Splits are all we have. They are like gold for the runner or coach, providing feedback on if the runner is too fast, too slow, or falling apart at the seams.
But think about a race-car driver and their team. What if the mechanics only had access to the speedometer. They had no data on how the engine actually ran, just the speed output of the entire car. It would be tough to know exactly what to adjust, and whether the fine tuning actually made a difference.
Yes, in the end, speed is all that matters, but how we get to that same speed differs.
The same principle holds for running. When we look at our running speed, how we get there differs between runners. Some runners turn-over like mad, while others have long loping strides. Some accomplish this by short ground contact times, others slam their foot into the ground putting a ton of force and hoping that the ground gives some of that energy back. The point is, we all have our stride signature, our way that we run a certain speed.
So when my athletes do fast interval work, we like to open the hood of the car, and look at how they accomplish running the splits that they do.
Today, let’s take a look using just one of these metrics tells a different story than the splits do. For now, let’s take a look at the stride rate data.
The workout consisted of 3×1 mile in a touch over 5 minutes and 3×800 in the mid 2:20’s. Within each repeat, the pace only varied by a second or so per lap, in other words, they were pretty even.
(To briefly explain, this is data on stride rate. The large dips represent the jog/walk time between intervals.)
What do you notice right away?
The athlete ramps up their stride rate, turning over like mad to accelerate, before gradually settling down. It takes around a minute to settle into their steady state, where for the next 4 minutes their stride rate stays between 182-184 strides per minute, in other words a remarkably consistent rate.
What does this tell us?
- To accelerate, this athlete is largely stride rate dependent. If we were to look at stride length or ground contact time, the differences are much smaller from beginning to end. Telling us that, they largely use a change in turnover to change gears.
- Settling- It takes them about a minute to settle into their rhythm. Once the athlete is in their rhythm, they do a great job of staying there (as shown by the flat line). The problem is, the athlete overcompensates drastically (200+ spm for the first ~1 minute) before settling in. This tells us that the athlete is a great rhythm runner once he or she gets to that point, but there is a large amount of variability before he/she gets there.
So what do we do with this data?
As a coach, this data give me a glimpse of how the athlete actually ran the splits that I recorded. I can now use this data to perfect the athletes approach to racing and training. In particular, since this athlete is training for 5k+ distances on the track, where being in a rhythm is paramount to success, we need to teach the athlete how to get into that rhythm quicker. They are wasting energy overcompensating by jacking up their stride rate to 200+ spm only to settle back down after a minute. We expect a little overcompensation at the start as the athlete accelerates from 0 to their race pace, but that should take place almost entirely in the first 100 meters. So for the next practice, we might work on the athlete getting into their rhythm by 200 meters, and then by 100 meters, continually working on getting to that point sooner.
This is but one example of how to utilize the data displayed above. The point isn’t quite on what exactly you need to do with the data, but instead to show you how it can be used to further elucidate what is actually going on. Just by taking a glance at a simple graph with one metric, we can understand how the athlete is actually running his race (or workout), further allowing us to hone in on making the coaching changes that actually work. By utilizing data, we no longer have to hope what we are doing as coaches or athletes will make a difference, we can actually measure it.
Steve Magness, Author & Coach
image credit: mandykoh via Flickr