Sports analytics is all about using data analysis to provide actionable insight for coaches and sporting directors”. He added “Analytics is analysis for purpose. It’s a servant function, there to help managers to make better informed decisions”. In his conceptualisation, “Analytics is decision-driven, domain-specific data analysis”.


In his essay on Concerning Human Understanding, John Locke asserts:

it is ambition enough to be employed as an under-labourer in clearing the ground a little, and removing some of the rubbish that lies in the way to knowledge

Bill’s comment about the servant function of analytics took me back to John Locke and under-labouring. I thought that any sport analyst would be keen to be such a labourer and contribute to “clearing the ground a little”.

One aspect of an analyst’s role is, I think, to reflect on the place granularity will play in the actionable insights we share. Another is to consider our creation of actionable insights from a user’s perspective (Kunal Jain, 2015).

But as Alexander Franks and his colleagues (2016) point out there are some important meta-issues at play here too. They consider “the metrics that provide the most unique, reliable, and useful information for decision-makers”. They employ three criteria to evaluate sport metrics:

  • Does the metric measure the same thing over time? (Stability)
  • Does the metric differentiate between players? (Discrimination)
  • Does the metric provide new information? (Independence)

Alexander and his colleagues note:

In general, any individual making a management, coaching, or gambling decision has potentially dozens of metrics at his/her disposal, but finding the right metrics to support a given decision can be daunting. We seek to ameliorate this problem by proposing a set of “meta-metrics” that describe which metrics provide the most unique and reliable information for decision-makers.

They add:

The core idea of our work is that quantifying sources of variability—and how these sources are related across metrics, players, and time—is essential for understanding how sports metrics can be used.

They conclude their discussion of meta-metrics by proposing a fourth meta-metric: relevance.

Relevance could simply be a qualitative description of the metric’s meaning or it could a quantitative summary of the causal or predictive relationship between the metric and an outcome of interest…

In Practice

Earlier this week, the Crusaders rugby union team, from New Zealand, advertised for a sport scientist. The job description provides a fascinating empirical focus for the discussion in this blog post.

The position description has these elements:

  • Reporting to the Crusaders’ Head Strength and Conditioning Coach, you will be responsible for overseeing and coordinating all aspects of the Crusaders’ performance monitoring systems including further enhancement of data collection, processing and reporting methods.
  • You will also be responsible for collating and reporting on all performance monitoring data to ensure optimal player loading for conditioning and recovery.
  • To be successful in this role, you will need to be appropriately qualified by training and/or experience, including a proven ability in research, data analysis and reporting including an outstanding level of understanding of performance monitoring and analysis tools ideally in a rugby environment.
  • You will also need to demonstrate extensive experience in the use of GPS Technology both hardware and software…

This kind of role is now becoming more and more frequent. It will be good to learn how the post holder adapts to this role and provides relevant, actionable insights that are domain specific whilst being mindful that in addition to structured data there are increasing opportunities for the analysis of unstructured data.

Photo Credits

Tree (Keith Lyons, CC BY 4.0)

Crusaders v Cheetahs (Geof Wilson, CC BY-NC-ND 2.0)