How do you if your training methods are effective? The simple answer is that you track performance over time. If performance is improving then you can be reasonably confident that something you’re doing is working. Though, there is always the possibility that an athlete’s performance is improving in spite of what they are doing in training and not as a result of what they are doing. As a result, I think it’s worth trying to answer the more difficult question of why a specific training method is or is not leading to observed performance trends. I’m always skeptical when I hear coaches talk about their mitochondrial biogenetic protocols, maximum lactate steady state progressions, and standardized protocols that increase stroke volume. It’s not that I don’t believe these methods have been effective in improving an individuals performance, but rather that i’m skeptical about the reasoning and confidence in why said protocols are working.
There are plenty of protocols that should elicit a given adaptation in theory whether that’s been shown in a specific study or it’s based on extrapolation from pedagogy. However, when we put these protocols to the test with athletes we seldom have a reliable way of knowing how and why they lead to performance improvements. As a coach you may not even care why something works as long as it does work. But there’s a good argument to be made for why you should care.
At some point you're bound to encounter an athlete who doesn't respond to cookie-cutter training protocols. If you don’t understand what that individuals underlying limitations are and how to target that specific limiter effectively you may be at a loss for how to modify their training. In that scenario you can throw your hands in the air and tell them they’re a non-responder, or you can select another protocol at random and throw darts at the board with a blindfold on. Alternatively, you can use the process of inductive reasoning to come up with an educated guess as to what they need, then follow that hypothesis to its natural conclusion and put it to the test.
A low cost way to better understand the effect of your training methods and how they relate to increases in performance is through basic data science methods. In the picture above we have a Crossfit athletes rate of change of muscle oxygen saturation (ΔSmO2) plotted against maximum power output on the Echo Bike over a 36-week training period. ΔSmO2 clues us into the balance between oxygen supply and demand — the more negative ΔSmO2 becomes, the greater skeletal muscle oxygen extraction is relative to skeletal muscle oxygen supply.
When I began coaching the athletes whose data we can observe above, we identified that their maximal rate of oxygen extraction and utilization was a primary limiting factor for increasing their VO2max. Additionally, through speed preservation testing we determined that they needed to improve their maximum sprint speed (MSS) while maintaining their ability to preserve a fixed % of MSS over a set distance. My hypothesis is that these energetic limitations and sport specific limitations had a common cause. In testing we found that this individuals maximum rate of oxygen extraction was 4.5% MO2 per second and that their maximum sprint speed on the echo bike was ~1,315 watts. Over thirty six weeks of training we had them repeat the exact same protocol (repeat desaturation training) and we tracked their the highest power output elicited in that session as well as the most negative ΔSmO2 value. In figure I you can see these data points plotted against one another.
Over the thirty six week training period we saw a 31% increase in maximum oxygen extraction and a 20% increase in maximum power output. But, the real kicker is that when we calculated the correlation between their ΔSmO2 and maximum power output trends the R value was -0.95, which is a near inverse linear relationship between changes in oxygen extraction and changes in power output. In other words, for every increase in oxygen extraction and utilization we saw a proportional increase in maximum power output. Furthermore, when we calculated the correlation between their training progress on a weekly basis (in the specified workout) and their increase in power output the R value was +0.84. Collectively, these data points give us a strong understanding of how the protocol we used works, how it changes the individual’s underlying physiology, and how it relates to increases in performance.
In order to confirm that these findings extrapolate to a larger population, I then recruited 21 subjects to perform a six week exercise trial where they performed the same repeat desaturation training session weekly and we tracked percent changes in maximum power output and ΔSmO2. In figure II you can find the data form that trial. Again, there is a strong correlation (-0.93) between changes in maximal oxygen extraction and increased in power output such that the individuals with the greater percent change in ΔSmO2 also had the greatest increase in maximal power output. Now imagine that you apply these concepts to the bulk of your training protocols and you have a streamlined system for identifying an individuals limiters — rather than guessing what protocols to use when, you can create a surgical system for spotting and training limitations.
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