Monday, March 8, 2021

We're Ruining Mindfulness with How We Do It: Ronald Purser's McMindfulness - A Short Review


We have a mindfulness problem. But it isn't the one you would think of, or that popular culture and media would have you think of. We have the most ironic of mindfulness problems. We think we know what it means to be mindful: to be aware, to be present, to be in the moment. We've listened to countless soothing voices remind us to bring our attention back to our breath. But for what, exactly?

I certainly don't take the position that mindfulness serves no purpose or that it doesn't have any benefits. But I do agree with Ronald Purser's main claim, that mindfulness is supposed to be rooted in something larger than ourselves. I think that having clarity and focus is important but why should we seek to be clear and focused? Purser shows how mainstream mindfulness has incorporated the Puritan work ethic to its detriment. That work ethic tells us that we can achieve anything if we work hard enough but when we apply that to mindfulness, we drive ourselves to be mindful only of ourselves. We learn that controlling ourselves is all we need to do. But that, says Purser, is not what mindfulness is.

The author makes many incisive observations about how McMindfulness has neutered what mindfulness is meant to be and about how mindfulness has become a tool of corporate interests. And while I agree with his arguments, I can't provide more or better insight about those areas. What I want to share are my thoughts on how McMindfulness has influenced how we function as coaches and how we interact with others, especially athletes in our care. As coaches are wont to do, we have taken a complex framework and turned it into a drill that we do in practice. We have flattened the contours and nuances of mindfulness into a thing that we can plug into our practice plans rather than keep the complexity and have it change how we view coaching.

To be clear, I think that a simplified version of mindfulness is beneficial in sports and performance. While my interest in ecological dynamics and related theories of skill acquisition may lead to differing opinions of what we should be mindful of, I still think that coaches and athletes alike can free themselves to perform better by applying ideas of mindfulness. The issue is that mindfulness means so much more and I can't turn away from that. It is meant to have us better understand our place in the world, not just our place on the court. It is meant to position us to do something about the conditions we see in the world. When we use self-awareness mindfulness techniques divorced from their roots in social and community awareness, we miss the point of mindfulness. We use these techniques to slow our thoughts but we do so to make space for small actions like jumping higher and moving quicker or "better". Purser is reminding us that the purpose of stillness of mind is to give us more clarity on what exists around us and how we are perceiving and interacting with everything outside of us but we can only do that by first making space within us. We can then use that space to be part of big actions too.

So are we using mindfulness in teams to help us do something to something like a ball? Or are we using it to do something with those around us? Are we using it to be better athletes or better teammates and better humans? I don't think we should just do a minute of focused breathing so that we are better at ignoring external "distractions". I think we should use the breath as a way to see our team and our place in it more clearly. I won't mistake a feeling of stillness for a feeling of peace. To me, the peace comes not from a quiet mind but from right living. The breathing gives me a chance to reflect on how I am living. I can see if I am working together with my teammates in meaningful and positive ways. Part of that reflection may be that I need to do my job better but I think that should come after the reflection on how I am integrating with my team. If I can't or won't be mindful of my part in the larger whole then I am putting my ego before all else, which is antithetical to a mindfulness practice.

I also think that using mindfulness practice is an opportunity to create awareness of things that exist outside of our teams as well. I think that we should be asking ourselves how our coaching and playing sports makes an impact on the larger community and the world. How can we use who we are and what we do to be meaningful parts of our community? If creating stillness and peace within ourselves and our teams means that we must do work outside of sport then I think we have taken Purser's words to heart. I am not saying that sport should be only an avenue to social justice but I do want to say that mindfulness in the Buddhist tradition means that all of these lie along the same path.

I have been thinking lately about the communities in which we practice: our teams, our opponents, our spectators, our histories, our futures, and ourselves. I have been wondering what it is that we are creating, recreating, reproducing, passing on, and adding to. When we say we want athletes in our care to grow and develop, what do we want them to grow and develop towards? How do our daily choices and actions contribute to that? How can mindfulness be part of that vision? Let us meditate on that.

Saturday, December 19, 2020

Academic Quick Hit: Ventral and Dorsal Contribution to Visual Anticipation in Fast Ball Sports - Simon Bennett, ed 2008

Where I attempt to give a quick summary and opinion on an academic paper that connects to teaching, learning, and/or sport.

