Ken Jakalski is head track and field coach at Lisle High School
As a sprint coach, my concerns should be answering two fundamental questions.
First, I’d like to know what really determines how much force a sprinter can apply to the ground considering that time to apply these forces decreases with increases in speed.
I don’t believe there are any current scientific explanations for this, nor have the researchers offered what kind of training would further enhance force in decreasing amounts of time in which to apply that force. That is for us to the applied practitioners to experiment.
The second question: why does a sprinter’s ability to apply force decrease as the length of the sprint distance he is running increases?
Our answer to this is always simple: fatigue. And we know that fatigue actually occurs more quickly in sprints than in distance events. But why is this?
We know that sprinting requires greater forces, and that these forces rely on faster firing muscle fibers, but that these fibers also fatigue faster.
Thanks to the Bundle/Weyand speed regression papers, we now know that max levels of neuromuscular activity reduce in a predictive manner as the duration of the sprinting distance increases. (Bundle, M.W. and Weyand, P.G.. Sprint exercise performance: does metabolic power matter? Exerc. Sport Sci. Rev.,Vol. 40, No. 3, pp. 174Y182, 2012) Neuromuscular activity reduced in a predictive manner is the key here.
And another significant aspect of their research is that it points to the mechanics of force application as determining these declines in speed. In other words, sprints aren’t like distance events where declines in speed can be attributed to the chemical energy input. As Dr. Weyand continues to point out at his seminars, “unlike the distance events, sprinting is demand driven and not supply limited.”
From a researcher’s standpoint, focusing on the mechanical output of the musculoskeletal system makes sense, because that output can be measured. Measuring chemical energy input can’t.
So what is that the Bundle/Weyand ASR research has provided that is important for coaches?
From their most recent paper:
“Our design strategies and force application framework have provided empirical, predictive, and testable outcomes that have not come forth from the energy supply-limit models. These include quantification of relative sprinting intensities, identification of a common duration dependency of relative sprinting performances, linking the duration dependency of performance to external force application, and the identification of a force impairment explanation for the duration dependency of sprinting.”
“The sprint portion of the performance duration curve predominantly represents, not a limit on the rates of energy resupply, but the progressive impairment of skeletal muscle force production that results from a reliance on anaerobic metabolism to fuel intense sequential contractions.”
For endurance events, metabolic energy determines performance. That we’ve known for a very long time. The aerobic sources of metabolism basically set the intensity of the musculosketal mechanics that can be sustained during these longer distances.
However, for sprint events, in the words of the researchers, “precisely the opposite is true: the intensity of the mechanical activity that the musculoskeletal system can transiently achieve determines the quantities of metabolic energy released and the level of performance attained.”
And how is this at all helpful to sprint coaches?
In addition to the value of the algorithm in terms of setting individualized goals for short bursts of high speed running, the research points to why there may be limited value in giving athletes who are sprinting from 2 to 120 seconds bouts of aerobic capacity training.
ASR or anaerobic speed reserve is basically a force application model set forth by Bundle and Weyand going back to ’03. What we know is that, as efforts extend from a few seconds to a few minutes, the “fractional reliance on anaerobic metabolism progressively impairs whole-body musculoskeletal performance and does so with a rapid and remarkably consistent time course.”
I use the formula to simply establish individualized speed goals for each athlete based on the speed regression algorithm found in their papers. So, in simple terms, ASR is a way to provide individualized bouts of high speed running.
Intramuscular coordination is the coordinated twitching of the small fibres within any given muscle. Intermuscular coordination is the coordination between different muscles.
Would high speed running improve these?
Frans Bosch talks a lot about muscle coordination. He mentions, for example, that “good coordination is pretension that avoids muscle slack.” He also mentions that “jogging is little pretension with a lot of slack.” He even describes as long distance runner as “just a sprinter with bad coordination.”
If we accept this notion, then higher speed running is a means to improve Frans’s interpretation of “good coordination.”
There are various kinds of workouts coaches can design once they have individual athlete projections from the algorithm.
For example, here’s one that has generated interest in our area.
Coaches who have seen this workout are always intrigued by how I’m able to set it up, and how cool it really is.
I refer to it as my “benchmarking” run for twenty-five seconds. Many coaches, like Chris Korfist, also use this notion of a benmarking time for the 200.
Here’s how I set up mine:
Using the data from the ASR algorithm, I’m able to get a distance each athlete should be able to run for twenty-five seconds. For each athlete, this distance will be different.
What I do is to chalk a line on the track (lane four or five) indicating the point each athlete needs to run to. I will have a separate mark for each of my sprinters. If they reach their goal–hopefully exceed it–I know they’ve improved since my initial T1 and T2 tests.
In the past, I’d simply set the scoreboard clock, say “go,” and then start the countdown clock when the reach their respective starting points. They then attempt to reach the finish line before the horn sounds on the scoreboard. This is not too bad for running one athlete at a time, but definitely not accurate if you’re running multiple athletes at once.
Now–with the power of FREELAP, all problems are alleviated and whole process is even simpler.
