 Everyone, this is Rob Gray from ASU, the Perception Action Podcast and the author of the new book, How We Learn to Move. In this presentation, I want to talk about kind of taking an ecological approach to cricket, right? Both in terms of practice design, coaching, and what I want to do in this presentation is I'm going to kind of introduce the main ideas of the ecological approach, talk about relevant cricket research. I'm also going to bring in work from baseball that I think is important, kind of testing these ideas. Then I talk a little bit about research that has looked at different ways of practice design, including some of my own. So that's my goal for this presentation. So first off, the ecological approach. Let's give the basics of what we mean by that and how it differs from the traditional one to skill acquisition. And the way that I like to explain this and always start is to talk about variability, how the importance of variability and the role it plays in skill, both in terms of developing skill and in terms of performing, right? And for this, I want to talk about the relationship between three types of variability. Performance outcome variability, right? How consistently do you produce the goal you're trying to achieve, right? How consistently do you bowl the ball to the spot you're aiming for, right? In all theories, all theoretical performance approaches, we want that to be low, right? We want to consistently produce good performance outcomes. The other types of variability are movement variability, right? How you move your body to achieve that goal, to get to ball to that place with that spin with that speed, right? So we're talking about kinematics, how your arms are arms moving, how your elbow and wrist, what they're doing. That's movement variability. And then we have the variability of practice, right? How much we're varying the conditions within practice to promote skill acquisition. And what we're going to find is, of course, these two are the way the big differences in theories happen, right? So the traditional approach to skill acquisition is the idea that there's one ideal technique, right? There's one ideal way to bowl. There's one ideal way to hit that we're going to teach you through rope repetition, right? So here's how you swing a cricket bat or a baseball bat. We're going to teach you that, and we're going to have you try to repeat it over and over till you get the basic fundamentals and technique down with the coach correcting you. Nope, you're bending your elbow too much. No, your wrist isn't straight enough, et cetera. We're going to give you lots of body-focused cues. And the main fundamental idea is that second type of variability, the movement variability, it's bad, right? It's noise, right? Being inconsistent in your swing technique is a bad thing. We don't want that. We want you to try to produce the exact same swing every time when you're hitting the ball or you're back saying the exact same delivery when you're trying to do a fastball and cricket. We want the exact same body kinematics, what your arm's doing, right? Because variability is noise, right? In terms of what we're going to do with variability in practice, I like to distinguish between what I call adjustability and adaptability, so the main goal of adding variability of practice in the traditional approach is adjustability. So what we're going to do, for example, in cricket is we're going to teach you the batting technique, how to swing the bat. We're going to do it by having, I'll look at some of the ways we do it, but imagine bowling the same ball over and over again with the machine until you get the technique down, right? So you develop a motor program, an internal model. What then what we're going to do is we're going to vary the condition so that you can adjust this internal motor program, this basic technique, the fundamentals you use, so that you can hit a short ball and a long ball, right, and one with spin, right? So what we're doing is giving you the basics first, then learning to adjust it. Another example I like to use is golf, right? We teach you to swing a golf club by having you hit the ball on flat ground over and over and over and over again. Then we go take you to a downhill lie and say, here's how you adjust that basic technique, that motor program you've learned to hit a downhill lie. So what we're trying to achieve is repetition with repetition, right? We're trying to get you to produce the same swing, the same, despite the fact there's going to be variations in conditions, OK? So for the implications of how we use variability of practice in the traditional approach, we want it later on, right? We want you to develop the fundamentals first. We're going to achieve that by breaking the skill apart and I'll get that to that in a second. For example, in baseball, we're going to hit you off a tee first because we want you to get the basic motor program of swinging, being able to repeat it, having a repeatable swing. Then we're going to add variability to and you can learn to adjust your one swing for low pitches, high pitches, curve balls, fastball. We also, of course, are going to use representative conditions in that one that we're varying, right? We want you to be able to adjust to things that are really going to happen. Different spins, different depths of pitches, right? We're not going to teach you to adapt to a different size cricket ball, right? Why would we do that? There's nothing ever happens in the game, right? So we're going to adjust to things you'll actually face in the game. OK, so the traditional assumption overall of how you relate those three types of variabilities is we want to produce low outcome variability, so