Why I think this paper matters:
Information Processing and Ecological Dynamics are two separate schools of thought in motor learning and their proponents are well-entrenched in their positions. The two-stream system may well be an opportunity for each side to compromise a little in the name of more accurately describing how skilled movement happens.
"In the course of action the activity of the ventral and the dorsal systems must be synchronized in a meaningful way such that they can work together. The interaction is reciprocal in that the ventral system constrains the contributions of the dorsal system, and the dorsal system may also affect the workings of the ventral system" (p. 120).

Bennett, S. (2008). Special Issue: Ventral and dorsal contribution to visual anticipation in fast ball sports. International Journal of Sport Psychology, 39(2), 97–177.

Type of Paper: Special Issue with target article and six commentaries
Occasionally, a journal will focus on a specific topic for an entire issue and sometimes that topic is a specific article that all the other articles in the issue respond to. In this case, the target article is "Ventral and dorsal contribution to visual anticipation in fast ball sports" by John van der Kamp, Fernando Rivas, Hemke van Doorn, and Geert Savelsbergh.

Highlights (target article):
- The authors of the target article are building on the work of Milner and Goodale, who have written books and articles about the "two visual stream system". There is a great deal of neurological evidence that visual information enters our eyes and then goes in two different "directions", ventrally and dorsally, once it hits our brains. Each stream specializes in different kinds of information.
- "The ventral system is involved in perception of objects, events, and places. As the ventral system gains knowledge about what the environment offers for action, it can also contribute to action" (p. 102).
- "The dorsal system is designed to visually guide movement execution" (p.102).
- The authors' use of the term "knowledge about" is important because it contrasts with "knowledge of" the environment. They intend to show that each system is better at managing certain kinds of information. I think this is an opportunity recognize that each system needs the other in order for an athlete to perform many skills, especially in ball sports.
- The ventral system is allocentric, or world-centered, while the dorsal system is egocentric, or body-centered. These terms further reinforce the distinction between "knowledge about" and "knowledge of" the environment.
- For me, the most important part of the target article is figure 3 (p. 109) in which the authors propose that "in the course of action the ventral and dorsal systems show parallel engagement". So the two systems work alongside one another.
- The authors show that many experimental results that are intended to support the information processing point of view are likely incomplete because they only allow the implementation of the ventral system.

Highlights (Abernathy and Mann article):
- The fact that many experimental results don't engage the dorsal system has been "largely a consequence of methodological constraints rather than necessarily a strong, conscious commitment by researchers" to uphold a particular philosophical view (p. 137).
- A main difference between experts and novices is probably that the expert's dorsal system is better attuned to their environment (p. 138).
- We must be careful to recognize the difference between errors of "poor pick-up of advance information" and errors of "poor response selection strategy" (p. 140). Did two athletes see and interpret something differently or did they see the same thing but selected different solution strategies?
While there were plenty of ideas I took away from the other commentaries, I think they are more specific to my personal learning so I won't take anyone else into the weeds with me.

What I'm left wondering:
- The commentaries are written from an Ecological Dynamics viewpoint. What would Information Processing commentaries look like?
- In volleyball, we often talk about defenders needing to be stopped/balanced when the opponent attack happens. I find information in the target article that suggests that movement during opponent contact could be either helpful or hurtful. How can I better understand when movement at opponent contact is useful?
- Ara├║jo and Kirlik's article draws a distinction between "representative design" and "ecological validity", writing that many researchers use the latter term when they are really talking about the former (p. 163). I still don't think I understand the distinction.
- Probably the largest question I am left with is if the two motor learning camps have made any movement towards each other and if the two stream system is part of such compromise. As primarily a practitioner, I tend to be more pragmatic in my approach to these theories. I want to use what gets results, meaning the best learning and skill performance. I don't know how to do that in a way that doesn't run afoul of one of the two world views. A coherent, consistent framework is very important to me as a coach so I find this dissonance difficult to reconcile within the realm of my practice.

Saturday, December 5, 2020

Academic Quick Hit: Wayfinding - Woods, Rudd, Robinson, and Davids, 2020

Where I attempt to give a quick summary and opinion on an academic paper that connects to teaching, learning, and/or sport.

Why I think this paper matters:
- Using the metaphor of wayfinding for skill adaptation can be a very helpful tool for translating from formal academic language to something easier for practitioners (coaches, teachers, learners) to absorb, understand, and apply.