I set one transmitter at the start of the 200. My assistant coach sets the second transmitter at the chalked mark on the track representing where a particular athlete needs to get to. When the athlete passes the second receiver, he goes over to my assistant and gives him his time.
If it’s under twenty-five seconds, he’s achieved his goal. Athletes are happy, and I’m happy to see some progress. It also is yet another indication that they might need a retest on their T1.
If you are limited with watches and transmitters, running athletes one at a time is fine, but if you have more watches and receivers, you can easily run more than one athlete at a time. With additional watches and receivers, you can be creative.
You can place two receivers at the finish line of the 200 and then four receivers going backward on the track to each athlete’s goal distance(athletes get a running start). This eliminates the problem of the stagger. It’s very effective and efficient.
Over the years, I’ve invested in all kinds of technologies for use in making speed assessments, but aside from FinishLYNX for actual FAT times in meets, FREELAP has been the single best investment I’ve ever made for my track and field and field program.
The more you know what you can do with equipment, the more creative you can be relative to getting timed segments for your particular workouts.
Every kid has a specific goal distance, and a quality day means not just running endless reps of some made-up distance (with times dropping each rep) but athlete’s achieving something measurable that clearly reveals their level of improvement.
Maybe that’s why I like Christopher’s line so much: “What you measure, you improve.”
The origins of speed regression:
Back in 1975, I was trying to construct a “projection table so that my athletes could have specific speed goals for their workouts, and specific times they need to run the repeats within those workouts. I was never comfortable just telling athletes to “sprint at 95% effort,” since my runners simply had no idea what 95% meant based upon their current level of training.
In order to accomplish my goal of removing “guesswork” relative to high speed repetitions, I had only once source—work that Russian coach Valentin Petrovsky did in training Olympic 100 meter champion Valery Borzov.
I thought that Petrovsky’s approach was very forward thinking for the time, and I always envisioned eventually being able to do something with sprinters like I was doing with Jack Daniels’s vVDOT tables, originally presented in his book, Oxygen Power.
The problem with this approach is that Petrovsky’s table was very sketchy, and focused primarily on the concept a final 100 meter dash time segmented on the basis of fly 30s and 60s compared to block times at 30 and 60 meters. In other words, Petrovsky believed Borzov needed to focus on hitting specific times in the fly and block 30 and 60, since those times could project out to a time for 100meters.
Are there any immediate limitations in terms of using the algorithm to predict a 100 meter dash performance?
When measuring block times, average speed as a function of distance is not an exponential decay, and in fact, the curve will increase in the range from 10 meters to 100 meters and beyond for most athletes, not decrease. Therefore, you cannot use the original ASR algorithm directly to predict block times for short sprints
This is why I’ve never focused on ASR relative to its ability to predict short sprint times. There is no “formula” for acceleration. When coaches ask about this, I direct them to the original Petrovsky tables, with Petrovsky’s difference between a fly-in 30 for Borzov and a block 30 being 1.1. I pretty much have “accepted” this after looking at Professor Bruggemann’s Athens Project research. Bruggemann noted a “comparable speed development” in the elites he analysed, and he also concluded that “reaction time showed no relation with race result.” Bruggemann further noted the following: “It is evident that all finalists exceed the 10 m/s mark between 20 and 30 meters, and reaching an average interval time of 0.883s, which corresponds to a speed of 11.23 m/s during the next ten meter interval.” So, I often suggest adding 1.1, but with the caveat that this is by no means “formula based.” It does not track decreases in contact time with increasing steps, nor does it track the increasing length of steps out of the blocks.
Many coaches who had access to the original Petrovsky tables applied a similar fly vs. block time difference. For example, Jimson Lee (SpeedEndurance) noted the following:
“On a side note, I have always advocated the difference between a running start and a crouch start is 1 full second. 1 second has always been my magic number to account for acceleration out of the blocks.”
What is more good news for coaches is that Christopher has modified his speed calculator to allow for a block conversion that coaches can modify.
Throughout my career two things of note have happened to me that changed the way I do things relative to sprinters and middle distance runners. First of these was my exposure to Jack Daniels’s Oxygen Power, but even more valuable were the wonderful correspondences I had with him on a variety of issues related to that work, and what he was eventually doing at SUNY with his college athletes. Second was my exposure to the speed algorithm, and the great opportunity I had to spend time at Rice with both researchers Peter Weyand and Matt Bundle, discussing the how I was using their algorithm, as well as how I could translate their research to coaches in ways that I would never have considered had I just read their papers.
So the two most significant aspects of my coaching career I believe have come down to three scientists who chose to give me a considerable amount of their valuable time in going over these issues with a high school coach. These individuals are often way too busy to spend time with people who have questions about how their research translates from the lab to the track. They probably receive hundreds of these kinds of inquiries. And I’ve often wondered just how many whose situations are similar to mine have had calls that were never returned or e-mails that were never reciprocated, and, as a result, never do make that one contact with the potential to change the way they do things.
The irony is that many of those individuals, without the benefit of my good fortune, were– and still are– far better coaches than I could ever hope to be.