Bridging the Gap: Improving the Coach-Analyst Relationship (Part 2)

Originally Written: December 2016

4. Analytical results are usually presented most effectively to coaches by using data visualisation and story-telling.

As well as the imperative of translating analytical results into practical recommendations framed in the language of coaches, a number of speakers stressed the importance of data visualisation and story-telling as communication devices. “A picture is worth a thousand words” has become even truer in the age of data analytics where effective data visualisation has become a vital tool for the analyst. Rob Carroll (The Video Analyst) illustrated this very well with his graphics on the quality of shooting opportunities in Gaelic football, a form of expected goals model. Ann Bruen (Metrifit) suggested that we should always have in mind the story we are going to tell as we collect and analyse the data. Ben Mackriell from Opta, whose core business is providing performance data, made the same point when he said that it is possible to have a conversation about data without actually mentioning the data (or the analytical techniques). Of course when it comes to evidence-based story-telling we must remain open-minded and allow the precise details and ending of the story to emerge from the analysis. There is always a danger of not allowing the data to get in the way of a good story, of pre-judging the results of the data analysis; it is what cognitive psychologists call confirmation bias. A good evidence-based story is a story that conveys analytical results in the language of coaches, focusing on the practical implications with explanations of athlete and team performance framed in terms of skill technique and tactical decisions. As Edward Metgod (Royal Dutch Football Association) pointed out, coaches are interested in causality not correlation. Analysts must translate the evidence of statistical associations into credible stories of cause and effect with clear implications for targeted interventions to improve performance. When all is said and done analytics is actionable insight.

The discussion of the importance of story-telling reminded me of the advice of Alfred Marshall on the use of mathematics in economics. Marshall probably did more than anyone to systemise economics as a subject and much of his mathematics and diagrams still remain in the textbooks. Marshall was very aware of the uses and abuses of mathematics. Economics was intended to be a practical subject about the everyday business of life but Marshall became increasingly concerned that economists assumed good mathematics meant good economics. He advised that if the mathematics could not be translated into English and then illustrated with important real-life examples (i.e. a good story), then it should be burnt. Apart from the health and safety issues (perhaps safer to shred than burn), Marshall’s advice holds good for data analytics too. If it doesn’t produce actionable insight, it is worthless.

5. Don’t ignore external commercial data if it is available and affordable.

Any discussion of data analytics must include a discussion of the nature of the data being used. The Forum was a great place for this type of discussion giving that it brought together external and internal data providers, data analysts and end-users. In the past there has been too much emphasis on different types of data as substitutes whereas now there is greater acceptance of the complementarity of data. And that complementarity will get even better as there is more and more cross-over in personnel between teams and commercial data providers. Ben Mackriell at Opta is a good case in point, now in charge of OptaPro but with years of experience working with teams in rugby union and football. External commercial data offers consistency and coverage whereas internal data is team-specific and often includes expert coach evaluation of skill technique and tactical decision-making relative to the game plan. The differences between these two types of data are variously described as objective vs subjective, frequency vs evaluation, general vs expert, small data vs big data. The differences were well illustrated in the Q&A that followed Edward Metgod’s presentation when he was asked how he would define the transition phase of play. Edward replied as a coach and scout with a subjective/evaluation/expert definition that the transition represents the period of play after a team loses possession but has not gained its defensive shape. Transition is determined by tactical factors in contrast to more objective definitions in terms of a specific time period (e.g. the first five seconds after possession is lost) or the number of passes made by the team gaining possession. What is important to recognise is that these different types of data have different but complementary functions. For example, external data, possibly in the form of a player rating system, can be used at the first stage of player recruitment to identify a target group of players for whom at the second stage internal data is then produced by the team’s scouts. This is exactly the system of e-screening of potential player acquisitions that I recommended to Bolton Wanderers in 2005. Increasingly I am finding that my greatest and most interesting challenge as an analyst is to generate expert insight from non-expert data particularly in opposition analysis. Can I get inside the minds of the opposition coaches by studying the patterns in their data?

6. Data analysts can make a vital contribution to the organisation of training sessions.

There were a number of speakers at the Forum whose specialism lay in strength and conditioning, and sports science. In addition the Forum also included presentations from coach educators. Both of these groups shared a concern with the optimal use of training time. As a qualified coach and university professor, I want to gain a deeper understanding of the skill-acquisition process whether it be how players learn to perform in games or how data analysts learn to be effective in teams. Nick Winkelman (IRFU) was the lead-off speaker at the Forum and made some great points on both skill acquisition and the role of analytics. As both Nick and several other speakers stressed, when it comes to effective learning, “context is everything” and randomised but relevant learning opportunities provide the most effective way of acquiring and retaining new skills. Blocked repetitions of a specific skill will improve the accuracy with which a skill is performed in a training session but this does not necessarily transform into a game context when the player must not only accurately execute the skill but also make the right decision as to when to execute that particular skill. Nick argued, rightly in my mind, that too much of the data analysis linked to training is focused on workload when what is also needed is a greater input into creating the appropriate game-related contexts.

7. Data analytics is only one input into decision making by coaches, albeit a potentially very important one if used effectively.

The Forum brought together a diversity of specialisms involved in high performance sport. All agreed, albeit with greater or lesser conviction, that data analytics is potentially a very important coaching tool but its effectiveness had been often limited by poor communication particularly the failure of analysts to translate analytical results into actionable insight framed in the language of coaches. I came away from the Forum feeling positive about the future of data analytics in high performance sport. Data analytics is now being seen as another tool to complement scouting, video analysis and reporting. But analysts must guard against complacency. There is still much to do in many sports and in many teams to create a thorough-going commitment to evidence-based coaching. And we will only do that by “bridging the gap” and producing actionable insight relevant to day-to-day coaching decisions.