What is your data telling you?
When you know what you’re looking for you can begin to fully understand the story behind the run
Why Analyze your Data?
Many athletes utilize the real time data for their run, click save, and move on with their day. This statement could be taken broadly to describe how we process most parts of our running; more narrowly it shows that we’re not truly digging in as deeply as we could. There are more stories to be told about how a run was executed: how effectively you completed it, how efficiently you run it, and provide you with insights on how to move forward.
This blog is about looking deeper at the data, the work I do every day as a coach with my clients. I truly believe that just giving people an excel sheet or weekly plan is only serving them 20% of the time, with lip service making up another 30% of the equation. The other 50% on the table is how well did you stick to your principles? How well did you run the rep, the race, or climb the hill? How did you pace the effort? If it was too fast - why? If it was too slow - why?
What happens when you analyze your data?
When you know what you’re looking for you can begin to fully understand the story behind the run. This insight and knowledge is what helps you make fewer costly mistakes and truly move yourself forward more rapidly - if you know what to look for.
As many of you know I’m an ambassador for TrainingPeaks. The training analysis tool & training log used by professional cyclist, triathletes, and runners. As runners there is far more to the game than weekly mileage and mile splits, we can use our data to understand when we need to return to training principles, assess areas of weakness, and determine where things fell apart. Why would you just sit around and hope it gets better at your next outing when you can dig in and see where you made your mistakes! Let’s dive in!
Slow down in your Warm Up & Cool Down
This is what I look at first
When the first Tuesday workout rolls around with a new client, this is the first thing I look for in the file before even diving into the workout feedback. What most people don’t know is how often they’re “over running” their warm up and cool down miles. The biggest issue is that you’re never actually warm up or cooling down. Take a look at this file. If you were to look at the HR without pace data you couldn’t tell where the workout started and ended. I focus on this because newer runners tend to burn their matches before they get into the workout and ultimately end up fading or getting frustrated because they’re out of energy. As runners progress they tend to want to get through the workout and don’t want to slow down the cool down and end up with a HR that’s just as high as the main set.
More time at a higher HR should be a good thing right?
No, you didn’t just crack Fermat’s Enigma of run coaching but over running your cool down. The point of warm up and cool down are to prepare your body for a specific stimulus (speed, hills, tempo, etc), and cool down is to help flush post-workout waste, add to your weekly mileage, and allow your muscles to cool down in a controlled way. If we didn’t need warm up and cool down our bodies would respond perfectly to just getting out of the car and getting into the main set. Anyone else game for 20x 400m directly out of the car? Didn’t think so. Keep the hard bits hard and the easy bits easy. More stress means more waste productions which = more recovery time. You’re actually making it harder to recover when you over run your warm up and cool down.
Controlling Pace & Heart Rate = Efficient PA:HR
Efficiency is more than just mechanical
Pa:Hr or Pace to Heart Rate comparison known as “decoupling” is one of the most powerful metrics in the TrainingPeaks arsenal. It allows a coach or athlete to look at an interval, a particular run, or race and get an understanding of how well they manage certain sections. When we look at the data set above - we see that Pace follows the Heart Rate trend with only a few exceptions. This means that they are highly correlated and have only minor differentiations. The relationship here would be close to 0% meaning the 2 pieces of data are closely correlated. The ideal decoupling range falls between 0% and 5%. Once we go beyond 5% we start to see that athletes are having a hard time controlling pace either over running or lack fitness. Because we are dealing with 2 distinct data sets (Pace and Heart Rate) the following relationships are considered inefficient and are worth understanding. There are 2 statements worth seeing from both the HR and pace
Two Perspectives:
STATEMENT 1: If Pace is maintaining and HR is skyrocketing - you are “maxed out at that pace | If HR is climbing away from Pace that is maintaining - you are inefficient
STATEMENT 2: If HR is climbing away from Pace that is declining - you are inefficient | If Pace is dropping and HR is Sky Rocketing - you are “fading”
Poor Pacing - Inefficient PA:HR
Once you see it!
