#084 - Data Without Dogma: Rethinking Wearables, Workloads, and Return-to-Play

In this Cardio Talk episode of the Decoding Excellence Show, I dive deep into a conversation that every coach, sport scientist, and high-performance practitioner needs to hear; how we’re misusing wearable technology and workload monitoring.

Most people treat these tools like diagnostic or predictive engines when, in reality, they’re contextual instruments that require interpretation, experience, and nuance.

Data should never drive the process; coaching does.

To illustrate that point, I walk through a detailed return-to-play case study, breaking down exactly how I manage the transition from individual rehab to team reintegration. I explain how I assess individual workloads, manage micro-dosing of stress, and slowly build load tolerance; all while balancing communication between medical, performance, and coaching staff.

This isn’t just about collecting data; it’s about understanding the story the data tells and aligning that story with what the athlete is experiencing in real time.

Key Takeaways

• Wearables and workload metrics are contextual instruments, not diagnostic engines. They require interpretation, experience, and nuance, not blind trust.
• Data should never drive the process; coaching does. Technology should simplify complexity, not add more of it.
• In return-to-play, manage the shift from individual rehab to team reintegration by micro-dosing stress and slowly building load tolerance.
• Keep medical, performance, and coaching staff aligned on the story the data tells and what the athlete is actually experiencing in real time.
• Turn raw metrics into clear, concise insights delivered in the moment, not days later in a spreadsheet.

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Episode Transcript

This transcript was automatically generated. While we strive for accuracy, some words or phrases may be misheard.

[00:00:00] Sport science, strength and conditioning, high performance coaching, welcome to the Decoding Excellence Show. Good morning everyone, welcome back to the Decoding Excellence Show I am your host as always, my name is Adam Ringler and I've been hosting the show for nearly 90 episodes at this rate. I am a strength conditioning coach for almost 20 years of sports scientists and essentially an enthusiast of human performance, right? And today I have a great episode for

[00:00:50] You. I want to walk through how I end up using our wearable technologies when it comes to workload monitoring. I know a lot of people out there have wearable technologies actively employ the idea or principles of workload monitoring. But I want to walk through scenarios, a case study, how one might actually use it in a return to court scenario. So that sounds like something you're interested in. Stay tuned. This episode is brought to you by Momentus, the supplement company

[00:01:22] I personally trust for my own performance and recovery. I've been coaching division one athletes for nearly 20 years and there's one supplement that has never left my program. That's creatine. Momentus creatine is science back. It's clean and NSF certified for sport, meaning it's third-party tested for purity and safety. The same standard used by professional athletes. If you're serious about getting stronger, recovering faster and performing better start with

[00:01:51] What's proven. Go to livemomentus.com and use code Adam Ringler at checkout to save on your order. That's livemomentus.com code Adam Ringler because performance isn't bought. It's built. Welcome back to the Decoding Excellence Show. As always, my name's Adam Ringler. Like the intro ended up saying today, I am doing one of my famous cardio talks, my walk and talk, where I am walking my neighborhood. I have a 45 pound rucksack. I'm up back. I am out here enjoying

[00:02:25] Some sunshine out here in Boulder, Colorado. And I am essentially getting my daily steps at faster cardio here earlier in the morning. What that means is that this is not a studio. This is me, a phone, cars whizzing by. Here's a 4-F-150 coming right past me. Dogs barking in the background, birds chirping, planes flying overhead, noises. So this will not be a polished podcast. This will be like you and I are walking the trail together, imperfect, messy, ugly, but that's the

[00:02:57] Nature of the show. I'm multitasking. I do not have a lot of time to be able to sit down in a studio with a script and walk through a full podcast. So this is my best attempt of sharing some ideas, getting things out there, and talking about sports science, talking about strength conditioning in a way that still allows me to function as a human being because I'm pulled in a million different directions. So today, like the intro said, I'm going to walk through a scenario,

[00:03:27] A case study, a hypothetical case of essentially how to workload monitor, right? How to actually use it, not just look at the data and say, yep, cool. We're doing it. Hey, coach, here's your report today, but rather actually informing decision-making. We're actually using the data to make decisions yourself, right? Indirectly or directly the impact of the coaching staffs in a workload monitoring fashion. So that is my nature today. That's what I want to end up discussing. And now, whether you employ the use of

