Thursday, June 8, 2017

Your First Race by James Shaefer

So is this the year your best riding bud finally decided to take the leap from club rides and Grand Fondo’s and do a race?  

Is this you?  

Or do you just want to gain some skills and knowledge to become a better all-round cyclist? 

If you live in the mid-Atlantic, the season is just about to begin.  In the next couple of blog posts I’ll try to compile a few tidbits of advice to help you make that first race or goal event a positive and fun experience.   

Before you can enter in your first race you will need a USA Cycling (USAC) racing license.  You have several options for purchasing your first license.  At most races, but not all, you can buy a one-day license on race day when you register.  You can also purchase a one-time “try racing” single license through USAC. I would suggest this.  You will start to have a record of the events you participate in and this makes tracking your upgrade points much easier.  Speaking of upgrades, all racers are grouped into categories (think skill level) and age groups: Junior 9 to 18; two-year age groups, Senior 19 – 35, and Masters 35+ five-year age group. A new rider, whether man, women, or child, will start out as a Cat 5 and will have to finish 10 mass-start races to be eligible to move up to the next category.  More information on the upgrade process can be found here and you can contact your upgrade coordinator through your local association

Here are a couple of “make sure you do” so you don’t get “called out” or worse, depending on the officials on race day.  

  1. Find out at registration which side of your jersey your number needs to be placed.  My advice is don’t skimp on pins, if my number is flapping in the wind it drives me crazy.  Your number should be positioned so that it can easily be read by an official standing on the side of the road.  
  2. Your “kit” jersey and shorts can’t be your favorite Pro-Tour team.  No Dimension Data kits ( go Ben King) and your jersey needs to have sleeves (No sleeveless, triathlon type jerseys…It was 80o F in Virginia on February 19th).  
  3. And last but not least, when you are on your bike anywhere at the event you need to be wearing your helmet.

Hopefully the tips this month will help you get to your race ready to roll and not distracted so you can enjoy the experience.  

More to come in next month’s blog.

James Shaefer lives in Richmond, Virginia and is a Peaks Coaching Group Elite/Master Coach

Tuesday, June 6, 2017

Data Isn't Just Data

By Hunter Allen

In his book The Information: A History, a Theory, a Flood, James Gleick says that the basis of the universe isn’t matter or energy. It’s data. This is quite a profound and magical statement when you think about it. How we interact with data changes our lives—our cycling lives, but also our life in general, from your smartphone to your internet connection with knowledge from all parts of the world.

Data is just strings of bits. Whether or not it contains information depends on what you do with it and how you learn to interpret it. Interpreting data into useful information is a key skill we all need to improve in all facets of our lives. The more information we have, the more we can understand the role it plays in our lives and how we can become better cyclists and citizens in the world around us.

Using a power meter is one of the main ways we can collect data in our cycling training and racing. However, a power meter can only collect the data. We have to interpret that data into information that can be used to make changes in our cycling. Power meters collect this data in one-second samples in seven different channels, such as speed, cadence, elevation, torque, and even GPS. The key to making use of that data is turning it into information through software analysis in a program like TrainingPeaks WKO+, or education in articles and books.

Each category of data can give us some insight into an aspect of our cycling that we can improve or just learn about for a better experience down the road. Using a power meter on our bikes is really the only meaningful data capture device currently available in our world. (A meaningful data capture device, in my opinion, means that it has the ability to help us make changes in our training; it gives me the information I need to decide whether or not one of my athletes should do a workout, do 8 hill repeats or 12 hill repeats, or train his threshold power or his anaerobic capacity. This information makes my job more precise and efficient.)

There are seven main categories of data:

1. Point-related data (the power, cadence, or HR at a specific moment in a ride). This can help determine if an interval or exercise was executed correctly. It’s the simplest of the data you capture, and it drills down to the minutiae so we can determine if we held just enough watts for the required period of time. I look at point-related data daily with my clients’ files, and it’s something I learn many things from—how many watts an athlete cracked out for the interval, whether or not he paced himself correctly, and even whether he created the watts correctly using the right balance of force and cadence.

2. Warning system data. Data can be used as an early warning system. This data is comprised of many, many smaller data sets, and we need to look at this data over a long period of time. Unfortunately, in order for this warning system to ring the warning bells, your data set needs to include your rest days, your hard days, your races, and all your rides, no matter how easy or hard they are. This is a critical part of the warning system. If you’re missing data because you didn’t use your power meter in a race or because it had to be sent back for repairs, the integrity of the warning system is really compromised. My warning bells can tell me when a client is doing too much training too quickly and when overtraining could occur. Another warning could be a sudden and unexpected drop in your threshold power. While out on a ride doing intervals, you could use your power meter to tell you when to stop doing intervals when your power decreases below optimal levels in creating the right training stress.