Woods, C. T., Rudd, J., Robertson, S., & Davids, K. (2020). Wayfinding: How Ecological Perspectives of Navigating Dynamic Environments Can Enrich Our Understanding of the Learner and the Learning Process in Sport. Sports Medicine - Open, 6(1), 51.

Type of Paper: Review/Opinion
The authors give readers a new way to think about the process of skill adaptation. They build out the wayfinding metaphor and how it can be used to think about learning movement and, more importantly for coaches, how it can be used to approach teaching movement.

- A definition of wayfinding: "...wayfinding is an activity that confronts us with the marvellous fact of being in the world, requiring us to look up and take notice, to cognitively and emotionally interact with our surroundings" – M.R. O’Connor (p. 1)
- Wayfinding is contrasted with transport, "where an individual is more interested in reaching a pre-planned destination by transiting 'across' a landscape, as opposed to moving 'through' a landscape" (p. 3). It is more important to experience the landscape.
- The idea of knowledge of is contrasted with knowledge about the landscape in which the wayfinder moves. (To be clear, the "landscape", in this case, is metaphorical, like a "solution space" or a realm of possibilities. This is not talking about how the learner actually moves through physical space.)
- The role of the teacher as landscape designer, in which they create opportunities for learners to "learn to learn how to move" rather than problem-solvers for the learners.
- The role of the teacher as asker of questions rather than explainer of answers to the learners.
- "...wayfinding isn’t knowing before we go, but, knowing as we go" (p. 10)

What I'm left wondering:
- How do I know if the landscape I design is working? If learning is nonlinear and questions should have physical answers instead of verbal ones (p. 8), how do I assess that the learners are seeing what they need to see? What if they are focusing on useful sources of information in the environment but moving in ways that don't get them closer to their goals? How do I get at that discrepancy?
- How does this metaphor fit in with tasks that are less physical (more cognitive) in nature, like remembering an opponent's tendencies?

Saturday, October 31, 2020

The Same, Only Different: Reclassifying Serve Reception in Volleyball

I wrote about serve reception in a post a couple of years ago (read it here) and my thoughts there were a bit more philosophical than technical. I want to build on some of the ideas in that post and add some data before giving some ways to integrate the ideas I present. So here goes...

I have been thinking a lot about how we evaluate serve reception since at least when I wrote that previous post and this spring and summer finally yielded some productive ideas that I am looking forward to integrating into my team's training and competition. I'm not changing how I grade receptions but I am expanding how I think about serve reception to include its contribution to scoring points. This expansion is a product of treating all non-terminal skills (receiving, setting, digging) not as isolated skills but as opportunities to either make it harder or easier for our team to score on the next attack. In the case of serve reception, pass average and in system percentage both treat passing as an isolated skill so how can we incorporate scoring into our passing evaluation?

Before I get into that, I think it is important to quickly look at how I grade passing. To be clear, I don't think that my way of grading is better than other ways, it is just an expression of what I think is important and that can vary from one program or scout to another. I think it is important to explain my grading because it influences the data that underlies everything else I'm writing about. First, I grade on a four-point scale so a "four" is a ball passed within a step or so of the setter's "perfect" location while also allowing the setter to be in a desirable posture. Threes, twos, and ones are basically determined by how many options I think the setter reasonably can set on the pass (there's a difference between can and should, which I wrote about here). I use Data Volley's R/ grade for "one-half" receptions, which are passes that are kept in play but the receiving team cannot take a swing. This grade is useful to make "one" grades more connected to scoring points without being affected by the noise of shanks and overpasses that aren't aces.

The data set that I'm using is the last three years of Pac-12 matches for the University of Colorado, where I am the Technical Coordinator. While the data visualization below contains the data for Colorado's opponents as well, I am going to focus on my team. The graph is built in Tableau, which is a really fun and powerful data visualization tool. It is highly interactive, so click around and enjoy.