[00:04:05] Catapult sports, stat sports, trying to think of some others, or connects on sports like we do, the technology is not nearly as important as the principles of the philosophy of what I'm discussing today. So if the metric of your choice is player load or accumulated acceleration load or some other proprietary metric native to the specific platform, that's fine, right? Use the principles, use the ideology, use the philosophy, don't be married to the specific metric, your metric may vary. So this setup, right? I want to go through a hypothetical case. You could

[00:04:50] Call it an ACL. You could call it a severe ankle-sframe. You could call it a meniscus. You could call it an MCL. You could call it a stress fracture. You could call it whatever, right? But let's set the scene or a scenario where you're trying to return a player back from some type of injury, right? And maybe they've been out. They've been limited for several weeks, for five weeks. And the only data that you have on this individual is maybe some of the early reporting August individual sessions on a basketball court. I'm going to use basketball, but it could be insert

[00:05:29] Volleyball, insert any other indoor sport that might be employing the use of this, right? Now, a couple other things real quick before I continue this. If I'm out of breath, I'm out of breath, y'all. I don't know. I'm very early into this. Now, I've had a good weekend, but this rock walk thus far is picking me right in the tail right now. My heart rate's pretty chill. Not nearly high, but boy, maintaining a conversational tone and pace of voice conversation, like Peter Atio would talk about being sort of your zone too. I am labored while talking and

[00:06:14] Walking at this point. So, apology is if I'm having to gas for breath because it's kicking my butt right now, but nonetheless, indoor sport. The only data that you have on this athlete is maybe four to five sessions of 60-minute individuals, right? And understand the nature and that individual is not a team practice. For those that work in basketball, you know, individuals may be 60-minute long, 45-to-16-minute long, depending on your coach's flavor of how they like to break up those indie sessions. But your job now is to transition this person back into court.

[00:06:56] And all you got is data from their individual sessions. But what you do have is a mountain of data of what the current practice volumes are, what the current practice loads are, high-speed running and player workload or accumulated acceleration load. And you also know what your average warm-up load is, your shoot-around load is, and multiple seasons worth of game data, including your team's own game data right now. So, I'd like to kind of walk through a couple of these scenarios for us as far as what I think about what I look at, how I calculate it, and how I would be trying

[00:07:41] To navigate this athlete back into a return-to-play model. So, a couple scenarios. This is going to be a very math-heavy episode for us today. So, let's assume this. This athlete in this case study was doing their 60-minute individual sessions. And the only thing you have on them is that on average, they were accumulating roughly for their workload metric. They were getting, sorry, my hands are freezing out here in the Colorado Code. They were getting 348 units of accumulated acceleration load during the 60-minute session. And I could also report high-speed

[00:08:38] Running, but I think just having this conversation, it actually detracts from the point I'm trying to make rather than add to it. So, I'm going to drop that metric, although I do the same philosophy and principle across both metrics, because I think it's important. I think we need to also replicate this for high-speed running, because that is obviously high-velocity change direction, high-speed sprinting, that's going to have an impact on any type of joint integrity, bone integrity, ligament, cartilage, etc., muscular as well. So, but for this purpose, we're just talking about the summation of all loads

[00:09:21] In a high-speed running or in a workload scenario. So, you have 348 as a average of the three to four sessions that this athlete participated in before some type of injury. Now, they've sat out or been out with their injury since then. Now, let's assume that this athlete could not participate because of the injuries that they incurred. So, they weren't doing autorgi, they weren't walking, they weren't treadmill, they weren't underwater treadmill, any of the therapies that one might use or modalities that one might use to

[00:10:09] Continue to bridge that gap. You just had this hard cutoff and that hard cutoff was 348 load, time has passed, and now your job is to get them back reintegrated with the team in a safe fashion. Here's the steps that I would take. First of all, I want to know what a typical practice looks like right now. So, in the system or any of your AMS or in Excel, if you're just a science geek or Python or R, whatever you might want, I pull that data in and I looked at all of our game day minus fours, minus threes, minus twos, prep practice, minus once ones, and then our game day loads.