3. Detector data. Data can be a detector. How fresh were you when you cracked out your best twenty minutes? When you blew on the big climb, what happened five minutes before it or ten minutes before it? Use post analysis of your data to better understand your failures and successes. When you succeeded, what exactly did you do in order to succeed? When you failed, why did it happen? Was it the tenth hill that crushed you, or was it the violent attacks up the tenth hill that crushed you?

4. Instantaneous data. Data can provide instant feedback. During a workout we continually watch our power meter in order to keep ourselves within required limits for optimal training, and this is where a power meter can help us in pacing. Cycling is a sport of pacing, and you have to pace yourself in a breakaway, in a long road race, in a short criterium, and in a century ride or gran fondo. Pace your effort on the hills. Pace yourself in your nutrition and hydration, as well. These are all key fundamentals to your success as a cyclist, and one of the beauties of using a power meter is that the data is instantaneous. You push down on the pedals and see the number on the screen instantly. There’s no lag time, nothing to wait for or download later. It’s right there, and it happens immediately.

5. Investigative data. Data can help us be detectives. If a problem occurs, we can use the data to help us detect the cause. Sometimes we have to dig deeper into the issue surrounding a success or failure, and reviewing the data may be the way we discover the true underlying cause of our performance. I spend a lot of time being a detective when I analyze an athlete’s data, asking myself questions like, “How many times did he have to attack, and how many watts were in each attack before he was able to get away?” or “As this athlete fatigues, does she choose a bigger gear because she has more natural strength than endurance, or does she just not have enough muscular endurance to begin with?”

6. Explanation dataData can explain why we go faster or slower than normal, but we have to understand what information the data is trying to tell us. We have to translate it. Like James Gleick said, “Data is only a string of bits and has nothing to do with information. The information comes from understanding, and that is our job—to understand it.” Why were your watts lower than yesterday? Is it because you were tired and couldn't physically produce them? Was it because you tried to test up a steep climb when you’re better as a flat time trialist? Was it because you were chasing your arch nemesis and therefore pushed harder than ever to beat him? This type of data is similar to the data we get from investigative data, but explanation data provides a quicker insight into the information you need.

7. Incorrect data or biased data. This kind of data is worse than no data at all. Sometimes we can adjust for incorrect data from our past experiences, but other times we have to throw it away. Incorrect data is easy to identify in most cases, but biased data is much harder to discern. Fortunately our power meters aren’t biased (I hope!), and therefore we rarely have to consider biased data, but our data can often be incorrect, which can pose many problems in analysis.

The data is always clear as a bell to see, but it's not always clear whether or not it explains the problem. We must first prepare the data in order to identify the problem, and this is what turns data into information. To achieve the right interpretation of the data, we need experience and a gift for joining the dots together in one picture (or just good computer software). I do believe you need to have a personal connection to the data and understand this information first for yourself before you can understand it for others. I’ve seen too many coaches trying to coach athletes with a power meter without having ever used a power meter themselves; they have no understanding of what 300 watts or 1,000 watts feels like. This data—this information we capture on a power meter—is unique in that we can associate it with a feeling and learn that sometimes our feelings are incongruent with the data and other times match exactly how it appears.

Experience and a basic knowledge of riding and racing a bicycle are essential. We are creating a harmony between man and machine. We’re trying to optimize what our bodies tell us about how it feels and what the data tells us about how we feel. Relying solely on data is dangerous and doesn't tell the whole picture, but the information we gather from the different categories of data can help us improve as cyclists and citizens of this world of data.

Hunter Allen is a USA Cycling Level 1 coach and former professional cyclist. He is the coauthor of Training and Racing with a Power Meter, He helped develop TrainingPeaks’ WKO+ software, and is the CEO and founder of Peaks Coaching Group. He and his coaches create custom training programs for all levels of athletes. Hunter can be contacted directly through

Monday, June 5, 2017

The Evolution of Running with Power - By Rachel Ruby Zambrano

Nearly two years ago, Stryd released their first version of the run power device, called the Pioneer. I took their device and I ran with it. Literally. Initially some things were clear.  There were very clearly defined zones for every effort that corresponded with already existing and scientifically supported pace and heart rate zones.  I proposed my initial zones in September and October of 2015, and posted them publicly so others could make use of them and validate them.  The zones were so successful they made their way into multiple platforms with some slight variations, and were published in many locations.  I held off publishing anything ‘official’ because my gut said there was more work to be done and I noticed some variation in power that related to my form and with my terrain that didn’t correspond with effort. In addition, run power was so new we didn't know how to use it. Coaches and athletes were treating run power like cycling power, and instinct said this was a mistake. There were also some other issues that we couldn’t address yet with the metrics we had.