consistently hitting the ball square on the cricket paddle or square on a baseball bat, which is what you want. We need a repeatable swing. We need to be able to keep our swing the same as much as possible. And we do that by focusing on developing the mechanics, the technique, the one ideal swing through repetition and practice. So low outcome variability is produced by low movement variability because variability is noise developed by relatively low practice variability, right? Another way, one of the main ways we achieve this, right? And I've talked about this already, is through task decomposition, right? To get you to learn the one swing, and this is a great quote from Ian Renshaw, what we're going to do is break the skill of swinging into pieces. We're going to decompose it. We're going to teach you each of these parts, and then we're going to put it back together, right? And a great example of this in cricket is the drop feed, right? So you have a person hitting their drop in the ball. What we've done is break the skill of swinging apart, right? The batter does not have to respond to an actual pitched ball, right? So we've broken, and what we will see in a second, broken the perception action coupling, right? The batter doesn't need to perceive anything really to hit other than the ball dropping, which is not realistic information. They'll have to hit. They're not making any decisions at all, right? They have to swing every time. They're not being, they're not adjusting to the depth of pitches. Another one is the throwdowns, right? These are examples of we're going to break the skill apart, teach you the pieces so you can build them back up into the real game, right? That's kind of a fundamental idea in the traditional approach. OK, so what's the alternative, right? The alternative comes largely started with Nikolai Bernstein in his work. The idea that there's actually not one ideal technique. There's multiple ways to swing, right? And each individual needs to find their own one that works optimally for them, right? Through being adapting to different constraints. So what we're going to do is let you self-organize, right? We're a final solution that works for you. And the main idea is what we we call Bernstein called repetition without repetition, right? So you repeat an outcome hitting the hammer. In this case, blacksmiths were trying to hit a piece of sheet metal, consistently hitting the same spot on the sheet metal, but not these things. So two swings of the hammer, but not by repeating the movement, right? So the idea of movement variability isn't noise. It's good, right? Having a slightly different swing on every pitch, being able to, you know, use your elbow in different ways, drop your elbow, use a different hand position is not a bad thing. That's not noise we want to get rid of. That's actually good, right? Because it's going to allow you to repeat this repetition without repetition, being able to keep the outcome there even though the conditions are changing, right? So that's one of the fundamental differences. There's not one ideal technique. We actually want to promote variability in the swing, right? So the ecological approach, the idea is that we still want the same outcome as the other theory. We still want consistent performance outcomes, barreling up the ball and baseball, hitting it, you know, square on the paddle, the bat and cricket. We achieve that not by using one repeatable swing. We achieve that by having multiple different swings, right? Being able to adjust, you know, our core to hit balls at different heights, right? This picture photo from Bart Hennigrath created. The way that we do that is adding much more variability in practice, right? So the low outcome variability requires a significant amount of movement variability. So movement variability is not bad. It's good. And we do that. We develop it through higher practice variability, OK? And so in this, we're not trying to learn adjustability. The purpose of adding variability to practice is not adjustability. It's adaptability. So right from the start, we want you to learn how to solve different movement problems, learn how to learn to move, right? So we want you to learn to repeat an outcome, not by repeating the movement, by learning to adjust your movement slightly for different conditions, right from the start, right? So this has a very different kind of idea of what you do with variability. You first of all, you want it there right from the start, right? You don't want there's no fundamental, there's no ideal swing, right? So we don't want to teach you that first, right? By, you know, and then add variability. We want to start you off by problem solving and making decisions right from the start, right? You know, obviously we have to adjust this and we'll talk about this in later on how to the level and age of the performer. But we want variability right from the start. It's also the case that we don't necessarily need what we're varying to be something that will vary in actual games or competitions, right? We can vary the weight of the bats, vary the, you know, the stance, vary the ball, depth, the size and weight, right? Because we're not trying to teach you to how to adjust to different things you're going to face in competition. We're trying to teach you to be adaptable, to solve problems. So it's fine to use problems that you'll never actually face in competition because we want you to learn to be a movement problem solver, be adaptable, right? That's, that's the goal, OK? So instead of breaking things apart, right? If we want you to learn what we're going to use, and I'm going to talk about a cricket example in a second, you know, what we're going to