[00:10:52] So, now I have a basic target, plus or minus one standard deviation away from the mean or all of our workloads for those given days. But more importantly, as we're starting to try to reintegrate this athlete back in, safely, soundly, we know that her floor, 0%, with 0% increase, would be that 348. Now, based on most of the exercise science and physiology and return to play and rehabilitation journals and research out there, whether you believe in the ACWR or Qtachronic workload ratio or exponentially

[00:11:38] Weighted moving averages or Tim Gabbett's research about the Qtachronic. What we can probably safely agree on is that a no more than 10 to 15% increase a week of any given measure is probably a safe progression back. 10 being what is heavily cited in most of the scientific research journals, but every percentage above that is probably slightly more risk involved. So 10% would be safer than 15%, which would be safer than 20%, you get the idea. Every percentage more is a risk probability that you're having to weigh measure and so to assume. So 10% your safe return,

[00:12:35] Let's say 15% is a little bit more riskier than increasing up to 20% weekly increases. You assume more risk probability of less desirable outcomes in that return to play scenario. But you now have an athlete who is staring down multiple games on the horizon and your jobs to get this athlete back participating. What do you do? Well, we know that we have a 348 AAL Accumulated Acceleration Load or Player Workload and we know that a typical game load for us average is somewhere around 625. We have players that are obviously multiple

[00:13:31] Standard deviations above that and players that are below that. But when we drop players that are less than 12 minutes again, so that's our threshold for us. If you're under 12 minutes, we drop you from the report because your contribution to the game is less than what our high contributors would do. So we take everybody that's above 12 minutes per game. We average those workloads from the highest to the lowest and 625 AAL is roughly that sort of mean target. Now from there, that means that if we want an athlete to be able to participate on average in a game,

[00:14:21] We need to get them up to that 625 AAL average. So we know now the floor and we know a target that we have to increase towards. Now, like any of us basketball heads, we know also that game load is just that. It's just game. We have a morning shoot around that can be anywhere from 300 and 400 load. We also have a 60-minute warm-up that can be anywhere from 300 to 400 load. So majority of athletes will go into games with anywhere from 600 to 800 accumulated acceleration load prior to the first tip, which is kind of crazy to think about because

[00:15:12] That's a standard two-hour some practice already. The math is somewhat simple. For me, I look at it and I set week zero as 348. I set a 10% increase in that column from that 348 player load or accumulated acceleration load. And that means that the next week's load could be load target. Somewhere in the vicinity of 383 followed by 421, followed by week three at 463, week four at 510, week five at 560. And then by week six, we actually we get 617 close to that 625 average game load.

[00:16:07] So in that scenario, I had a 10% increase weekly of their load, daily load. We are achieving game return in about a six-week progression. If one were to be slightly assume more risk, not just the 10%, but upwards to 15% of an increase across the week of daily loads, we would go from that same floor, that 348, immediately up to 400 load in week one into 460 in week two into 529 in week three. And we would get to 609, 610. Let's just round up by week four. So a four-week progression

[00:17:02] To get this athlete back participating our average in games. Now, that's great, right? We have we have a benchmark, a target to work towards each week. So when a coach comes to you, an athlete, a trainer comes to you and they say, hey, what can so and so do today? You can provide them real world objective targets. Not, I don't know, college, maybe half of the drills or quarter of the drills. In real time, you can say their target is 400 today, their target is 460 today. Now, I always equate this to finances. You have $460 to spend today.