Let's remember that I said scoring is what drives my work here. Reception is a step towards scoring, which is what really matters. Passing well is nice but good passes are ultimately useful because they make scoring easier. So let's approximate how well my team scores after different reception grades. The graph below is built on reception grades (x axis) and expected first ball efficiency (y axis). To find expected first ball efficiency (xFB), we'll need the number of times each attack outcome (K, 0, E/B) occurs following a particular reception grade and the number of times that reception grade occurs. xFB will be calculated in the same way we calculate attack efficiency with one crucial difference. I am using reception attempts as the denominator rather than attack attempts because this calculation is about the passer rather than the attacker so I want to include receptions that don't have an attack that follows. Each reception grade has its own calculation so each team has five points on the graph and these points correspond to the xFB for that reception grade for that team. I then asked Tableau to show curves to relate each team's data points to one another so each team has one curve that roughly links their five points together, giving us a sense of how the values change as we move from grade to grade. The xFB changes from grade to grade are what triggered my thinking around reception evaluation.

The conclusions I draw from this visualization are not exactly universal but I think that they are similar enough that my conclusions about my team can be useful for many other teams. Let's start at the top end of the grade scale, fours and threes. Better than almost any other team in the conference, Colorado shows that there is little difference between these two kinds of receptions in terms of how well we attack after such a reception. That makes sense because there is probably less difference between what fours and threes look like than the difference between any two other grades. (This could be an argument against scoring on a four-point scale but that's not what I want to focus on here.) We see a big gap between xFB on threes/fours and xFB on twos, which is important because that means twos are clearly different than threes and fours in terms of our ability to score. We score less on our first swings when we pass twos than if we pass better. Just like with threes and fours, this makes sense but it is important to see how large a difference in xFB there is (almost 100 points). It is worth noting that, even though there is a drop in xFB, we can still be reasonably successful hitting .250 in our first ball offense but we start to put more pressure on other aspects of our game. There is an even larger drop between twos and ones (from .250 to .104) and now we have entered dangerous territory, it will be really hard to win if we are only hitting .100 in first ball. Not every team in the graph shows the same grouping of xFB values but there is almost always some kind of grouping that is apparent. I think that if I used more than 5-6 matches of data for the non-Colorado teams, the groupings would be more clear but I'm focusing on my team and extrapolating out from there. The important thing to take away from this is that there are reasonable ways that I can group reception grades together when I consider how well teams attack after those reception types.

I see three groups of receptions for CU: ones after which we hit well over .300, ones after which we hit mid-.200s, and ones after which we hit .100 or worse. I see these three groups as being either favorable for, slightly below average for, or poor for scoring. I could easily stop here and just assign new number values to my reception grades but that doesn't connect the skill to scoring in any meaningful way. Why not just use In System Percentage (IS%) to express the same idea? I think there are two reasons, IS% doesn't connect to scoring and IS% ignores that we can still win passing twos. IS% is certainly useful but it doesn't accomplish what I'm looking for. After thinking about alternative scales, I arrived at Green-Yellow-Red (G-Y-R). This scale separates us from reception average-type numbers and gives us a sense of the situations that attackers find themselves in after a reception: favorable, questionable, and difficult. G-Y-R allows me to continue grading serve reception in the same way I have been, as an expression of the number of front row attacking options that are available, but now I have a way to talk about how reception affects scoring.

Here are two different visualizations that are showing almost the same thing, G-Y-R reception frequencies for opponent passers. Each bar in these stacked bar charts represents a different passer and the colors represent how often that type of reception occurred for that passer. The numbers at the top of each bar are numerical expressions of each passer's G-Y-R. The difference between the two graphs is that the first shows gross counts so we can see which passers received the most serves while the second shows receptions as a percentage so that each passer's performance can be easily compared to that of another passer. I show both because I think it is interesting to see if there are particular passers that are above/below average in terms of their number of attempts and I also think it is important to be able to make comparisons regardless of usage rates. I created these plots in R and I am happy to share the code with anyone interested.

So what does a good passer look like in G-Y-R? The obvious answer is that more green is better, as is less red. But that generalization, like IS%, ignores the yellow, the in-between cases, that can make or break teams' first ball success. Compare Player 13 and Player 80. They have almost identical G% but Player 13 has 10% more Y than Player 80. That means that Player 80's team is going to be hitting around .100 10% more often than Player 13's team when each of them passes. That's a difference in siding out that I want to be aware of and I wouldn't see it if I only looked at IS%. But that comparison doesn't answer the basic question of what a good passer looks like in terms of G-Y-R. I think that 50-30-20 would be the sign of an elite passer. In the sample above, Player 41 and Player 89 are, in my opinion, the best of the bunch. There were passers in my sample that met my criteria of 50-30-20 but they had fewer than 40 receptions, likely because we thought they were good passers and tried not to serve them much.

