Tuesday, July 28, 2020

...If it ain't sayin' nothin'. - Searching for My Voice and Meaning

I first learned about Vollequality last night when a few coaches I follow on Twitter posted about it. Reading and understanding their mission doesn't take long. But understanding my own mission has not been as straightforward. To be clear, this post is not about their mission. I support them completely. I have signed their pledge. This post is about my relationship with social justice, commitment, and meaning.

The title of this post is part of a line from a Public Enemy song called "He Got Game" (from the 1998 Spike Lee movie of the same name). The full (NSFW) line is "It might feel good, it might sound a little somethin' But f*ck the game if it ain't sayin' nothin'". I have recalled that line often over the past few of months as I have looked for a place in the larger social struggles that are being highlighted by the nightly news. What good have I achieved by yelling at my television? My game ain't sayin' nothin'. I have to do better than that. But what is the next step?

Vollequality is asking me to sign a pledge and get the word out. It's a positive step, right? You bet it is. So why does taking that step bother me? I don't think it is a discomfort with what they ask me to represent. If anything, it is a discomfort with the idea that I may let myself off the social justice hook after telling my social media followers that I "did" something. Hashtag activism like that bothers me. It can be long on chatter and short on change. It's not the movement's fault if I post something on Instagram and call it good. That's all my fault. It's about where I set my own standards for caring about something. It's about moving up the "change" axis however I can. I decide what it means to be committed to a cause. I am free to choose if and how I talk about the things I care about. I choose to interpret the discomfort I feel to mean that my commitment must go beyond a tweet. But that's not the end of my introspection. (The drawing comes from Jessica Hagy's Indexed, which is awesome.)

If I want to care more deeply, then what does that look like for me? Whether I am completely happy about it or not, the first step for me is just to talk about it. Talking about equality, equity, racism, sexism, ableism, etc. is uncomfortable already and that is an important obstacle to overcome. But then there is the personal obstacle of discussing matters that are personal and/or private. These two obstacles, though separate, are regularly intertwined in my life. I don't like to talk about much of anything on social media so what do I do when it's time to talk about how I feel about social justice? I don't know how to do it but I have to commit to doing it. And screwing it up. And working it out. And trying again. But, like I have told many an athlete in my care, we're not going to get better at it by not doing it.

So what good is just talking about it? By itself, it doesn't feel like much to me but I can contribute to making social justice something that gets talked about by just talking about it. Inspiring others is one of my core beliefs so I view this as a chance to inspire others to care a little more about what matters to them. Maybe, as we talk more about social justice we can begin to understand it, and by extension, each other a little more.

And what about my need to feel like I'm doing something? I have found little things to begin stretching myself out. Sometimes it is consumerism disguised as activism. I've ordered stuff from Free Hugs Project (which may be the saddest shirt to wear during a pandemic), Black Lives Matter, and a brewery participating in the Black is Beautiful collaboration. (For an interview with the brewer who started this, check out the excellent Share a Pint podcast.) I have added my pronouns to my email signatures as well as to any presentations I make. I try to ask good questions and then listen to what people want to tell me. And then I've been donating to different causes.

For me, this started around Ahmaud Arbery's murder. Reading about him helped me realize how much I take running, which I do often, for granted. So decided to take a thing that is comfortable for me and make it uncomfortable. I decided to donate a dollar to BIPOC causes for every mile I run this year. I also added BLM to the visor I wear when I run. Now I am invited to think about how others view me while I am out there. I wonder if they think positively or negatively of me. In my mind, I beg them to think about it. This is nowhere near what it's like to be Black and I have to think about that out there too.

It's never going to be enough. The work is never done. But that also means that I can spend every day doing a little more work. Maybe I'll raise a little hell before I'm finished. Maybe we can do it together.

