Bridging the Gap: Improving the Coach-Analyst Relationship (Part 1)

Originally Written: November 2016

Last week I attended the Sportdata & Performance Forum held at University College Dublin in Ireland. The Forum is in its third year having been previously held in Berlin in 2014 and 2015. The organiser, Edward Abankwa and his colleagues are to be congratulated on yet again putting together an interesting and varied programme with a good mix of speakers. Frequently European sports conferences are dominated by (association) football but this gathering was again pretty diverse with Olympic sports, rugby union, rugby league and the Gaelic sports all well represented. And crucially the Forum is not a purely sports analytics event but draws speakers and delegates involved in all aspects of sports performance – coaches, coach educators, performance analysts, data analysts, sports scientists, academics, consultants and commercial data providers. I presented an overview on developments in spatial analytics which I will discuss in a later post. In this post (split into two parts) I want to draw together the various contributions around the theme of how to make data analytics more effective in elite sports.

1. The analyst must be able to translate analytical results into coaching recommendations.

A recurring theme throughout the Forum was that the impact of data analytics in elite sports is often limited by a language problem. Brian Cunniffe (English Institute of Sport) talked about the need to bridge the language gap between the coach and the analyst/scientist. So often analysts and coaches do not speak the same language. Analysts see the world as a modelling problem formulated in the language of statistics and other data analytical techniques. Coaches see the world as a performance problem formulated in the language of skill technique and tactics. My very strong view is that it is solely the analyst’s responsibility to resolve the language problem. Analytics always starts and ends with the coaches. Coaches have to make a myriad of coaching decisions. Analysts are trying to provide an evidential base to support these coaching decisions. The analysts must start by trying to understand the coaching decision problem and then translate that into a modelling problem to be analysed. The analyst must then translate the analytical results into a practical action-focussed recommendations framed in the language of coaching not the language of analytics. Denise Martin, a performance analyst consultant with massive experience in a number of sports in Ireland, summed it up very succinctly when she said that the task of the analyst is to “make the abstract tangible”. To do this the analyst must spend time with the coaches, learning how coaches see the world in just the same way as performance analysts do in order to produce effective video analysis.

Martin Rumo (Swiss Federal Institute of Sports) provided a great example of the coaching-analytics process working effectively. He described his experience collaborating with a football coach who wanted to evaluate how well his players were putting pressure on the ball. In order to build an algorithm to measure the degree of pressure on the ball Martin started by having a conversation with the coach to identify the key characteristics of situations in which the coach considered there was pressure on the ball. This conversation provided the bridge from the coaching problem to the modelling problem and increased the likelihood that the analytical results would have practical relevance to the coach.

One of the most interesting speakers at the Forum was Edward Metgod, the former Dutch goalkeeper and now a scout and analyst with the Dutch national team. Edward has a playing and coaching background, a deep commitment to self-improvement and an open mind to using the best available tools to do his job effectively. He is precisely the type of football person with whom a data analyst would want to work. Edward started his talk recounting how he had read a number of books on data analytics which he had found interesting but when he came to books on football analytics he was quickly turned off. The problem with the football analytics books is the language (although I also sensed that he had found nothing new in these books to advance his knowledge on football in any practical way). Edward then detailed that in Dutch football there is a common coaching language which breaks the game down into four moments – defensive transition, offensive transition, ball possession, and opponent ball possession. All of Edward’s reports are structured around these four moments. The clear implication for any data analyst, like myself, working in Dutch football is that you must learn this coaching language if you want to communicate effectively with coaches. I should add that I have subscribed to the four-moments perspective for several years and apply it as a way of structuring my analysis in any invasion-territorial team sport.

2. Data analytics can only be effective in organisations with a cultural commitment to evidence-based practice.

The importance of having the right organisational culture to support data analytics was stressed by many of the speakers. Rob Carroll (The Video Analyst) defined culture very neatly as what a team does every day. A common characteristic of every sports organisation with which I have worked and in which data analytics has a real impact is a cultural commitment to creating an evidential base for their decisions. And that cultural commitment is led from the top by the performance director and head coach with buy-in from all of the coaching staff. As I have discussed in a previous post, Saracens epitomise an elite team in which data analytics has become part of how they do things day to day, and that culture has been built over a number of years led by their directors of rugby, initially Brendan Venter and then his successor, Mark McCall. Many European sports organisations still have a long way to go to in their analytical development and some remain staunchly “knowledge-allergic”. Analysts themselves have been part of the problem by not learning the language needed to communicate with coaches. But the organisations bear much of the responsibility for the lack of progress compared to many leading teams in the North American major leagues which have used evidence-based practice to gain a competitive advantage with the 2016 World Series champions, the Chicago Cubs, just the latest case study of how to do evidence-based practice effectively. Too often teams have appointed analysts without any real strategic purpose other than it seemed the right thing to do and what other teams were doing. Data analytics must be seen as a strategic choice by the sporting leadership of the team, a point made eloquently by as Daniel Stenz who has extensive experience in applying analytics in football in Germany, Hungary and Canada. It can also require buy-in from the team ownership particularly since, as Denise Martin explained, evidence-based practice thrives in a culture that emphasises the process not the outcome. But of course an emphasis on process requires that the team ownership adopts a long-term perspective on their sporting investment which is always difficult in sports organised as merit hierarchies with promotion and relegation (and play-offs and European qualification). When the financial risk is so dependent on sporting results the team ownership inevitably tends to become increasingly short term in judging performance so that quick-fix solutions such as signing new players or firing the head coach prevail. Analytics is unlikely ever to be a quick fix.

3. Start simple when first introducing data analytics as a coaching tool.

Another common message at the Forum for teams starting out on the use of data analytics is to start simple, a point made by Denise Martin and Ann Bruen (Metrifit) amongst others. Analysts are often guilty of putting more emphasis on the sophistication of their techniques rather than the practical relevance of their results. Analytics must always be decision-driven. Providing some simple useful input into a specific coaching decision will help build credibility, respect and coach buy-in, all vital ingredients to the successful evolution of an analytical capability in a team. Complexity can come later. As Ann reminded us, avoid the TMI/NEK problem of “too much information, not enough knowledge”. Elite teams are drowning in data these days and every day it gets worse. Just try to imagine how much data on physical performance of athletes in a single training session can be produced with wearable technology. The function of an analyst is to solve the data overload problem. Analysts are in the business of reducing (i.e. simplifying) a complex and chaotic mass of data into codified patterns of variation with practical importance. Start simple, and always finish simple.