use instead of breaking scale apart, task decomposition, what we're going to use is task simplification, right? And here's my favorite example. One of the ways, if we turn to the sport of soccer, one of the ways that we we try to teach people soccer is by decomposition, right? So instead of actually playing against another player, we have you dribble around cones because you need to learn the fundamentals, dribbling technique, the one way to do it before you can actually play soccer, right? So here we have a thing that's decomposed, right? You're dribbling a ball. There's no information here at all. The eyes are down. This is what I call this fake agility, right? Going around something has a purpose. This has no purpose, right? So what do we do instead of instead of breaking apart? What we switch to is using soccer tag, right? So what kids are asked to do is they're asked to try to tag another teammate while they're controlling the ball, or you can have them control the ball and the coach tries to tag them. There's lots of different variations of this. Why is this better? Well, they have their eyes up. It's way more fun. It's real agility, right? It's real functional agility. You are this guy is going to the left, his left, because for a purpose and based on information, because that guy is reaching towards that place, right? It's not because a coach told them when you get to the first cone, turn left, right? That's fake. That's just aesthetics. That's just eye wash, right? This is real, right? So that's the one of the main differences. And as I said, we'll see an example of that. So simplification instead of decomposition. OK. So that's kind of the background. Let's get into some actual research findings that are consistent. I'm just going to kind of pick out a few that are consistent with these ideas, right? And then we'll get into practice design. So the first one I want to talk about is this interesting paper by Jones and Lou Hardy and colleagues. They did this really interesting pair of studies that I talk about on my podcast. If you're interested in more details, they looked at they took a large amount of data, practice and developmental histories of players and cricket at different levels, right? So they were playing at elite levels versus county versus, you know, national. And what they did was use the machine learning algorithm. So basically they trained a system to pick out the factors that predicted the level, right? Of that the player was performing at. And they found a set of 18 different factors that separated elite batters from super elite ones, ones that represented their country. There was a lot of kind of basic things, you know, consistent with the ideas of maybe deliberate practice was, you know, the more they practice, the people that higher levels practice more, right? They started earlier. But one of the things I want to point out is that our findings demonstrate that the super elite batsmen undertook a larger volume of skills bay practice that was both more random and more varied in nature at age 16. So they were starting at younger with more variability of practice, right? They were also the idea we're going to talk about in a bit of their practice was more optimized to challenge them at the right level instead of doing the right thing. So this study kind of gives some initial things that what separates people at the highest level is the kind of the variability of conditions that you know, developing the skill that they face. And I said, as I said, there's lots more to the story if you're interested, you know, search machine learning on the, on my podcast page, perceptionaction.com and you can find more about this work, go through it in detail, okay? The other thing I want to look at is this idea of is movement variability good or bad, right? And for this, I want to talk about a recent baseball study, right? So in this baseball study, what they're going to look at is the relationship between the hand position, right? Of the pitcher and kind of the kinematics, right? How consistently they're releasing the ball, right? What you want in baseball, you want hand position to release point to be consistent. If you're going to get the ball to the same place, you're going to have to kind of release it at the same point. How that relates to the actual kinematics of the body, right? Shoulder elbow, right? Do I get a repeatable delivery in baseball or cricket by moving the same way every time by reducing variability down to zero or is there something else going on, okay? So hand position in baseball, this is from baseball, pitchers have very different individual release points. This is the kind of position in space, but overall they're very consistent about hitting the same release point, okay? So what they were asking in the study was, is it due to reducing the variability because variability is bad or do I get a consistent delivery by having what we sometimes call in the ecological approach motor synergies, right? So I'm not doing the same delivery every time. What's happening is my shoulder and elbow are working together. So on one pitch, one delivery maybe my elbow, my shoulder starts to open a little bit too early. I compensate that for by having more wrist movement at the end, something like that. So the two things are working together, okay? We call that a synergy. So I'm having my delivery is going to be variable from execution to execution because one time this is going to open early, one time this is going to open late. I'm not achieving repeatable outcome by having the exact same movement. I'm achieving a repeatable outcome because my joints are working together, right? There's variability in my joint angles, which is functional. So