So let’s dive in and see this in real time. The workout prescribed was 3x 1 Mile. When you look at the blue highlighted area we selected the first mile rep ( I could have been more exact). The athlete shared that he felt great starting each rep and then it got really hard. Why might that be? Despite increasing pace each rep - what can be improved? What I can tell you is that each rep had a very high decoupling because the same mistakes were made nearly every time. What does this mean about fitness? Where does focus need to shift?
Every rep the athlete gets out aggressively and often settles in with a late acceleration. The first rep shows a longer slower HR ramp. However, look at reps 2 and 3 much shorter and sharper to 170 bpm!
Major changes come around the 300/400m mark - by now the athlete is starting to notice their watch or feel the effects of their mistake. By this point it’s too late to fix their mistake they can only hope to hang on. This is the mentality of rep 3. It’s aggressive, fall off a little and fight through.
What does this say about fitness? The athlete is over running these and doesn’t have much speed work in their legs, there is development needed. The development is more practice pacing shorter efforts before getting to longer ones. They are in good shape, as shown by how quickly HR drops after the rep. So we know the athlete has a great aerobic base and would benefit from a mentality change by being queued to progress the effort and hit the gas with 400m to go instead of surviving each rep.
Long Slow Decline - Progressive Inefficiency (easy to see)
Statement 2 in Action
The classic fade and die but on a much longer scale. We’ve moved from a single interval to a longer 30 minute effort. If we only scratch the surface we’d say that the athlete just needed to progress the effort and move on. However, what are the greater implications here? Was this a fitness test? What else can we determine about this athlete other than pacing?
If we assume the initial pace was their current threshold and we started to see this fade we can ask a few bigger questions
Did environment come into play (heat humidity?)
Has tis athlete been consistent? Have they lost fitness in training?
Have they lost specific fitness that would indicate this was a poor testing protocol?
If we assume this is pacing alone we can confidently say this athlete did a poor job and queue a few other factors
Look at how low cadence is - athlete tired significantly
Look at how sharply speed drops
Look at how high HR rises and maintains
Look at the IF of .95 - 5% off threshold
This might have been a “miss”
The athlete may have had a bad day for many reasons - give feedback and move on!
What can we infer from a long run? Advanced Data Trends
What do we make of all this?
Looking Closer
To truly paint the picture, you use broad brush strokes and detailed ones to create a vibrant picture. When we break down a 4.5 hour run we’re balancing to competing priorities: objective vs. subjective data and correlation vs. Causation. To save this from being too long we’re going to take an objective view and leave the athletes comments out of the picture. As I’m the coach of this athlete I’m going to share a few things I gave as feedback on a recent call.
Feedback
We discussed how aggressively he took the first climb didn’t set him up for success the rest of the day. Too many matches were burned.
The relatively high HR on the descent of climb 1 - we’re working on increasing confidence on downhills; this athlete is not an efficient downhill runner.
The general overall effort - while the pace produced an IF (intensity factor) of .69 average HR was 153 with many points over 165 and a max of 179.
This athlete needs to be willing to slow down and control these long efforts. The desire to get fast has often been overridden the principles of aerobic development. This athlete needs more slow aerobic time to progress their fitness.
Fatigue after 20 miles. Look at that HR! Pace is declining big time and the incline is miniscule. This shows us that this was a big effort and we had started to approach or even reach beyond the level of our fitness (at this higher than necessary) effort
Take Aways
If you have one take away from this - it’s to be engaged with your data. Review it in a space that you feel comfortable. Garmin Connect, Strava, and TrainingPeaks are all great tools to review your data.
Take a close look and see where you’re being inefficient
Make the main set cont - don’t burn your matches in the warm up and cool down
Be willing to go slow to go fast!
If you have questions or would like me to review a file for an upcoming post for free - send me an e-mail: andrew@lifelongendurance.com