At times, the term “sports science” feels so vague and ambiguous, that regardless of what organized attempts you make to integrate sports science, you will always fall short in capturing the whole picture. As a matter of fact, that is 100% correct. Regardless of what you do, what you think you do, or what you want to do, you will never be able to fully understand a single individual, let alone every individual on all 27 teams… Sounds like an uphill battle, right?
Well, the good thing about sports science is that it is a failure driven process. Anyone who tells you otherwise is lying through their teeth. Unlike what might be the initial hopes and dreams of someone looking to get into or take on sports science, it will never be a utopia-like, rainbow filled process that will elucidate all of your problems. However, the exciting aspect of sports science is that right there! We don’t know, which means what we are currently doing without the use of sports science is also unknown. So, instead of not asking questions and thinking we are right, we might as well start looking for answers and accept the bumps along the way.
Another somewhat redeeming quality about sports science is that unlike most binary solutions (yes or no), there is partial credit. Regardless of the money you have to spend or the logo on your shirt, sports science can be integrated across the board. One might ask “Well jee-wiz Mr., that sounds too good to be true”. Well, believe it or not, it is true. Not to get too in depth in this topic (trust met, this could be a book), the nice thing about the sports science world is that we have researchers doing all of the hard work and all we have to do as integrators, is understand what those researchers are saying. You see, unlike big business data scientist, we have banks of “true research”. We don’t have to run high-level statistics across ungodly sized data sets to understand what might happen… We have science. Because of this, we get a little bit of a head start compared to other fields. Instead of firing off shots in the dark, we at least get to have the lights on with one eye open.
Now, before I go any further, I just want to make it clear that trying counts. No matter who you are or how smart you are, if you are trying to use sports science in any fashion to better yourself and athletes, I applaud you.
The Three Major Keys
This paper is not supposed to be a full guide to sports science. Hell, if you do have one, feel free to send it my way. I don’t think one can truly exist, but I am always up to be proven wrong (not hard, just ask my girlfriend).
Getting back on topic, I wanted to highlight three critical aspects of sports science. Yes, there are other aspects I am not going to cover, but these in my opinion are three, easy to grasp and apply concepts.
#1 Actionable Insights
The common integration of sports science is through the utilization of objective feedback to provide actionable insights. There is where the whole tech world has really helped out. We are now able to quantify way more than we ever have been able to in the past and because of this, we now have a whole slew of new information at our fingertips. This objective feedback provides information that can be immediately acted upon (actionable insights).
Here is a little example
Lets say I am getting ready for a fight and I need to lose 10 pounds over the next two weeks. The scale will provide me with immediate objective feedback as to where I stand in regards to my goal and will help guide what actions I take next. Lets take it one step further and say I don’t just want to lose weight, but I want to maintain my muscle mass and focus on fat loss. Now, I can use a DEXA to get objective feedback see exactly how my diet is affecting my body composition. Lets say I also want to make sure I am still powerful during this 2 week cut. Well, I can use daily force plate and barbell speeds to help objectively see how my power is changing. But…. What if I want to see my hormonal system and how it is responding to the increase in external stressor? I can throw on my Omegawave and get immediate feedback that will help decide how I should progress. You see, thanks to the research of many great scientists, we don’t have to guess as to what is important and how it can be measured, they already did all that stuff. All we have to do is use this information to provide insights that will help the training process
Now, you might be thinking, “That’s cool and all, but I don’t have money for all of that equipment”. Now, this is where sports science is cool, because there is typically a “next best”. Look at the previous paragraph and think about tools you have that can be substituted for the more expensive items. For example, instead of an Omegawave, maybe you get a phone app with HRV and keep track of their sleep hours and some subjective measures (irritability and arousal). Maybe for the power output, you just measure vertical jump height (how high they can touch on the wall). I think you get the point. The idea is, do what you can with what you have.
Before we move on to the next section, it is critical to mention that you should understand what you are going to do with the data you are collecting. This probably should have been mentioned earlier, but collecting data just for the hell of it is a good way to get nothing done fast. You should have a clear understanding of what you are trying to measure, why it is important to you and what you are going to do with the feedback you receive from it. If you miss one of these steps, the data just becomes that one impulse buy of a t-shirt that sits in the bottom of your dresser never to be seen again.