to test this, they had 12 pitchers. They threw the same pitch over 10 times in a row, relatively low velocity that point out. What they did is a really interesting method to answer this question called the permutation method, right? What they were doing was looking at the variability in the outcome, okay? And what they did was they compared two different ways of doing this, right? So one thing I could do is I could see what is the variability in the outcome? So they developed a model that predicted the outcome. So what is the variability in the outcome I get when I take the joint angle from the knee angle, the wrist angle, all from the same delivery, right? Versus what happens when I, in my model, I use the knee angle from the first throw, the hip angle from the second, the elbow angle from the third, okay? Think about the difference between these, right? If your delivery is completely repeatable, that's your secret to being a great pitcher, then it shouldn't matter which execution I take your wrist angle from. It's the same on every trial, right? Cause you're repeating your delivery. So this should lead to the same result as this, right? It doesn't matter that these two happen on the same thing because they're the same every time, right? It's a basic idea. If they're compensating for each other, though, if my, the wrist angle I use on an individual throw is related to the shoulder angle, then it should matter, right? Because putting these two together from different executions, then I'm not getting this synergy, right? So that's what they're kind of looking at these two things. And they did this for both upper body and lower body. This is kind of the basic idea of this. And I went through this article in detail in my YouTube channel if you want more of it. But the basic thing they found, I wanna show you here, this is the variability outcome, the hand position, right? These are measured for different angles, whether we're talking about vertically, horizontally and so on. But the main finding they found is the variation in outcome was lowest when you took all the points from one delivery, right? When you took them from different deliveries, it went up, right? So that means the way that I'm achieving the consistent outcome is by synergy. My joints are working together, right? My shoulder elbow, because when I take them from the same execution, I get way better performance when I take them from different ones, right? So it's not reducing variability is zero, okay? It's not getting the same exact delivery over and over again. It's having different deliveries in which my different joints and degrees of freedom in the inverse these terms are working together. So the regulating variabilities rather than reducing variabilities, right? So this is gonna be an important message. What skill is regulating and structuring variability, it's not getting rid of it, right? That's one of the main points I wanna make here. It's not reducing things to zero by repeating a movement over and over. That's not what skill is, right? It's making the variability functional and useless and useful, not useless, useful, right? So the variability, my shoulder angle is varying because it's compensating for my other angles, right? It's working together in this synergy, right? So that's the basic idea, okay? Kind of this. I found, so that's pitching. I found very similar results in studies. I've done a baseball batting, which I think you would expect similar in cricket as well. What I did is we look at use force plates. In baseball, there's kind of these distinct phases, biomechanical phases in hitting. You lift your front foot off the ground, the stepping phase, you bring it, which coils your weight to the back. You step back down in the landing phase, which loads the weight and brings the weight forward. You swing and then you hit ball. What I did in my study was I looked at a big training study in a couple of different papers I published. Using force plates, I looked at the timing of these things, okay? And what we found, the first thing you found is we get what happens. If you look at the timing of the different phases of landing, wading, swing, and then the bats actually in the zone, what's happening is the variability is going down and down and down, right? So when you start your swing, you're pretty inconsistent, but as it unfolds, it's getting more and more precise into the actual level of 10 milliseconds of better needs. This would be impossible if you're just executing a pre-programmed, repeatable swing, right? That was just, you're just having a ballistic swing. This is evidence adjusting on the fly. Again, using these kind of motor synergies. The other thing I've done in baseball, and in my baseball papers, I looked at the, what kind of a conceptual version of what's called uncontrolled manifold analysis. So imagine I have, I'm making a swing where, and I'm gonna divide the swing into my weight shift to my back foot and my weight shift forward. If you think about it in baseball, those two things need to add up to equal the time it takes the ball to get there, right? If the ball is gonna arrive in 480 milliseconds, say, I need to shift back and shift forward in that time to get my bat to the right place at the right time. If those two things add up to smaller value, I'm gonna be there too early, too big a value, gonna be too late. So if I start with a particular swing, it's possible, I can vary that. I could do a slightly quicker shift weight back and a longer weight forward by and vice versa. That, what we call in uncontrolled manifold analysis is good variability, right? Because it's going to allow me to adjust, right? And still achieve my goal, be adaptable, right? Be