The use of data to describe sport is fundamental, how else would we keep score? However, the use of data to inform and enhance sporting performance is a more recent concept, firstly through the rise in sports science through the latter half of the 20th century before increasing exponentially with the digital revolution in the 21st.  Data analysis in sport really hit mainstream consciousness with the publication of the book Moneyball by Michael Lewis in 2003, and today a search of “Data” + “Sport” in Google produces over 1 billion hits.

The challenge for the modern practitioner is not only to utilise the data for actual performance enhancement of individuals, but also to contribute to the performance of the organization as a whole. To do this the data we collect must transcend our own working practices. Within a professional sports setting we exist in a complex system, where the interdependence of the components combine to affect the outcome. Relating our data back to the outcomes and basing decisions off this is good practice, but we must also be aware of the impact those decisions have on the wider organization and of course, the athlete.

There is a pathway here for taking our data and translating it across the organization. Firstly, data must be translated into information, organizing it in a way that we can begin to draw conclusions. To do this we must give it some context. This can be done by visualizing it graphically, presenting it verbally or in a written report. It is vital if there is to be success that the practitioner discovers which format works best with the athletes and staff they work with. In practice, a combination of all three may be best when working in a diverse organization.

A large part of the challenge is avoiding some of our inbuilt cognitive biases. Daniel Kahneman, in his book Thinking Fast and Slow, describes the phenomenon of What You See Is All There Is (WYSIATI). This is our inability to question what evidence may be missing (such as statistical noise in testing data), particularly if it confirms what we already thought (confirmation bias). As humans, we are prone to filling these gaps and stringing together information into a false narrative to explain the outcomes we are experiencing. This can lead to a false attribution of responsibility and incorrect conclusions drawn. As practitioners, we must firstly be aware of this in ourselves, but also cognisant that it will be present in the athletes and staff we work with. As the late, great Nick Broad said, “Statistics are our weapons” and provide a toolset to add additional, robust context to the information you provide.

In general, the move from data to information is done well in most professional environments. The challenge comes in turning that information into knowledge. New knowledge is developed through absorbing the information, interacting with it and reflecting deeply on how this relates to what we already know. Once we have done this, inevitably, we will have further questions and the process begins again with data collection/analysis, thus it is cyclical. Again, this is of only limited use if it occurs at the level of an individual practitioner or department. Processes and structure must be put in place at an organizational level to facilitate this absorption, interaction and reflection for everyone. Firstly, organisations must have all information easily accessible to all practitioners through the creation of a capable information management system that is clear, intelligent and can be customised and edited by each stakeholder, leaving them free to explore.  Leaders must then foster an environment of psychological safety that encourages robust reflection on presented information.

Knowledge successfully translated across an organization no longer becomes purely knowledge but working philosophies. Therefore, this transfer of knowledge occurs in two directions. Upwards from the practitioner on the frontline but also from the leadership down, as knowledge is related back to the organisational values. In a strong organisational culture, these will help to inform the wider impact of any new knowledge and steer the application of this knowledge by the practitioners.

Once we have been through many iterations, learning from mistakes and refining our organisational knowledge and practices, we will arrive at the final level in the process that begins with raw data – wisdom. Wisdom is the ultimate level of understanding. Data and information deal with the past.  However, when we gain wisdom, we start dealing with the future as we are now able to vision and design for what will be, rather than for what is, or was. Wisdom will tie very closely to the fundamental values of the organisation (their “why”) and be aligned with vision and mission statements.  In the areas that we gain wisdom, we will have vastly reduced uncertainty surrounding the decisions we take.  Over time, they will become aphorisms that form the basis of our philosophies on performance.

Although it may often feel distant for the frontline practitioner, the way that data is collected and information disseminated has a much wider impact than is immediately obvious. This process begins with robust data collection and analysis, making use of statistical weapons to cut through cognitive biases and provide realistic reasoning to help inform our decision-making.