In last week's article, we talked about a holistic way of looking at sprint biomechanics – what’s been called a whole-body approach, and I shared Dr. James Wild’s initial forays into this from over a decade ago.
In this work, he developed a dual-axis framework which determined the motor strategy of athletes based upon their relative step rate - step length reliance as well as their push - pull dominance.
This week, I’d like to fast-forward to the current day, and specifically to what we do with our NFL, MLS, MLB, and EPL teams, as well as other clients in professional sport.
Let’s get right to it:
The above graphic shows the motor preferences of 44 NFL players [all the same grouping – ‘big skills’: TEs, RBs, LBs]. Each player is plotted as a pink circle, where the larger circles indicate a higher maximum velocity.
If you remember from last week, I shared the 2014 earlier iteration of this – where Dr. James Wild plotted an athlete’s step reliance [as rate or length] and their push or pull dominance.
The update in the 2024 version a decade later is significant: the vertical axis now compares step length to step rate, and the horizontal axis compares flight time with contact time. Each of these relationships is plotted as z-scores and standardized for step length.
Two big things jump out from these plots:
- The distribution of sprint strategies is significant – there are circles [players] all over the plot.
- The distribution of the fastest athletes is also significant – there are big circles [fast players] all over the plot.
These are two points that many sprint coaches already knew, but have been overlooked or ignored throughout the industry: there are many ways to sprint, and there are many ways to sprint fast!
In fact, every single individual runs differently from every other single individual – often in very significant ways!
The most important takeaway from this whole-body system approach to understanding an athlete’s mechanics is that it allows you to better individualize your training based on each athlete’s natural tendencies. This approach differs from the typical approach of many coaches – teaching the same technical ‘optimal’ model to all players regardless of their individual differences.
You’ll know by now that such an optimal model does not exist for all [check out this great paper from Dr. Paul Glazier for an overview of this important concept].
“Sports biomechanists and coaching practitioners are advised to be more circumspect with regard to interpreting the results of applied sports biomechanics research and have greater awareness of their assumptions and limitations, as inappropriate interpretation of results may have adverse consequences for performance and injury.”
– Glazier, 2019
Previously, I shared what we call a Motor Strategy Plot of a group of NFL football players, and I wrote about the large distribution of both strategies and effective strategies – where players were all over the plot, and fast players were all over the plot.
But what about a more homogeneous population? What would the Motor Strategy Plot look like for a group of elite sprinters?
Well – very similar, actually!
The below plots a group of elite male sprinters, using race and training data collected from 30 sprinters with personal bests all under 10.10 [in addition to a few other ALTIS sprinters]. In total, about 150 data points were used to create this plot, with all sprinters compared only to each other, not to any non-elite sprinters.
So what jumps out to you here?
Again, as we saw in the NFL player plot – there is a very wide distribution of both strategies and fast strategies.
There are fast sprinters everywhere. Usain Bolt, the world record holder, has an average relationship between flight and contact time, and a much greater reliance on step length than step frequency – but this doesn’t mean that’s the only way to run fast, does it?
Consider Su Bingtian – the sprinter who ran the fastest 60m split in history. The Su-21 circle shows his strategy just before his 2021 Olympic Games Asian record performance of 9.83. It’s very different from Bolt’s, isn’t it?
You might be thinking, “Well, duh! Su is pretty short, and Bolt is really tall. Of course, they have different strategies!” But what about comparing Su with American sprinter Christian Coleman?
As you can see, Su-21 relied on a ‘bouncing’ strategy – with very high step frequency and very short ground contact time. Coleman, on the other hand, despite being similar in stature to Su, has a more neutral strategy [but clearly biases towards spinning, being in Quadrant 3]. He shows a slight preference for frequency over length and a slight preference for contact time over flight time – but neither is significantly dominant.
Cool, right?
The best part is that this is now a major feature of the brand-new updated Motion IQ Linear Report. But what would you do with this information? Is this Whole Body Motor Strategy System more valuable to you than diving into the minutia of step length, frequency, contact time, flight time, velocity, etc.?
What do you think? I’d love to hear your thoughts on this.
Please come back when I'll be posting next week's article, in which I will share some of the ways we have been using this information with our own athletes, as well as with the professional teams we work with.
Thanks for reading,
P.S. Did you know the guys at VueMotion have updated the VueMotion / Motion-IQ app – which simplifies how you capture, upload, and process video.
Here’s the highlights:
The indoor and outdoor modes have been optimized to ensure high quality 4K 60fps video is achieved and minimizes any motion blur that is often present when using the standard iPhone camera.
You will have access to in app tools such as drawing and annotation, stop watch, video-comparison, and sharing ability – with the added benefits of being able to choose your test, distance and rep, tag the athlete and process it straight away to obtain your Motion-IQ analysis and detailed reports.
The app also provides an option for you to record multiple videos simultaneously from a single controller device such as an iPhone or iPad. This also provides flexibility when using a single camera, where users can control the video capture remotely without having to start and stop video directly from the phone.
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