adaptable and still achieve my goal. This is bad variability because it's gonna make my swing too early or too late. So what I found in my training study is there's the black dots or a bunch of swings made before training, where you can see is there's an equal amount of good bad variability going away from this optimal area and good variability. After training, all the variability of the white dots, importantly, it's not going to zero, right? Batters are not swinging by using the same weight shifts every time. It's varying massively, but their variability has been restructured, right? So that it's keeping the goal, that's working together this synergy, this functional synergy, right? So that's consistent with that. Another one I wanna talk about just quickly because I think Paul is presenting in this series on cricket. So I'm sure he's gonna talk about this work. But this is another example I think that fits really well with the ecological approach. This is really awesome. I think it's award-winning paper on cricket, optimizing cricket. So the kind of question that kind of we can think about through the different theories of scale acquisition is what do we do with biomechanics, right? If we do a big biomechanics analysis and we measure all the joint angles involved in elite cricket bowling or elite baseball pitching, what do we do with that information? Well, the traditional view is we identify the optimal technique, right? Elite cricket bowlers all have this knee angle on delivery. They all have this shoulder angle. They do this with their elbow, right? Then we try to give novice pitchers those things, right? So we bring them in the lab and say, oh, nope, you're not bending your elbow as much as the elite pitchers do. So we change that. So that's option one, which in the ecological approach, that's wrong way to do things, right? You're trying to give person the one technique when there is no one technique, okay? The more interesting way is these kind of two options and the paper by Paul really does the second one. How can we identify an optimal solution for each individual, right? Because it's gonna be different. It depends on your own individual constraints, your own kind of body height, weight, flexibility, and so on. So what they did in this paper was they took us one bowler from the England Wales Cricket Board. They measured all the forces and movements and torque and things from their delivery and they created a model, a biomechanical model of the delivery based on 16 segments, right? So they had the output from the model and the outputs were the ball parameters, spin, direction, speed, right? And what they did was they actually, their model, the first they tested, does it actually fit their deliveries? Do you get the same kind of angles and outcomes? Yes. But what was really novel and interesting about this paper is they used it to optimize this individual bowler's technique, right? So they identified something by using their model, biomechanical model, something that could be changed in theory that would result in higher ball velocity, right? So they identified these kind of factors and they showed that if they changed these parameters, basically in terms of the extension, right? It should get an increase in ball velocity of about 10%, right? Which in this case was about eight miles an hour, which is huge, okay? Also ground reaction force, right? So essentially what the change was doing was making the change in the stride, the landing, so to optimize more of what we call in biomechanics breaking, right? So any kind of force generation thing like hitting or pitching, you load the force, you throw the force forward and then you stop it, right? To throw it all into the ball, right? Or onto the bat. So you need breaks, right? You need to be able to break your lower half and that's what they're essentially doing. Breaking their lower half so they get the, you know, much more whip and force in the delivery, right? And that's what this kind of model was, change was getting them to do, right? So in specifics, right? We want it to your front ankle and you need to remain more extended, you know, more extension basically. And these are pictures showing the difference between the optimized model and the actual, you can see it's very, very subtle differences, not surprisingly, right? So this is a really interesting idea. I think this is a credibly novel and important idea. The question is, you know, from a coaching standpoint, can we actually give that new solution to a batter and how, right? Okay, we can get more speed by doing these changes to your delivery that we've optimized for you. How do we get you to do that, right? It's very tricky because there's more than just, it's really good in this paper, they took into account the individual constraints but there's more to it than that, right? We're actually, when you think about coaching, we're building on top of what you already have an important concept in ecological approach is the idea of transit dynamics. You have certain way that you've developed in coordination for throwing, right? Maybe it's doing more sidearm, more over the top, right? That those are strong coordination patterns that any new thing is gonna have to build on top of, which can be really, really challenging. But I think this over idea of using biomechanics for individual solutions, instead of the one optimal solution is a great fit with the ecological approach. The last kind of thing in basic research I wanted to talk about was returning to this task decomposition versus task simplification, right? What we're essentially talking about here is trying to get the difficulty of the task right for a new learner, right? How do we get a