Let's look at data from a single match to see what G-Y-R can look like. The image at right is of a simple Data Volley worksheet I built to compare reception average, xFB, and G-Y-R. The data make it pretty clear that all three are pretty tightly related. The upper team in the worksheet passed 50% of their receptions at a grade of one or lower and that distribution was reflected in the low xFB and reception average values. Meanwhile, the lower team had 50% G and had xFB and reception average values that were in line with that. If the numbers are all so closely aligned, then aren't xFB and G-Y-R superfluous? I don't think so because I don't think that reception average gives us a clear indication of how a team should score. I don't like relying on xFB because it is too easy to conflate its value with reception averages. (Look at how closely the values of the two measures can seem.) Both xFB and reception average boil a passer's performance down to a single number which removes valuable context. Passers are not going to pass every ball the same but xFB and reception average give us the sense that every ball will be the same because they each give a single value. G-Y-R helps us very quickly understand that passes will be different and gives us a sense of how the passes will be distributed. Compare two passers on the upper team, one at 1.97 and the other at 2.00. If I was deciding who to serve at based only on those reception averages, I might be inclined to pick the lower average but the G-Y-R gives me an interesting insight. The 2.00 passer passes 5% more R than the other so I can give my team a much better chance to earn a point 5% more often if I concentrate my serves on that player instead of on the 1.97 passer. Compare that player to the 2.00 player on the lower team whose G-Y-R is 20-60-20. The dramatic differences in Y% and R% are reflected in a 30 point difference in xFB. These are important differences that affect a team's ability to win points in first ball.

I think G-Y-R can also have an impact on how we coach our athletes. I don't think that it changes how we want our athletes to look and move but I do think G-Y-R should shift our interest and energy when working on reception. Knowing that there isn't much difference between a three and a four in terms of scoring, how much energy should we put into improving a pass' location from 6-7 feet off the net to 1-2 feet off? But what about improving pass location from 11-12 feet off to 6-7 feet off? That's a potential difference of 100 points in hitting efficiency. How important is the height of a pass? A low pass to the center of the court may not prevent our setter from getting to it but it may prevent her from setting it with her hands. Passing a ball to that same location but higher can therefore mean a 150 point increase in xFB. To me, this is a different perspective on improvement. Improvement can mean raising the ceiling on our performances, that our average performance improves because our top scores consistently become a little better. G-Y-R gives us a way to quantify the benefit of raising the floor, making our lowest performances better. G-Y-R suggests that raising the floor would have a much bigger effect on our ability to side out than just seeking general improvement that is reflected in reception average.

G-Y-R also give me an alternative way to think about scoring reception games in practice. We play a serve receive offense game at Colorado where points are awarded based on the quality of our pass and the outcome of the first ball attack. We have been using traditional reception grades for scoring but that's going to change so that our team can more clearly see the effect that reception quality has on side out offense.

I am looking forward to exploring G-Y-R with my team in the coming season. It could be the beginning of an important shift in our thinking towards valuing non-terminal skills in relation to scoring.

Sunday, August 23, 2020

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 until Schulz made this point at the end of chapter two. I thought all of chapter one and most of chapter two were too abstract and philosophical for my taste. But then, in the last pages of chapter two, she set the hook and both my highlighter and I were in. That's when she wrote about the medieval knight errant and reminded readers that the Latin word errare, meaning "to roam", gave rise to the English words "error" and "errand". The English word "errant" can be used to express either idea. But enough about etymology. To be errant is to be wrong but also to be on an adventure.

In this spirit, Schulz gives us the 'Cuz It's True Constraint: we believe what we believe because our convictions (we believe) are based on facts. We're willing to accept the general notion that our beliefs may be biased, but if we pick a particular belief, we'll probably say, "but not that one". We don't believe things because it makes us feel better, smarter, or more in control. That's what other people do. Wrong is something that other people do and they do it for the wrong reasons. Schulz points out that even when we do admit we were wrong, it immediately becomes past tense because we no longer believe the same thing. We were wrong.