[00:17:50] It does not roll over. You can't, you can't save it for tomorrow. How you want to spend those $460 is up to you, coach. Do you want to spend it in live? Do you want to spend it in 5VO review? Do you want to spend it in shooting drills? Do you want to spend it in passing drills? Do you want to spend it in defense? That is the technical, tactical element of how one might want to

[00:18:20] Utilize the 460 load. And it's a give and take relationship. I am at practice always feeding them both. Or 100 in, or 150 in, or 200 in. You want to sit around, okay? She's out. She's out. Okay, we're 300 in. We're at 350. How do you want to spend the next 100? That is the math, the finances. In real time, that a sport scientist, a strength conditioning coach, and or sport coaches have to navigate because again, we're trying to achieve a specific loading target on a daily basis. We don't want to exceed it. We certainly can

[00:19:11] Under achieve that target. However, part of what we're trying to do is build strategically and systematically robustness and tolerance under load. I don't truly want to under achieve a target metric because I want to gradually gain exposure to these targets. So, the idea is not to constantly come underneath it. It's to hit the target perfectly, stress, a bone, a ligament, a muscle, a tendon, and then rest it and allow it to adapt and build tolerance and robustness to those loads. Right? So, by being scared of the number and

[00:20:02] Constantly coming in underneath it, we're under preparing an athlete for those loads. Now, if we're going constantly way over the target, we're also on the other side risking pushing a structure, an organism, an element above the tolerance that they have yet to develop. So, that's equally as bad. Now, I do that and then sorry, my air pod just fell out, which is less than an idea for a live podcast here, as we're doing this, this rock today. For whatever reason on Sunday is kicking me in my tail. So,

[00:20:51] I apologize for the labor of breathing here, but which makes a very math and talking centric podcast probably hard to follow, but you know, I just think there's a lot of people that end up, that I had conversations with athletic trainers, with strength coaches, with sports scientists. I feel like sports scientists are on it. They get the, and they understand it. With strength coaches, it's not necessarily always their job to understand it. Some do, most do. Some are admittedly, everyone like listen, I do it, and we have a, you know, we have a grad student break down the numbers,

[00:21:29] And they just share the numbers every day, but like, what's that? Influence. Again, my philosophy has always, always been wanting the data to influence decision-making, right? Like that's that's the cornerstone, I think, of sports scientists. We're not sports historians. I don't want to collect numbers and be a sports information director and SID. The idea is to take the data in real time, interpret it, communicate it back into concepts and ideas that coaches can understand, that drive real world decision-making. So, I think that's really important.

[00:22:18] I'm trying to pull up something that I've shared before. Of course, in real time, pulling anything up is probably less than idea, but this is what these podcasts are all about. They're long-form content. So, I've often shared this idea as far as my philosophy or data.

[00:22:47] We'll just have silence here. Yeah. I am searching on Twitter, on Exno, and it's absolutely terrible. All right. So, I've shared this before, multiple, multiple times. I had it printed out. It was on a picture frame on my desk. At one point when I was more aligned on the sports science track, if you will, and pursuing that in full transparency, I am a strength coach, guys. I'm a strength coach that loves sports science. There's other really phenomenal sports scientists out there, probably not me, but I love data, I love technologies. I love using and applying it, how to love

[00:23:37] Being in the field, being with the athletes and grinding through them as a strength, conditioning coach. But what I mean by all of this and my long-winded answer here was I always shared this that my ethos was to build a world where information is readily accessible for every athlete, for every coach, and every stakeholder at the exact time that it really matters to create accessible, concise, actionable, and easily understandable information available in real-time. I clickable button. That is the paragraph that I piece together and a Frankenstein

[00:24:21] Together. When people would ask me about sports scientists, science, data, and my thoughts, this is what I would point to as my North Star. That was my ethos. That is what I wanted to do that is how I wanted to approach data. Build a world where information is readily accessible. Check. For who do? For the athlete? Yeah. For the coach? Absolutely. For an administrator or stakeholder, you bet, readily accessible for anybody that needs that info and how you communicate it to an athlete, maybe different than a coach, which should be different than an administrator, right? There's layers

[00:25:00] And levels to all this. Let's examine the next sentence. At the exact time, it really matters. If I give that coach a workload report on this return to play 30 minutes after, did it really matter then? In fact, it's over. The drill selection is over. The ability for that coach to make a decision about this athlete's return to a play or return to court model. Is she included? Is she not included? Is he included? Is he not included? If I provide that five minutes later, 10 minutes later, it doesn't matter. It's too late. I'm out. There's a story in that point, right? That coach needed