new cricket batsman to have some proficiency, right? And as I mentioned, the traditional approach is task decomposition, breaking it into parts, right? So we're just gonna teach you the actual movement of the bat without having to pick up the fly of the ball. We've broken it apart, okay? Hitting off a tee in baseball, dribbling around cones and soccer, passing lines of basketball. This is a well-used for a long-time approach to teaching novices, right? Why is this problematic? Well, we know from a lot of research you do completely different things when we break perception and action apart, right? This is some work by Keith Davids and I was showing when you ask a person to toss a volleyball just to practice that versus toss a volleyball to actually hit it, you get completely different tosses. You get different gaze behavior. People look in different places when they're gonna actually perform an action versus not. You get in different hitting, stepping forward and backwards when you change the cricket delivery, right? So versus a machine versus an actual bowler, you get very different behavior. You actually use different brain areas a lot of times, right? So that's a problem with decomposing, right? Decomposing fundamentally changes the task such that you can't really put it together in the way people think, right? So in task simplification, the alternative, what we wanna do is keep the whole thing together. We wanna keep you swinging the ball at the ball based on the information about the ball flight, right? What we wanna do is scale it down, right? We wanna scale everything down to somehow simplify. We can do it by reducing speed, reducing distances, changing equipment and so on. And this has been done nicely in cricket. One example people have used is scaling the pitch size, make the cricket pitch, this paper by Harvard College. They showed that bowlers can have better length deliveries, better actual mechanics, right? The fact that each person has their own individual movement solution isn't to deny that there's some fundamental invariance or tractors that you need, right? A downward angle on your pitch delivery. In tennis, you want a rainbow-shaped stroke, right? There's certain things that have to be there or it's not to deny that. And what a lot of research found is that when you change things like this pitch there, you can get closer to those invariance, right? There's some great work on tennis by Tim Buzzard and colleagues from Australia showing you get this when you change the equipment. Kids use in tennis. Lower compression balls and smaller rackets. Not only do they hit the ball better, hit them with more force, play better, but they also hit the ball more in front and have more of an arching stroke. So you get it just like we're seeing with changing things. You also get benefits for batters, right? By using a shorter pitch, right? They're doing more shots. There's also some interesting work looking at equipment modification, right? Changing the ball, changing the bat and cricket so that you have more surface area, where you have a better chance of contacting what they found in this study, this study, they use 43 children. They use either a regular bat or the modified one. What you find is you get more hits, right? When you have a modified one, not surprisingly, that's very motivating, better proficiency, but you're also getting these kind of technical changes that you want, right? You're moving the person in the right direction, not towards the ideal solution, but to having kind of the basic things you need to have there in terms of grip and stepping forward to getting more desirable technique. So again, I think that's a very, very consistent with the ecological approach we're talking about here, okay? So in the last part of this presentation, what I want to look at is getting into more of the practice design. We've already talked about practice design a little bit, for example, with the scaling, but mostly focusing on the constraints-led approach, right? So the constraints-led approach to coaching is the idea that, okay, well, there's not one ideal solution. So what we're gonna do, instead of trying being an instructor as a coach, bringing in a player and saying, here's the answer, here's how you bowl, here's how you hit, what we're gonna do is be in a designer or a guide, right? We're going to develop practice conditions, practice activities, not drills, so that we can help the person explore and find their solution. So I talk about, and this is an infographic I made and I made a page on my website if you want to look at it in more detail at perceptionaction.com, what we're trying to do in the constraints-led approach, use the typical way that it's used, is what we wanna do is add a constraint that's going to push you away from one solution and have you explore others, right? Ideally, often we wanna take away or destabilize the solution that we don't like. And we do this, there's three different types of constraints that we can use, individual ones which are harder to manipulate, and those are things you bring to the table, your speed, your size, your flexibility, the environment, we'll see an example of that, the playing surface is an example, but the more common one we do is task, right? So we want to push you away from one solution and have you explore. And a good example, right? Example of constraints you can see when you look at the difference between baseball pitching and cricket, right? So in baseball pitching, one of the ways we can understand how you coordinate a movement like throwing a baseball is by looking at your solution space, right? So in theory, you could use any combination of shoulder and elbow angles to throw a