So why do we make errors? Because our brains are really good at inductive reasoning. We learn language by hearing others conjugate a verb a few times then we go out and conjugate it the exact same way in all situations until someone points out that we say "took" instead of "taked". We are all built to work on assumptions and we don't realize when that gets us into trouble. As Schulz phrased it, "every one of us confuses our models of the world with the world itself" (p. 107).

We silently substitute how we think the world works for how it actually works and then quickly convince ourselves of the certainty of our model. So if my model is how the world works and I see that your model is different, then your model and, therefore, your world is wrong. I can see all the ways that your world is wrong but I can't see how mine is. I see your certainty as laughable but my own as righteous. (It is worth remembering that this book was published in 2010 and written earlier than that so we now have a decade of social media to show just how right Schulz is about how we treat being wrong.)

To me, the beauty of the book is that Schulz doesn't turn it into a how-to on not being wrong, nor does she pontificate. She writes an ode to and explanation of "wrongology". The closest Schulz gets to telling readers what to do about being wrong is suggesting that it is about far more than getting our facts straight.

But facing up to the true scope and nature of our errors is also (and more self-evidently) psychologically demanding. Crucially, these two challenges are inseparable: if we can't do the emotional work of fully accepting our mistakes, we can't do the conceptual work of figuring out where, how, and why we made them. (p. 207)

There is a vulnerability in being wrong and it scares the crap out of us. So fixing our mistakes means fixing ourselves as well as our facts. But Schulz describes an "optimistic model of wrongness" in which error is a force that "imperceptibly human beings - to grow up" (p. 289).

So what does this have to do with coaching? Nothing. Everything. I take this book as an invitation to admit to and talk about being wrong. How does my wrongness affect my decisions? How does it affect how I treat those I see as wrong? How do I deal with my mistakes when they are made apparent? How do I treat those that show me my errors? As coaches, our relationships with our athletes (and so many others) are built on trust and a power imbalance. Our positions of power can raise the stakes on the errors we make. Do we rely on our power to make things "right"? Do we rely on some version of "because I'm the grown up and I know better" to paper over our misjudgements? When we rely on our power to manage our mistakes, we do so at the expense of the trust placed in us. Our models of the world encourage us to be certain we have done the right thing so we don't understand why faith in us erodes. That's not the place I want to find myself anymore. I'd rather be wrong. After all, that's where the adventure is.

Friday, August 21, 2020

When You Realize You're Not Even Close - Volleyball in Light of Skinner and Goldman's Optimal Strategy in Basketball

Skinner, B., & Goldman, M. (2015). Optimal Strategy in Basketball. ArXiv:1512.05652 [Physics].

You know the feeling when you taste a new dish that is so good that you keep repeating to your friends how good it is after each bite?
You know the feeling when someone shows you the difference between what you've been doing and the way it should be done?
Have you ever felt both of those at the same time?

That weird mish-mash of thoughts and feelings is the result of reading Skinner and Goldman's "Optimal Strategy in Basketball". Add to that the dismay of knowing that they wrote it five years ago and I just discovered it now. The only thing that eases that dismay is the knowledge that I wasn't anywhere near prepared enough then to do anything about what I read. While I still may not be prepared enough now, I can at least see how to get there from where I currently am.

So what was so good about the paper that I couldn't stop telling my friends? The introduction was all it took. The authors clearly summarized what my friends and I have been fumbling around for months, if not years.
- The score difference fluctuates somewhat randomly throughout play.
- That net score difference is determined by the skill levels of the teams and the scoring strategies they use during play.
- There are three main principles that go into determining what the optimal use of scoring strategies should be.
Those ideas may not seem so earth-shattering but that is the amazing part. If those ideas are so plain to see, why hadn't I seen anyone else nail these down in some formal, empirical way until this paper? It's not until you try to explain something complex to others that you realize just how little you truly understand it.

That's where math starts to come in. The first of the three principles to consider is "allocative efficiency", or determining the frequencies at which certain players/plays should be used in order to maximize scoring. The authors demonstrate that the answers are not as simple as we think they are. It isn't as simple as using the average points scored by each player/play. We need to understand the marginal points scored. Marginal rates are derivatives. They explain how large the changes are between data points. The question shouldn't be "how many points do we expect to score running this play?" The question should be "how many more points would we expect to score if we ran that play one more time?"

