“Analysis isn’t impact. Interpreting analysis isn’t impact. Taking action on analysis might be impact, but we have to evaluate it along the way.”
Last week I attended the The Sports Performance Data & Fan Engagement Conference and spent a good day listening, learning and reflecting. It’s always good to sit and listen to sharp minded people. I nodded in agreement to the sage words of old colleagues Matt Parker (EIS) and Andrea Wooles (Canadian Cycling), put a face to the name and enjoyed the sense of Finbarr Kirwan (USOC) and Ben Smith (Chelsea FC) and as ever took interest and inspiration just as much from the line of questioning from perceptive folks such as Daniel Green (BMC racing) and Tom Robertson (Edge10).
I think Data and Technology conferences are useful and a valuable forum to gain insight into objective measurement. However, from a Sports Data conference, though there was one take home that lingered in my mind days after this gathering. That take home seem to be the persistent and enduring preoccupation of all of those involved in, delivering and driving performance OR (and let’s not rule it out) I am just bound by my own bias that;
“Performance can’t be solved by technology alone.”
For every question about data and technology from those chairing, the sessions were answered with a call for simplicity and a focus on human factors – like having a conversation, making an agreement, working together…
“What’s the latest approaches to data?” was met with “Doing less of it and getting the team working more effectively”
“What technology do you see making the big breakthroughs at the moment?” was met with “Smartphones! Keep it simple – everyone’s got one!”
“What are your recommendations for using Python and R?” was met by “It’s not the analysis, it’s the conversations with players, which means getting along with them and finding what makes them tick!”
The same experienced views came in the fan engagement session from the heads of various big brands;
“What’s the best platform to apply CRM funnels to your audience?” was met with “Forget about the platform, ask what you’re trying to achieve and you’ll probably find most platforms are overpowered for the task in hand”
The questions from the floor rarely talked of the ‘what’, or the ‘method’, but pleaded for more guidance of the how and the heart of making it happen with real people, with real needs, real desires, real hopes and concerns.
There was much yearning for big data to sort everyone’s lives out – but those who live it day to day pushed back!
An article this week in Stanford Business probed this particular dynamic, that we’re searching for the reductionist single answer by ploughing budgets and hopes into machine learning and technology for the elixir of singular clarity. However, as Susan Athey, Professor of Economics Technology states;
“Machine learning solves simple problems, but it is not sentient. And it struggles when applied to many business problems.”
They could for example show that Winter Olympic gold medals correlates with snow fall. Machines might point out that the snow fall is causative to Winter Olympic success, though the machine might just as easily suggest Winter Olympic medal success is causative to snow fall!
Let’s zoom out for some perspective. The gap between knowing/observing with ‘truth’ is a concept that has been long appreciated. Italian philosopher Giambattista Vico’s verum factum principle (1710) states that truth is verified through creation or invention and not through observation. This was developed more fully in the last century by Swiss psychologist and philosopher Jean Piaget that “Knowledge does not reflect an objective ontological reality, but exclusively an ordering and organisation of a world constituted by our experience.”
In other words, knowledge does not match reality: it organises it. Piaget reminds us that the only tools to the ‘knower’ are the senses – through seeing, hearing, touching, smelling and tasting (not all are applicable to get your printer working – I’ve tried them all) that an individual builds a picture. Therefore, what I see is different from what you see, because we have different experiences. Communication is possible because we have a similar reference point about word meaning, however, what I say is not always what you hear and perceive.
Understanding this means acknowledging the complexity, appreciating the interpretation of viewpoints, working hard to clarify communication, agreeing and reviewing, respecting and challenging and many many more dynamics to be understood and skilfully worked with. That way we can work more concertedly on the collaboration, connection and to ‘share’ a common understanding of what we’re working on.
Nick Grantham put it brilliantly after our 2017 conference;
“In a world that focus on the XO’s of performance sport, big data, spreadsheets and complex theory delivered by ‘experts’ that have rarely got their hands dirty in a high performance environment, we need an unashamed focus on the subtleties of what it actually takes to support champions”