ball, right? We see that this is all this Chapman who has a world record for the fastest pitch 103 miles an hour. He has shoulder angle, elbow angle. Where does he lie in this space, right? That's solving the degrees of freedom problem, right? He's gonna pick out some certain angle, okay? Why does all this Chapman throw like this versus a cricket bowler like that, right? They're trying to achieve the same goal, get the ball as hard as possible and throw someone out. The reason is they have different constraints, right? They have different constraints. In this case, a task constraint in cricket, there's a task constraint about the regulation of not being, of keeping your elbow straight, right? So cricket bowlers have an additional task constraints that shapes their solution, right? Based on the rules, right? So that fundamental idea we can use as a coach by adding our own new rules, our own new equipment things to do the same thing, right? This constraint of this rule is shaping how you find a solution in solution space, right? And here you can't bend your elbow angle, so it's all shoulder angle and wrist. Now we can do this as a coach. We can try to do the same thing. And let me show you a few examples of that, right? Here's a quote from Ian Renshaw, talking about an example of what coaches do when players in cricket have a weak hand, right? A weak top hand. So a weak top hand sometimes causes a batter to have problems swinging in a straight line, okay? To pick up the bat straight. So there's this example of this player, the coach, what they did was put a piece of wood, a bamboo in the ground, just outside of the stump. So basically the batter had to kind of pick up their bat, learn to pick up their bat, which both, so what they're doing here is taking away the solution, a movement solution of not swinging straight through, right? Not lifting up your bat by adding a constraint and they're forcing the batter to explore other ones, which is both going to learn a new pattern of coordination and probably make their capacity, they change their individual constraint by making their top hand stronger. This idea is something exactly kind of the thing that I did in a recent study in baseball where I was trying to get batters not to deal with the problem of hitting with a weak hand, but to actually learn to hit the ball in a certain direction, right? In baseball, there are some situations where you not just want to hit the ball, you want to actually hit it to what's called the opposite field. So if I'm standing on the, this side of the plate, I want to hit it over there, right? Just like with that cricket example, there's certain swings that are not going to work for that, right? We don't want you to swing in really pulling inside, right? So there's certain things we want to take away in order to encourage you to get to a solution that will work for hitting it to the opposite field. So again, ecological approach is not to deny that there's certain movement solutions that are not going to be effective and there's certain invariant properties that should be there. So what I did in this study was in, I've done this, I did this in a virtual reality environment but I've also done it lots of times in real, is create swing path variables much like that bamboo that the cricket coach rate. So put a fence up in terms of in front of the batter so that they can't swing out here. They'll hit the fence, put a barrier on the ground telling them they can't go across it, right? These are constraints. I'm adding a new constraint to practice such that the old way, your old movement solution won't work anymore. I'm not telling them, no point in these studies do I say, okay, pull your elbow in, do this. I'm not telling them the solution. I'm adding a constraint to get them to explore and find it themselves. So what we did, another one I love in doing both hitting and pitching is what's called a connection ball. So what we do is we take a kid's yellow ball and have you hold it against your body, right? Either between your forearm and your upper arm for hitting or between your arm and your side for pitching. And what I want you to do is either pitch or swing so that when the ball comes out, it goes forward. And the reason we do that is if you have a movement solution where you're separating from your body early, you're never gonna be able to get the ball to go forward, right? It's gonna fall out and go sideways or backwards. So this constraint of holding the ball, this task constraint we've added is pushing you away from the solution we don't like towards one that's more effective. That's the constraints that approach. And in my study, what I found, I compared this to differential learning where people just practice a random stances and positions versus where I tried to tell you, here's how you hit the ball to the opposite field. Here's the mechanics, turn your hips, do this with your elbow, so on. And what I found was the constraints that approach was the most effective, right? They had more hits to the opposite field. Interestingly, without even telling them, they also picked up the affordance of which pitches were most effective for doing this, right? They swung more at pitches that were inside, right? Which is easier to hit to the opposite field. The prescriptive instruction group also did this because I told them to do it explicitly, whereas the CLA group just learned it on their own, which is a really interesting finding, okay? The last one I wanna talk about in terms of constraints is this really interesting study by Crowder and Ian Wrenshaw and colleagues looking at environmental constraints, right? So what they were talking about is the fact that, cricket, if you wanna be a