Weight - weight plays heavily into the calculations, but by the time most athletes get to the run on a half ironman, they have lost anywhere between 2-5 pounds if they’re hydrating correctly.  If they aren’t hydrating correctly, weight may shift from gaining 1-2 pounds to a loss of 10 pounds.  Without putting these athletes on a scale, it is impossible to see how much calculated power has shifted.

Wind - we know by instinct that we run harder when we run into the wind, but Stryd data wasn’t showing us anything.

Rapid changes in temperature - since I race and train in Texas, race day usually meant if ice was available, I’d be dumping it down my kit.  The rapid change in temperature created havoc with power readings and essentially made the data meaningless.

There were other issues, but those were the big ones.

Then, a year after the Pioneer was released, Stryd reimagined and brought out it’s successor.  Now we had the Stryd Summit.  With the release of the Summit, we got a few new metrics that changed everything…

The crucial metric for me that got my attention was form power.  This is, as Dr Coggan calls it, the “cost of doing business.”  This is essentially the force required for vertical oscillation.  I don’t remember now why it caught my attention, but in WKO, I started watching the relationship of form power to overall power.  I noticed something that would later have an impact on how I ran. Typically, form power hovered around roughly 30% of total power, and seemed to be around 1 watt per kilogram of body weight, give or take 10%.   Significant fluctuations correlated with changes in effort. 20% meant a near sprint, and 35% was an easy jog. What became important about this relationship is that I had noticed run form was tied to the relationship of form power to overall power.

Rewind to when I received my very first Stryd device: one of the first things I noticed when I started running with it was that if I leaned into more of a body forward position and picked up my cadence, my overall power would drop and my pace would increase.  This was one of the reasons I was reluctant to publish my run power zones anywhere official - if power could drop so easily with a change in run form, how could I assign strict zones to different efforts and call it done?  I paced the Houston Marathon in January of 2016 with this knowledge - leaning into more of a body forward position, and with a target total power.  The tactic paid off - getting me a 10 minute marathon PR and a Boston Qualifying time.  What really stood out were the mile splits - so very near perfect from one mile to the next.  Something had to be right about this new technology and the way I used it to adjust my form.

Fast forward to the release of the new metrics and form power, and suddenly, I realized I had a way to not only qualify that body forward position, I could also quantify it.  It took a few more weeks to realize what I was looking at, but at the end of March 2017, I messaged Stryd and asked them if they could write a custom IQ field that would include the relationship of power to form power. Their development team came through. What I wound up with was a field that displayed “power - form power/power displayed as a percent.”  In numbers it looked like “200-30” where 200 is overall power, and 30 is the percentage of form power to overall power (assuming form power is 60).

So let’s talk about the significance of those numbers.  Overall power is a great representation of the energy required to move your mass forward during running.  Playing with form can give you an indication of how your body position affects energy, but in using overall power by itself, it was easy to “cheat” on a workout.  Simply slouching or sitting back into the run increases the force required to push the body forward, showing as an increase in power.  Essentially this means that simply evaluating power meant that while it may look like you were working harder to have a strong run, it was possible that you were sacrificing form, working harder to train at the same speeds.  Since form was failing, you were potentially creating an opening for injuries, by using muscles in a way not that does not equate to the running efficiency.  Allowing form to fail might mean overall power creeps up slightly, but it also means that form power will increase in order to push the body forward for the same speed.

I took the metric up to the track and did some testing. Since the new metric represents the cost of vertical travel, the idea would be to minimize the percentage (a number I came to call form ratio). After some initial track testing with the new metric on my watch, I realized my theory was valid, and planned to test it at the half ironman a few days later.   A couple of things came out of that test; some expected, some surprising.

Result 1: the new metric was effective for pacing.  I targeted overall power of 180 and a form ratio kept at 30 or less.  As fatigue set in, my stride length shortened, but form remained the same.  I credit maintaining form throughout the race as the biggest reason I was able to finish what was otherwise a disastrous race.

Result 2: hills need to be treated differently and required further testing.  Further testing with the new metric revealed that while grade has quite a bit to do with the exact target of vertical efficiency, minimizing the percentage helped to minimize the impact forces during the downhills and streamline the uphills.

Result 3: I found the wind.  One of the biggest struggles during the past two years is to figure out why we haven’t seen an increase in power during running when the wind is strong.  The reason is that we change our biomechanics naturally to adjust for the wind, and it wasn’t until we looked at the relationship in the mechanics that we were able to see the effect of the wind on the body and start seeing how we changed our power.

Result 4: We can finally cancel weight out. Since the metric is based on two weight based numbers, by looking at how they relate to each other, we can cancel weight out.  This is potentially an extremely valuable tool for endurance athletes that will be on course for more than 90 minutes.

