Using the “Range” of Knowledge Within Your Performance Team

David Epstein has a new book titled: Range: Why generalists triumph in a specialized world. I am sure many of you have either read the book or are in the process of reading the book. If you haven’t heard of it, I highly recommend checking it out. Epstein does a wonderful job of telling the stories about development across a broad spectrum of specialties. From world-renowned musicians that can’t read music to CEOs to world-famous artists. These stories discuss the development and learning process that brought these individuals to the highest of heights in their profession. At its core, the book discusses the debate over specialization versus range for development. It details how in certain situations, specialization and repetitive practice has its advantages; but, in most cases, range of skills and experiences are inherently more valuable. This got me thinking about the work of sport and high-performance departments. Within these groups, there is undoubtedly highly specialized individuals with deep knowledge of unique areas of medicine, rehabilitation, psychology, strategy, physiology, statistics and many more. But truly high functioning and successful high-performance teams require the ability to have crosstalk amongst these specialties, as well as specialized individuals with a range of knowledge to understand basic concepts of other specialties and how they might apply to their own. 

Having spoken with hundreds of individuals in the performance realm over the last few years, it’s clear that they all have their own unique context and lens by which they see and interpret the world. Part of Range discusses the notion that our experiences shape our views and that seemingly insignificant experiences can provide deep-seated knowledge that helps us solve much more complex problems later in life (There are a few great examples from the book that I don’t want to spoil). The health, performance, and development of individual athletes is complex, but, in particular, in team sport where individuals are working as a whole to maximize a team goal (winning). The team is a collection of unique and specialized players that have to communicate and work together to accomplish the ultimate goal. So too is the team behind the scenes. There is the front office, coaching staff, analytics, medical, performance staff and, importantly, the operations staff that keeps everything running smoothly. These parts also have unique skill sets that are maximized when they communicate and work together. It requires not only the range of individuals to step out of their comfort zone but also the range of the environment to allow the cross-talk to take place. Information is currency, and within the best groups, everyone is rich (with information). This is critical, especially in a world where there has never been more data, but it’s easy to become data rich while remaining information poor. It’s context that creates information from data and everyone has a unique context to bring to the table. Sharing of data with a range of specialized individuals, who are free to share their unique context and insights (right or wrong), is how data transforms into information and eventually manifests as knowledge. 

Much of the book and the thoughts I developed while reading it brought me back to the start of Dexalytics. It really began in initial conversations about DXA data. We had just finished a pilot study, assessing the accuracy of a new DXA method. As part of it, we scanned a variety of athletes (Track & Field, Football, Basketball), and they received a full-body DXA scan. The players asked if they could get a copy and share it with their coaches. We agreed the information could be valuable so we printed off some copies and sent them over. What followed was a moment of realization that the sharing of data is critical to the generation of information. I stopped by the coaches’ office a week later to follow up and see if there were any questions. Surprisingly, the coach said he hadn’t looked at it yet, partly because the reports were so numbers dense that he really wasn’t sure where to start. He had found the variable percent body fat, a number that was familiar, and that’s about all he looked at. Again, this was a surprise to me at the time, but upon reflection, it was a realization that this is common to anyone when viewing something new. We identify with something we are familiar with, something that we can attach context too that anchors that something new and gives us some information about what we are looking at. I do this all the time when reading research outside my traditional knowledge base. I find something familiar to me, but unfortunately, this can result in a biased context of which to view the research as a whole. The context that I attach to it is based on my experiences and will shape my viewpoints and potentially conclusions about a study. That can be problematic, or beneficial, but what I have come to realize is that it’s probably not the best way to go about learning new information. 

Jumping back to the coaches’ review, we sat down and discussed how I look at the data, the process I go to that involves looking not only at totals but also the regional data (ex. leg lean mass, trunk lean mass). This helps us inform the distribution patterns of an athlete (ex. top-heavy, bottom-heavy) and finally the right vs left asymmetry comparisons. From this pattern, we can start to ask: Is body type, composition, or distribution a limiting factor for this athlete? Or, how has the athlete changed over time? What was even more enlightening is that after we started discussing the patterns for each athlete, the coach was able to provide incredible context (based on his deep experiences with them) around the why’s: why a player may be asymmetrical and why a player may have had a specific change. In some cases, these changes were good and intended, and others were more surprising and led to more questions. However, what it did was start a conversation, a conversation that can involve a wide range of specialists. Soon we started having review meetings with the medical staff and position/event coaches. They too have their own unique context to provide about the athlete. I will never forget reviewing the data with a rowing coach, and showing that a subset of athletes had a unique pattern wherein their “core” abdominal and gluteal regions seemed to be slightly under-distributed (less muscle than what you would think relative to the rest of their body) and their arms were overdeveloped (more muscle than you would think).  I said I don’t know why they have this but it’s clearly different from others on the team. The coach looked at me and flatly said, “This isn’t shocking, these athletes start pulling (with their arms) too early in their stroke. I have been telling them this for months and now I can finally show them some data to back it up.” That is the definition of context (experience that gives information to data and generates knowledge).   

These experiences have further entrenched our belief in what we are doing with Dexalytics. We know that DXA gives a wealth of data, we use a standardized process to aggregate, organize and analyze the data, but it requires the unique context of our users. They are the individuals who know the athletes, they have knowledge, experience, and context across a range of specialties that can generate incredible insight from the data and we want to give them that power. Our goal all along was to improve how practitioners use data to draw insights. They have the power to create the context, we simply provide the process to make it happen consistently and efficiently. We strive to provide data in a way that starts a conversation between the performance team members (because each has their own unique context to contribute) and through those conversations is when knowledge is generated. Data, shared in the right way and in the right environment will not only generate knowledge, but maybe more importantly, new questions. It’s led us to start investigating the relationship between quantity and quality or more precisely, muscle mass and muscle function. We are starting to see some very interesting things between how mass distribution relates to movement, how changes in muscle mass are not always equivalent to changes in muscle function. More to come on this for sure, but it wouldn’t be possible without the wide RANGE of knowledge we have within our Dexalytics user network. 

About the Author:

Tyler Bosch, PhD is a Research Scientist in the College of Education and Human Development at the University of Minnesota, and is a co-founder of Dexalytics.

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