bowler, just like much like there's the case with tennis, right? You have to play on different surfaces, which are gonna affect your ball flight in different ways, right? So the pitch is a major environmental constraint, right? It can be clay, sand, silt, like just like I said, if you need to adapt your pitching delivery to these different constraints, this change in constraints, right? Being adaptable is being skillful. So what they did in this study was they wanted to see how people changed their delivery to two different conditions. One, which was they created this kind of small-sided game environment where they're pitching on a surface that's very similar to what they're doing, versus two attempts on a pitch designed to be more like is used unfamiliar to bowling and those used in India. So different consistency of the surface to have different flights, different ball flight. So they're unfamiliar and what they predicted is that when we put you in this unfamiliar environment, you're gonna search for different solutions, okay? And so they had eight spin bowlers, they did this four-week cap, they had three matches. The first one was on their familiar surface, the next two were on the unfamiliar. The here's the consistency, the familiar one was 72% clay where the other ones were a mix of clay and much more sand, right? So a different type of surface. What did they find? They measured a bunch of different variables. Not all, they measured kind of spin, outcome, success, getting the batters out. And they also asked them, what were you trying to achieve? And what they found basically was that on the familiar pitch, right? Batters were mostly focusing on kind of tweaking their existing solution, what we call parameterizing, right? So they were trying to change the depth of the ball, change the location to try to get the batter out, right? So they weren't really changing much fundamentally about their delivery, they're using the same kind of delivery, same kind of basic velocity, they're just parameterizing it differently, using it to different depths, different ball locations and so on, right? So kind of performing, if you will, right? Not, they weren't trying to explore a new solution, which made sense, because they've already developed a good one for this surface. But when they got on this different surface, the batters start talking, when you ask them, the pitchers started talking more about the surface, okay? They also responded, started to change their bowling style, right? Manipulating the arm angle, the launch angle, things like that. So here they're exploring, right? You've put a new constraint on them, so they're looking for a new solution, right? So this is another good thing, this is both something that, example of showing how athletes need to be adaptable, but also showing how you could potentially vary this in practice to get them again, to learn to be adaptable, to find these new solutions. I think that's a really important finding. The very last thing I wanted to mention is something I'm not gonna go into detail, but it's also really consistent with the ecological approach and this idea. And I cover this in detail in episode 310 of my podcast, is this issue of meta-stability and training in meta-stable regions, right? So to illustrate this first, let me give you an example from boxing first, right? So in boxing, when I'm standing in front of an opponent, there's several different punches I could use, right? I could use a hook, uppercut, jab, right? What we've learned is that good boxers are, what they actually do is they stay at a distance from their opponent to keep each of these solutions open, each of these different types of opponent open. If I get too close to you, I can't do the same kind of punch. I really limited to my jab. If I get too far from you, I've limited the punches I can do. If I stay in the right region, I have what's called meta-stability. I keep stable, but I have lots of different options, right? From this, people have proposed, and I said, I talk about this in detail, that a good way to train athletes is to force them into this meta-stable region. And cricket, the analogy of this in cricket is the ball depth, right? Whether you have to step backwards, step forward, stay in the same place, if you train people in this region where there's some uncertainty about which way to go, that can be a really effective way to make them adaptable, kind of training in this meta-stable region. As I said, if you're interested in more of that, have a listen to that episode. Okay, so some of that's kind of the, my idea is about applying an ecological approach to cricket, fundamental ideas we're talking. Repetition without repetition, not strict, rote repetition. Variability is good, right? We wanna, in order to get kids, kind of younger players, task simplification, scaling down, shorter pitches, different bats, different balls, not breaking it apart with drops, right? Adaptability, not adjustability. We want you to be a problem solver, not just learning the fundamentals, then how to adjust it to different conditions, right? So those are the basic fundamental ideas. So if that was interesting to you, here's some information about where you can find me, two kind of main things. Here's my podcast, the Perception Action Podcast. You can find it on all of your podcasts or go perceptionaction.com. And if you're interested in kind of this different approach, this rule for variability, constraints-led approach, et cetera, I've just recently published a new book called How We Learn to Move, which you can find on perceptionaction.com, the links or just go to Amazon and you can find a copy. Thank you very much and cheers for now and keep them a couple.