[00:25:45] Real-time info right now. Not a minute later. The bullets are flying metaphorically. Practice is going. The clock is ticking. We can't slow it down. I can't say, hey, Coach, I'll get you that info in five minutes. The drill is already over. Again, going back to that philosophy principle at the exact time, it really matters. If an administrator is meeting with the donor, and that donor has a glimmer in their eye that they're excited about a system, or a department, or a structure, or an initiative that we're building out. But a dashboard is only

[00:26:30] Accessible via a slide deck that needs to be scheduled and set up on it. You know, from there on the television or not. And that's going to take multiple hours to do or an hour or 30 minutes or you've got to reach in it. It didn't matter. It doesn't matter. It doesn't matter how good the slide deck is. It doesn't matter how good the dashboard is. If they can't access that information at the exact time that it matters, it's pointless. It's history. Let's move on to create accessible data. This is multiple words all in succession of each other, but accessible. So if there's 25

[00:27:11] Hoops, you got to jump through to access that information. That's barriers. That's 25 barriers that you have to jump through in order to get the right information at the right time that it matters. Concise, okay. So if it is a gigantic algorithmic formula that only the most advanced sport scientists can understand, is it really concise enough for a sport coach to make a decision than the exact key to the moment that they need to? No. If you're talking to your peers, present your white paper, for set your dissertation, lay it all out there, complexities

[00:27:56] On the table. But if you're trying to drive real decision making, it needs to be simple. It needs to be simple. It needs to be concise. It needs to be direct to the point. Who about this next piece in my ethos? Actionable. How do you act on it? You can present it to the data point. But what does it invoke? What decision does it drive? What decision does it spur in that moment? Is it actionable? Is there a yes-no? Is there a decision A or decision B that's based on it? You see high speed running, creeping up above a threshold. What is the decision?

[00:28:45] Shift this person maybe to half court. No transition work. Only ball handling work. Maybe they're on the practice spot. Maybe they get an additional sub. They're subbing out every other drill to reduce the high speed running. What is the decision that the data is presenting a coach to make? Or at least the hypothetical decision? If it's something that you cannot control through the influence of your environment, why are you collecting it? Why are you sharing it? What about this one? Easily understandable information. Let me rephrase back to the original sentence, to create accessible,

[00:29:26] Concise, actionable, and easily understandable information. So-and-so has an accumulated acceleration load in the third band of velocity. Hey Adam, the heck does that mean? Oh, well, they're workload. That's their high speed workload. Oh, why didn't you just say that then? Right? So easily understandable information. Again, very much like the similar point earlier, present it in the way that a coach can understand. They're not talking to another sports scientist. There's a good chance if you are operating with sports

[00:30:09] Science in any domain. Let's just say, you may have more data in sports science and physiology, extra science, technology, acumen. There are a lot of your counterparts. And that's not a humble brag. I'm just trying to put this out today. You may be that smartest person in that domain. That's specific. I'm not talking about, listen, coaches would coach you

[00:30:40] Under the table with offensive and defensive schemes and schematics and plays and design and philosophy. I get that, right? But when it comes to your domain, you're probably the smartest person in the room. Your job is to dumb it down to make it so easily understandable that a fifth grader could understand it and say, oh, okay. It seems like we're over the target. Maybe we should pull back. Oh, wow. You mean I have a dollar to spend and I've only spent 20 cents at the candy shop? What's that mean? I got 80 more cents that I can spend at the candy shop. Cool. Okay. Let me buy

[00:31:24] These sugar candies now. I don't forget that, right? But get the idea, making simple that a fifth grader can understand. And then I finish this actionable and easily understandable information available in real time at a click of a button available in real time. I think that's arguably one of the most important aspects of my entirety, folks, when it comes to data, sports science. If it's not available at the exact intersection of a decision point, it's useless. If I come to a culture that says her injury risk was elevated, yet she endured the injury two days

[00:32:12] Ago. It's pointless. What are we doing? Now, you could say Adam, but we could use that intelligence movement forward. That becomes, that becomes a historic point in our sports data history that moving forward, we could take the lessons that we learned from history and apply it to a huge, great love it. But if it still takes us a day, an hour, a minute later, then the point of intersection for that next decision, pointless. It does not inform decision making it gives us trends, it gives us benchmarks, it gives us things that we can look at, but data needs