Wednesday, July 22, 2020

The Game May Teach the Game But It Could Use My Help - A Short Review of The Constraints-Led Approach

If you have heard coaches talk about motor learning then you have almost certainly heard coaches utter the phrase, "the game teaches the game". This adage is usually meant to point out that the best learning occurs when learners are immersed in the most game-like environments possible as opposed to repetition-heavy drills. But the phrase has also been used as an excuse for coaches to be too passive in their practice design and execution. The authors of The Constraints-Led Approach: Principles for Sports Coaching and Practice Design use their book to show coaches how to walk the tightrope between doing too much coaching and too little and why that balance matters.

This book is written by four authors who are considered to be leaders in the relatively young field of ecological dynamics. The constraints-led approach (CLA) is a methodology that is built on the principles of ecological dynamics and non-linear pedagogy. And this is just the beginning of the list of technical terms to be learned in this field. The authors are well aware that there is a sizable divide between researchers and practitioners (coaches) and that divide is filled with jargon that the researchers are familiar with but that coaches are not. A main goal of the book is to bridge that divide by describing the existing research as clearly as possible. Another goal is to give coaches practical tools and processes to facilitate the application of CLA in practice design. It is a testament to the difficulty of these tasks that the book is still jargon-laden but it is also testament to the authors' skill that the book is still very readable. Part one contains the heaviest mental lifting, as it lays the foundation for parts two and three. Having read many scholarly articles, some of them in this field, I can say that the authors have done well in making their principles accessible but I still had to work for it. The ensuing parts read much faster and made much more sense as a result of the efforts put into part one (by both readers and authors). So what do the authors say?

While I do want to share some of the ideas of CLA, I don't want to spend the rest of this post trying to teach you CLA. After all, that's what the book is for. Motor learning, ecological dynamics, and CLA are all deep, complex areas that are worth study and I don't pretend to think that I can sum it all up here. I want to spend more space talking about how my exposure to CLA has affected my thinking.

Perhaps the heart of ecological dynamics is the idea that skill acquisition revolves around the relationship between the performer and their environment. This relationship includes the coupling of perception and action. The best learning is a product of tight perception-action coupling. Factors that affect that coupling, and therefore learning, are constraints and fall into three categories: task constraints, individual constraints, and environmental constraints. These constraints create affordances, which can be thought of as possibilities for the relationship between performers and their environments, and these affordances change as time and constraints change. Performers then self-organize actions in response to these affordances. Coaches, then, are tasked with manipulating constraints in ways that afford actions and/or decisions that encourage learners to self-organize productive movement solutions.

The main question I keep asking myself now is, "where does this leave me?" which is really a proxy for "what does this mean for my coaching style?" I have learned a great deal from reading this book and I also have a great deal to think about relative to my role as a teacher. As a methodology for learning, I see CLA as somewhere between the cliché motor learning, "the game teaches the game" and drill-based instruction. CLA appears to say that representative practice design (make it game-like) is crucial but that coaches can improve learning by manipulating constraints to accent certain aspects of the game at certain times. The authors put knowledge of the learner at the center of how and why coaches shape training. Coaches must know what their learners need to work on (often by consulting with them) so that they can maximize training sessions. This idea is very meaningful to me because I, like the authors, do not think that a "one-size-fits-all" method of coaching is best. Coaching requires work and, to me, that work means engaging often with the learners in my care and shaping our training with what I learn from them. The authors give me permission to do things that may not be completely "game-like" if I can create constraints that afford an aspect of the game that I have found to be important. This is hardly carte blanche to do whatever I want, but it is a degree of freedom (yes, I deliberately use this term) that strict motor learning proponents  might not allow. But, with that freedom comes responsibility. If I am going to depart from complete representativeness then I better have not only good reason for doing it but I better also have a detailed plan for how I am going to do it. Like any other methodology, CLA can be done well or poorly. The authors are giving me the tools and exhorting me to do it well. I think that the authors view intervening in or manipulating the performer/environment dynamic as a weighty choice that should not be undertaken lightly. If I am going to alter the way an athlete can view the environment then I need to fully consider how perception and action will be affected. I think that the tools that the authors give me are meant to, at least in part, ensure that I have thoroughly considered my interference.