The second principle is "dynamic efficiency", which the authors explain as an "optimal stopping problem". To maximize dynamic efficiency, teams must choose which shots to take and which ones to pass up within a given possession. Teams should shoot when they reach a point at which the expected value of the current shot is greater than the average expected value of continuing the possession. The expected value of a possession keeps going down as the shot clock keeps winding down so earlier shots are typically better than shots in identical circumstances later in the possession.

The third principle is "risk and reward". We can easily understand that there is a trade off between these two but the authors quantify this trade off in an insightful way. Increasing "risk" means that we decrease our chances of scoring our "average" number of points in order to increase the chances that we score a number of points that is much higher than our average. The downside of increased risk is not only the decreased chances of scoring our average but also an increase in the chances of scoring far less than our average number of points. So why would we risk, given how it negatively impacts our scoring? If we remember back to the introduction of the paper, we should care about the difference in scores more than how many points we score. If our opponents are likely, based roughly on average scoring, to outscore us then we have to take some chances to possibly increase our scoring. In statistics-speak, we are trying to fatten the tails of our scoring distribution. In COVID-speak, we are trying to flatten the curve.

So how do I think we can apply these ideas to volleyball?
I'm very early on in developing my thoughts on this but I view this post as a chance to sort of think out loud, which I find helpful.

When it comes to "allocative efficiency", I think that this encompasses both offensive play selection as well as setter choice. While there may be some play selection in transition offense, I think that, just by sheer volume of opportunities, we're mainly considering side out offense. Setter choice is always going to be constrained to some extent by reception/dig quality, which is only the beginning of the deep complexity involved in figuring this out. Let's start by considering a given play in a three-hitter rotation. We shouldn't just consider the attack efficiencies of the three front row attackers when running that play, we should compare how efficient they are as they are set more and more in that situation. We're looking to determine how to get the most expected scoring by leveraging each attacker as much as possible before their skill curve deteriorates too much. The better the individual attacker, the more the distribution will skew towards them. To some extent, we set different options to "keep our opponents honest" but mathematically, we are trying to maximize expected scoring. This is also an example of how the score fluctuations in a match are somewhat random. There are many factors that contribute to these situations and who the setter chooses to set can vary randomly within our maximal scoring scenario. We can achieve our allocative goals without strictly scripting what happens on every play. I think that allocative efficiency can also influence setter choice on less-than-perfect passes. To further complicate our thinking, this would mean not just knowing how to maximize scoring on a certain play with certain attackers but also how that efficiency may change as reception quality deteriorates. The abilities of the attackers determine this to some extent but there is always an element of allocation to consider.

I think that we need to understand how marginal scoring rates change in all of these situations before we can understand how to allocate most efficiently. I suspect that we currently only truly understand average scoring so when we start considering complicated situations, our thinking reverts to "set the attacker who is most efficient on average" which will not result in the best allocative strategy.

I think making direct "dynamic efficiency" comparisons between basketball and volleyball is a bit tougher mainly because our time constraints are much different than theirs. The rules of volleyball prevent lengthening possessions. With that said, I think that there are some interesting comparisons to be made. First, there is a constraint in terms of how close teams are to the end of a set. I have seen this referred to as the "red zone", or after 20 points. I have also seen research suggesting that 18-15 is a kind of point of no return, in that the trailing team is very unlikely to win the set under normal circumstances. We can study dynamic efficiency as a principle that governs decision-making as teams approach the end of sets. How should decision-making change as teams near 25 points? Does attack efficiency change in some important way as teams near 25 the way that expected points change as the shot clock or game clock nears zero? What impact is there on dynamic efficiency when a team is leading versus trailing? (Is that question really more about risk and reward?)
What about considering rotations as part of dynamic efficiency? Should teams be weighing how to shorten time spent in weaker rotations and lengthen time in stronger ones? Is this a different question from allocative efficiency? Is attack choice (line/angle, hit/tip, etc.) a dynamic choice, allocative choice, or both?