[00:32:54] To be accessible in real time. We need to be able to utilize your time decision making in conjunction with data to drive better outcomes. So going back to our workload reports, this is why live monitoring is so important, especially during a return to court, return to play scenario. If a coach is, if we have arbitrary benchmarks, we have specialists and we have to progress, whether it's high speed running and cumulative acceleration load, player load, you name it. If I could not in real time access that information, communicate it simply to a coach,

[00:33:40] What the next decision should be, then we don't have a workload plan. We have a historic log of our workloads, but we're not actually planning, we're not strategizing, we're not using that data in a way to inform a decision about so-and-so's inclusion in the next drill in the next live segment in the next half court drill in the next transition. So it needs to be accessible.

[00:34:10] In this case study that I laid out 39 minutes ago or so, I think it's really important. Sure, you can do the math, you can do the exercise science and the physiology and you can go back and look at the floor, you could pull out all of the workloads that this athlete did during their individual sessions. You got the floor, you got their historic information at that point in time. Now you can build out a load progression based on 10, 15, 20 percent increases each week. That's great. You have these targets that you've set and you could set them on multiple

[00:34:49] Different metrics, whether it's high speed running, if it's a certain philosophy band, if it's changing directions, if it's IMAs, if it's exertions and connects on, I'm sure stat sports has their own similar proprietary metrics like that. That's great. And quite frankly, we should we should be doing those. We should have a plan in place that is more sophisticated, that is more strategic than just 50 percent speed, 60 percent speed, 75 percent speed, right? That's that was maybe appropriate. Now we actually have objective, quantifiable, wearable technologies that provide real-time

[00:35:32] Information on every micro movement and athlete is doing. And some being really highly sophisticated technologies. But it still comes down to the fact that despite the sophistication of the technology, we also have to have the sophistication of our approach in how we systematically understand and apply an interface with data in real-time. So this has been a 36-some-minute soliloquy soapbox with me just saying, use the damn data, use it, communicate it, have it accessible in real-time, make it simple, make it easily understandable, share it, share it with multiple people,

[00:36:28] Stakeholders above you, coaches lateral to you, the athletes that you work with, let them know it, let them understand it, let them feel it, let's apply it, communicate it, explain it, make it visceral, make it real, make it part of the language in which you speak.

[00:36:51] All right everybody, that's going to be it for this Decoding Excellence podcast. Hopefully you took something away from my ranting about player workload, real-time monitoring, exercise science, sports science data, and everything in between. And if you did, please share this episode with a friend. Another sport scientist, a strength coach or a colleague in our industry, let us help each other make better decisions, more informed decisions, and use the data that we threat. Thank you all, until next show. Hey everybody, that's going to be it for this podcast.

[00:37:28] As always, if you missed any of the episodes here in the Decoding Excellence Show, you can go and explore the bat catalog. I host all of our episodes not only on every sort of podcast hosting site, whether it's Spotify or whether it's a YouTube or anywhere else, but you can go to my website. It is AdamRingler.com forward slash podcasts. From there, you can listen to all of the interviews, whether it's other sport professionals, whether it's the monologues, whether it's the basketball discussions, or the sports science intent.

[00:38:00] Go to the website, check it out, listen to the podcast. I'm telling you, you will take something away from the bat catalog of the Decoding Excellence Show. Now, one of the ways that you can support this show. Now, there's two, really. You could go to AdamRingler.com forward slash newsletter. You can sign up for my newsletter, and in this, you're going to get access to everything, the cool things that I discover from a monthly basis, new books I'm reading, things I'm discovering, articles that I found related to sports science. That's really cool

[00:38:37] And coaching. So pop in your email there, and that is just an easy way of supporting the show, and you'll get free behind the scenes access to all the cool things I'm discovering. If you took anything away from this episode, I would encourage you. No, I would beg you to share it with any of your friends, your coworkers, your family members, share it with somebody that could get a little bit of value from hearing what we've discussed today. And as always, I appreciate you. I thank you. Until next time, stay strong, stay healthy, keep pushing on.