Saint Augustine wrote that, "complete abstinence is easier than perfect moderation" and this may be what looms largest in my mind after reading this book. It would be easier, as a coach, to sit back and let the game teach the game or to control every aspect of practice through stringent drills. It is more challenging to walk the thin space in between the two extremes. But the point is that the challenging space between is where the best learning can happen.

n.b.: While I believe that this is an important book and have learned a great deal from it, I also believe that it is in bad need of a second edition. It is my impression that the editing of the text was either hastily or poorly done. I noticed numerous instances where good ideas were clouded and where language usage was inconsistent. Given the importance of terminology and ideas to this book, it seems unfair to me that the authors should be subject to and judged by poor editing. I look forward to an updated edition of this book that may benefit from better editing.

Sunday, July 19, 2020

"All I Know Is That I Know Nothing" - A Short Review of The Drunkard's Walk


Socrates is credited as saying that, "all I know is that I know nothing" and, after reading Leonard Mlodinow's The Drunkard's Walk, I feel like I should probably have that tattooed on my arm as a constant reminder. Mlodinow's thesis was certainly not to make readers feel like the world is unknowable so my assertion needs a great deal of context.

The author, a Caltech physicist, strives to give readers access to several of the statistical concepts that underpin our modern lives. Along the way, he also dispels many of our illusions about how well we think we understand the world around us. It isn't that the world is too complex, nor is it that it is completely random. When complexity and randomness are coupled, which is more often than we care to accept, then we need to admit that all we know is that we know nothing.

Mlodinow makes a convincing argument when he writes about stock market investors and prognosticators (chapter 9). We want to believe that it is possible to strategize our way into large returns on our investments. "Research has shown that the illusion of control over chance events is enhanced in financial, sports, and, especially business situations when the outcome of a chance task is preceded by a period of strategizing, when performance of the task requires active involvement, or when competition is present" (p. 188). We tend to fall victim to the Texas sharpshooter fallacy but how we do so isn't always clear to us. If we view an investor's run of good results as improbable, that may be, to some extent, accurate but we need to recognize what is actually improbable. If we had, prior to the run of success, asked "what is the probability that this exact investor has this much success in this exact time frame?" then the chances are small. The problem is that we have ignored a great deal of randomness to anoint this one person. When we view the results after the fact, the correct question to calculate the probability of that success would actually be, "how likely is it that any investor has that much success in any period of similar length?" Viewed in this way, I feel like the only appropriate reaction is to say, "it was only a matter of time".

Another important lesson for me in this book is about what it means to say that an outcome is out of the ordinary. This is particularly hard for me to comprehend because of how commonly we talk about "statistical significance" and calculate p-values to show that things we have observed are far enough from the average to be interesting. Mlodinow wasn't trying to tear all of that down but he did succeed in changing the way I frame such calculations and discussions. When I calculate that something is statistically significant, I have not determined that the outcome was nonrandom. I have calculated that the outcome is very unlikely to happen. We then tend to infer from that calculation that the outcome was nonrandom rather than just rare. This, to me, is an expression the "illusion of control" I mentioned above. For example, I have a solitaire game I like to play on both my phone and tablet. On the tablet, I have won 46% of the games I have played while I have won 50% of the games I have played on my phone. It's the same game and the same player so shouldn't the percentages be about the same? Only if I have more control over the outcomes than I really do. Maybe I screwed up some chances to win but it is much more likely that I just didn't get as many deals that were winnable and that's random. If I reset the stats on both devices and started over, it is likely that the percentages would still be different but it's hard to say just how different they would be.