"Risk and reward" is probably the most straightforward because it is a meta-strategy so it isn't tied to how the game is played. Risk and reward is about choosing strategies and about appetite for risk. There are plenty of stories in the sports canon about underdogs adopting radical strategies to overcome heavy odds. I don't think that volleyball coaches consider themselves separate from such ideas. In discussions that I have had with friends about this subject, I have framed this in terms of women's collegiate programs. How does a college coach think about risk and reward? How willing is any given coach to gamble on less-certain strategies? It's not that the strategies don't work (we need to be wary of dualistic thinking and resulting), it's that the strategies are much more variable. Do coaches who are willing to risk have any data to support how much variance accompanies a given strategy? How frequently can a coach utilize such strategies and keep their job? Variance means getting a much wider variety of results (in terms of scoring, not necessarily outcomes) and that means living with much less certainty, which can be exhausting.

But what if you coach a college team that can usually make it to a postseason tournament but has very little chance of advancing far? Would you consider risking more, especially given that there may be little chance of overcoming the gap between you and your opponent? What if you coach a team that makes the tournament every year, advances a round or two and then needs some luck? How many years would you be conservative and hope that the stars align to make it to the finals?

And then there's the question of how do we risk? What strategies are available to our teams that we aren't currently employing to score? Aren't we trying to practice those things that we can't control well enough yet so that we can control them better? Is risking all about tailoring strategies to fit certain opponents beyond the small tweaks that we usually make? Is it about putting more eggs into a single basket than we feel comfortable doing? Do we have to risk in all facets of the game (serving, attacking, blocking, etc.) or is deploying a large change in one area enough? Can we risk with certain personnel decisions rather than team strategies?
And how would we measure the risks we would take? We need to employ strategies that we trust either fatten tails or shift curves in a favorable direction. Blindly risking is still risking but we can do better than that. We should gather some kind of data on the potential strategies, whether those come from other teams or our own team in situations where we are favored enough to experiment. It is a lack of rigor that can give risk a bad name.

There's a lot of research to be done, data to be gathered, and numbers to be crunched to get a handle on these ideas. But before we do the math, we still need to decide how to translate between what we know about volleyball and what Skinner and Goldman have taught us about basketball. Are there areas unique to volleyball that the authors didn't have to account for? Are there arguments they made that won't hold up in our game? We have some substantial (but manageable) theoretical work to do before the applied work. Skinner and Goldman did the theoretical work and began the applied work but then ended their paper with this massive caveat:
At a practical level, the biggest hindrance to quantitative basketball strategy is usually the difficulty of accurately estimating the efficiency of different offensive options. The usage curves f(p) are particularly difficult to estimate from easily-measurable statistics, and are necessary for a quantitative determination of optimal allocation. What’s more, the usage curves are really only robustly defined relative to a particular defense, and can vary strongly depending on the quality of the team’s opponent. A major advance in their determination may therefore provide the most important step toward enabling quantitative optimization of basketball strategy. (p. 14)
So it's going to be a long haul. We're not even close. But we can get there from here.

Monday, August 17, 2020

Academic Quick Hit - Acquiring Skill in Sport: A Constraints-Led Perspective - Davids, Araujo, Shuttleworth, and Button, 2003

Where I attempt to give a quick summary and opinion on an academic paper that connects to teaching, learning, and/or sport.

Why I think this paper matters:
- The authors, leaders in the field of ecological dynamics, give a brief summary of the constraints-led approach (CLA) framework of skill acquisition.

Davids, K., Araujo, D., Shuttleworth, R., & Button, C. (2003). Acquiring skill in sport: A constraints led perspective. International Journal of Computer Science in Sport, 2(2), 31–39.

Type of Paper: Review/Opinion
The authors weave a short literature review together with their views on how to best structure learning environments for skill acquisition.

- Active participation and concentration on exploring the solution space by the learner is better for skill acquisition than satisfying task demands prescribed by a coach. (This can be loosely thought of as discovery learning.)
- There are three main task constraints that coaches can manipulate: equipment, practice structure, and augmented feedback (p. 33) (Augmented feedback usually refers to feedback from a coach rather than from performing the skill alone.)
- "practice structure should emphasize 'task simplification' rather than the more traditional technique of task decomposition" (p. 34) (Simplifying the whole skill rather than teaching "whole-part-whole")
- Feedback from coaches should use external focus of attention (from Wulf's OPTIMAL theory) and should be used more infrequently.

What I'm left wondering:
-How effective is external cuing as compared to internal cuing? Are there differences in the rates of improvement?
- How would I work with a novice athlete learning to serve? How would I use external cuing and different constraints to develop the skill? I know how I used to do it but incorporating a CLA means coaching very differently.

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