I had the opportunity to further test vertical efficiency at the Boston Marathon this year, and was satisfied with the results. The course is a net downhill, but has some decent hills and climbs, and this year was noted for temperatures in the upper 70’s.  Not ideal for racing or qualifying again, but a great chance to put my new metric to the test in challenging conditions.

Result 5: body forward position is associated with a different foot strike.  To fire up the foot strike debate once again, photos from the Boston Marathon indicated that when I was focusing on the body forward position (minimizing the vertical efficiency) my foot strike shifted to a midfoot strike.  When I allowed fatigue to affect my form, I shifted to a body upward position and a heel strike.  Multiple pictures throughout the marathon indicated this was the case, and as I would remember to adjust my form, I would go from body upward (less efficient) and heel strike to body forward (more efficient) and midfoot strike.  This indicates that the portion of the foot that strikes first has much to do with body position - potentially making the foot strike debate unproductive - and instead shifting the debate to shin angle, posture, and “lean.”

Some final thoughts on this about where it’s going.  Right now we don’t know if the numbers are the same for everyone.  I targeted 30% for the half ironman, and 29% for the marathon. Downhill, I target 32-34% and uphill, I target 24-25%.  These numbers represent 8-9 minute miles over the last six months, however since fatigue tends to limit stride length when form is kept correct, fresh miles at 30% tend to be around an 8:30 pace for me, while tired miles seem to equal 8:45. Another question to the matter is how vertical oscillation changes when an athlete is fatigued in relation to stride length.  While it would make sense to say that vertical oscillation decreases in response to fatigue, one might also suggest that stride length decreases, leaving the form ratio without significant change.  

Get your STRYD power meter: Click Here

To pose one final question, the debate over treadmill grade to simulate road accuracy may finally be put to rest by using form ratio.  Simply put: is it possible to use form ratio to put the body in the same position to match outdoor biomechanics, and then, when form and endurance are sufficient, can form ratio be used on a treadmill to assist in improving outdoor mechanics?

A form ratio of 32-33. Note footstrike, shoulders, arms, and 'body upward position'
 A form ratio of 28-29. Note footstrike, shoulders, arms, and 'body forward position'

Below you can find a quick summary on the state of form ratio as an actionable metric.

Where are we now
  • Real time feedback on run form - metric is actionable
    • Tired athletes “sit back” into the run and slouch
    • Slouch increases power requirement for same speed
    • Body upward position requires more power for same speed
    • Either slouch or “sitting back” into the run will increase the ratio, informing the athlete that they are less efficient.
  • Weight changes are cancelled out
  • Allows athlete to maintain correct form
    • Fresh athletes with correct form perform faster
    • Fatigue limits stride length in tired athletes, but reduces additional cost of poor form
  • Responds to wind
  • Reduces impact forces to legs on downhill
  • Encourages better form/efficiency uphill
  • Photographic evidence that “body forward” position ties into foot strike
  • Body forward position will encourage efficiency
  • Both overall power and form ratio are important metrics and must be used together.

Where we’re going
  • Unknown if ratio is universal or every athlete has their own range. Suspect it will be similar to a power duration curve for every athlete.
  • Suspect endurance athletes will perform better with the metric phrased as “180-30”.  They need to focus on minimizing the form ratio, but some will prefer to have the metrics separated.
  • Athletes running less than 10k may perform best with separate metrics, and may be best maximizing the horizontal efficiency rather than minimizing form ratio.  180 and 70 vs 180-30.
  • Potential for use indoors on a treadmill to maximize efficiency and mirror on-road biomechanics

Who is this going to benefit
  • Fatigued athletes - specifically those running the back half of a marathon and triathletes
  • Athletes racing on windy days
  • Athletes participating in hilly events
  • Athletes who exhibit poor form/efficiency

What we need to be able to use this on race day:
  • Power (existing metric)
  • Three new metrics
    • Power-FRatio (200-30)
    • Form Ratio (30)
    • Horizontal Efficiency (70)

The bottom line is that this deserves more testing and we need data on a lot more athletes to really evaluate the value and potential of this metric. It is race day actionable, but what values to use that represent the best form/efficiency are still in question. A huge thank you to Angus and the Stryd team: they’re making this happen, and I believe we’re going to reshape the way we look at run coaching and training through the use of “mobile run labs” such as the Stryd Summit. And also a big thank you to Peaks Coaching Group Karen Mackin and also to coach Steve Palladino for helping me test the theory, and figure out the edges of the big fuzzy gray cloud in new running tech.

Rachel Ruby Zambrano is a Peaks Coaching Group Elite Coach. She lives in Cedar Park, TX and Her focus is on Triathletes and Runners. Click Here to learn more about Rachel.