In my job as a Technical Coordinator, I work with athlete performance statistics and one could argue that my job is to draw conclusions with certainty because those decisions are based on statistics and math. As I have sometimes put it, "I'm right because I have a pile of numbers". Mlodinow helps me keep myself intellectually honest when he quotes Jakob Bernoulli: "one should not appraise human action on the basis of its results" (p. 100). It's not that I can't or shouldn't do my job, it's that I need to include the appropriate caveats when I do. It's important that I provide context about how rare or random an outcome may be. It's important that I can give nuance to the balance between what is chaotic and what is controlled.

Monday, July 13, 2020

It's a Jungle Out There - A Short Review of Statistical Analysis with R for Dummies

I have learned that my professional life as a volleyball technical coordinator means different things to different people. To coaches and athletes, I can often be seen as "the numbers guy" and perceived as an expert in the area of "statistics" as it is commonly understood in the sport. That is to say that I can collect data well and I can create tables and graphs of simple data like reception averages and attack efficiencies. Being able to do that well is enough to help my team function well and for my coaching staff to make informed decisions.

But to a statistician or a data scientist, that level of understanding and performance is below average. There is so much more to know and understand so that someone in my position can ask more and better questions of the data and present more and better answers to those questions. I don't mean to say that my job should be filled by someone with a background in such fields as data science or statistics instead of by me but I think it is important to recognize that there is far more to the world of statistical analysis than what most in our sport consider.

As I have grown and learned as a professional, I have found it important to expand my view of our world. And I have learned that our world is full of jungles, seas, deserts, and other wild, if not unexplored, territory. Our maps are full of cities and roads and other ordered, well-understood areas too and that's where we domesticated animals spend most of our time. But do the well-mapped areas provide the adventure that some of us are looking for or do we need to go out where "there be dragons"?

I have spent some time over the past couple of years beginning to explore the periphery of our known world. I have taken (and hope to take more) statistics classes at my university. I have learned R (a statistical programming language/tool) at least well enough to begin creating interesting code on my own. Joseph Schmuller's book, Statistical Analysis with R for Dummies, is another foray to the edges of my map and another logical step in my professional development. The book isn't meant to drop the reader in the middle of a jungle and force them to hack their way out. If you have a great deal of knowledge and experience in both R and statistics, this book is more like a tour of a local garden than any kind of jungle. But for people like me, with enough knowledge to hurt themselves out there, this book is a way to take day trips into areas that feel wild to us.

The author lays out his hopes for the book in the introduction, it's not just a statistics text or a detailed R text or a cookbook that provides code to solve specific problems. Schmuller wanted to blend all three and, I think, he has done so admirably and accessibly. With a small background in both stats and R, I was able to skate through most of the first two parts ("Getting Started with Statistical Analysis in R" and "Describing Data") as well as Part Five ("The Part of Tens", a tradition in "for Dummies" books) without too much trouble. Parts Three ("Drawing Conclusions from Data") and Four ("Working with Probability) and the online appendices were far more engaging and challenging given my starting point. I found myself not getting hung up on the numerous detailed equations because I knew that R would take care of the math for me. But the equations and the explanations that preceded the R code gave me the confidence that I could conceptualize or construct my own cases where the mathematical tools and tests would be useful. Schmuller has given me a trail I can walk in the jungle while guaranteeing safe exit. It is up to me find the trails that branch off deeper into the thickets and then go exploring.

For me, then, this book acts as a way to comfortably see more of what our world has to offer while also inviting me to take off on my own. It serves as a way to acknowledge that there is a wild world out there that I don't yet understand. It makes me see that the comfort of treading the streets and avenues is a false security because the important stuff is out there where I run the risk of getting lost or hurt. But I also run the risk of not finding anything. There may be dragons or there may be just my mind playing tricks on me. Either way, there is adventure.

Kathryn Schulz' Being Wrong - A Short Review

 "To f*ck up is to find adventure." I wasn't sure if I wanted to keep reading Being Wrong: Adventures in the Margin of Error ...