#2 Temporal Analysis
Temporal analysis is basically the older brother of actionable insights. For the most part, the same metrics will be used. This makes this whole sports science thing a little easier when you don’t have to keep collecting new data. The concept behind temporal analysis is that not all data you collect may have meaning right away. For example, you if want to see changes in max strength and test their max strength with a mid thigh isometric pull the day after their first workout, that data point might not mean much at the moment. However, if you collect this data bi-weekly to weekly, now you start to get an understanding of what the heck is actually going on. With temporal analysis, you typically look for trends in the data. For example, if you stress an athlete really hard for four weeks, you might expect their max strength to actually dip a little over this time period. However, when you pull back some of the volume you might start to see the infamous “supercompensation” and an increase in max strength. Typically, most injury red flagging is done using temporal analysis. You can see objective changes between specific critical tests over time and can determine whether or not what you are doing is increasing or decreasing these variables that you have determined to coincide with risk of injury. It seems pretty straightforward, but travelers beware, dangers lay ahead.
You see, we are human, which means we are biased. Unlike robots, we have emotions and like to think we are solving things to make us feel good (I do this all the time, which is why I know its an easy trap to fall into). Its called conformation bias…
Confirmation bias, also called confirmatory bias or myside bias, is the tendency to search for, interpret, favor, and recall information in a way that confirms one’s preexisting beliefs or hypotheses
I stole that from Wikipedia
Whether we are aware of it or not, we typically have preconceived notion about our training. These notions or biases are what can taint the process of data analysis. We may see a trend one-way and then immediately assume it occurred because of one thing or another. We try and retroactively justify certain changes in the data points. Now, this is not to say subjective analysis should not be done, because naturally it has to occur. However, being unaware of our own biases can ruin what we are looking for. There is a fine line we have to walk when looking at data through a subjective lens. It’s not a bad thing, but it can be.
Temporal Analysis Example
This is a quick little example of how temporal analysis differs from actionable insights. Below are two figures from the same graph, from the same athlete. Figure one shows the bar speed of two different loads over five days of measurement. Figure two is the same graph, but extended out to 20 days
Figure 1. Maximal bar speed (x-axis) of two different loads over the period of five days
Figure 1. Maximal bar speed (x-axis) of two different loads over the period of twenty days
One can quickly see that the temporal analysis over five days doesn’t give us much information to act upon. However, when we look at the same data, but over a twenty-day period, trends can be easily noted.
#3 Data Enrichment
Data enrichment is a long-term process that doesn’t just take weeks, but maybe years. Data enrichment is the process of giving more context and depth to the data you have been collecting. It helps you truly get a better idea of what is going on. However, you have to be patient. Data enrichment is basically the grandfather of data analysis. It is the old wise man that has seen more days than you can fathom. It encompasses all of your previous analytical actions and interventions. At much grander scale than just the individual, it helps you understand what works and what doesn’t work. Think of it like an old recipe past down from your great-grandparents. That recipe has been tested, modified and critiqued more times than you care to imagine. Through enrichment, we can start to better understand not only the data, but also our own process of collection, intervention and reflection.
Not too tough
The thing about integrating sports science is well, on the surface, its not very hard. However, as you know, technology can be tricky, but humans can be a pain. The thing about humans, we are not robots. We have opinions, feeling, and biases. Because of these lousy qualities that make us who we are, the integration part gets stuck in the mud at times. This is why sports science needs to a be a slow integrative process. The more aspects you add at once, the more duties people have to take on. The more duties someone has to take on, the more disruption you cause in their daily habits and the bigger the pain in the ass sports science can be. For this reason, optimization is a slow, step-by-step process. If you have ever gone on a diet, it’s the same pain. At first, you try really hard to make it work. But, the more work it becomes the more you want to stop doing it. For this exact reason, integration needs to a slow process that hinges on patience and communication. You don’t just get to do “science”. The human aspect is what really matters in this equation and until that is all settled, the data analysis is worth nothing.
Sports science is more than doable for any coach or athlete. It really comes down to what you want, what you have, and what you want to see. The more we act like it needs to be the perfect, harmonious interplay between all sciences and disciplines, the further we will ever get from making it work. We need to take our time, optimize what we can and have direction to what we are doing. The more you have distilled before you integrate and the better you know what you want out of the data, the easier this whole process will be.
Who is Max Schmarzo?
Max Schmarzo (MS, CSCS, ATC)-Max works as an Applied Sports Scientist at Stanford University. He is an author of two books, “Applied Principles of Optimal Power Development” and “Isometrics for Performance”, and also writes professionally for his website, strongbyscience.net. Max is the Chief Science Officer of Exsurgo Technologies LLC, the developers of the G-Tech, with products such as the G-Flight. His areas of expertise are in human performance and technology integration. Follow him on Instagram @strong_by_science
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