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I’m a data detective. I spend my days investigating clues in the TrainingPeaks dashboard, WKO charts, my “homemade” excel spreadsheets and even post-session comments from my athletes.

Why spend so much time examining this information? It’s where the clues to achieving your best performance lie!

Every time that my athletes (or I) race, I collect and analyze a series of metrics, including:

Pacing strategy, as evaluated by heart rate, power, rate of perceived exertion (RPE) and speed
Placement in the field
A summative narrative (prepared by the athlete)

The Narrative

Within a day or two of finishing the race, write a report to provide context for the day. Consider the following:

What did you learn?
What worked well?
What you are proud of?
What didn’t go well?
How well did you respond to things that weren’t in your control? (e.g., weather)
What are areas for growth?
What were your thoughts and feelings during the race? How did they influence your actions? What was or was not effective about your mental game?
How did your pre-race planning help or hinder you?
What was your fueling and hydration strategy? Did it work?
What were the circumstances of the day (weather, terrain, competition, etc.)?

Your narrative can (and likely will) go beyond this list of questions. Include anything that comes to mind—even if you don’t think it’s important.

When you (or your coach) conduct the analysis, the seemingly insignificant details can become key clues to unravel the mystery of how to continue to improve your performance for the next time.

Pacing

The key data for analyzing your pacing strategy includes:

Heart rate
Power
Rate of perceived exertion (RPE)
Speed

I start with overall averages first; then I split the file into the first half and the second half (or in thirds or quarters), as seen in the charts below.

07185-how-to-do-a-proper-post-race-data-analysis-fig3

07185-how-to-do-a-proper-post-race-data-analysis-fig2

Segmenting the file allows me to assess consistency across the race. The file above represents a 45 to 49 year old male’s bike leg at IRONMAN 70.3 Chattanooga. The athlete’s effort was indeed consistent, as his power did not fade, and he even went just a few watts higher in the second half.

From there, I assess variability index. (Read more about VI here). This athlete had a high VI, at 1.12.

For this terrain, the VI should be lower, under 1.1 and preferably closer to 1.05. So, I examine what the cause might be: Are there a lot of valleys and/or spikes in power?

The Power Distribution Chart in the TrainingPeaks dashboard or WKO can be helpful for this examination.

What about heart rate (HR)? In the first 30 minutes, his HR was elevated above 160 bpm (which was the cautionary “cap” we set in his race plan).

He recognized this issue as he was racing, and he adapted by temporarily keeping the power under his target to help his HR settle, which it did. His narrative confirmed that this was an intentional strategy.

In a 70.3 from the previous season, this athlete ignored the higher heart rate in those opening miles, and his HR never settled in that race, which led to a lackluster run. Not so this time! He raced to a 25-minute PR with a strong, negative-split run.

Heart rate can also give you insight into whether you were well-hydrated or well-fueled. Pace or power to HR ratio (Pa:HR or Pw:HR) gives you a sense of your cardiac drift.

When this number is high (above 5 percent or so), it can be a sign of dehydration, or working beyond your current fitness level. This is where your narrative can be helpful in providing useful context to interpret what the numbers are telling you.

With respect to the athlete above, his Pw:HR was below 5 percent. This coupled with his even effort and strong run means that he executed this race as well as he could for the conditions of the day.

The data further demonstrates how one segment of the race can affect another. For example, have you ever heard (or said) this: “I had a good swim, and a great bike. But, my run was terrible.”

Here’s the hard truth: You didn’t have a good swim and a great bike if your run was terrible. You had a bad race.

The athlete mentioned above clearly learned from previous experience of a bad race to execute a good race this time.

While I’ve focused on a bike file as my example, you should conduct a similar-style analysis for the swim and run with whatever metrics you have available.

I don’t rely solely on one metric because I think a consideration of all of the available information will help you build the strongest case.

Placement in the field

Analyzing the field goes beyond just a basic glance at overall placement.

You should analyze your splits at various points in the race (if the race results provide various split times). Does your placement rise or fall as the miles pile on?

For triathlon, analyze your placement within each segment. To do this, create spreadsheets that include the 10 to 15 finishers who came before and after you (in age group, gender, and/or overall—depending on your particular competitive goals).

I sort  four tables (fastest to slowest) by:

Overall time
Swim time
Bike time
Run time

Using these tables, you can see at a glance where you fare in each sport compared to the field.

The table below provides a sample. Athlete “J” is highlighted in yellow. The run is clearly his strength. This prep race tells us that we want to continue to bring his swim and bike closer to that run as he works toward his “A” race of the season.

07185-how-to-do-a-proper-post-race-data-analysis-fig1

Beyond an understanding of your placing, this information provides perspective if you missed a time goal. Let’s say your goal was to ride a sub-six hour IRONMAN bike (112 miles), but you wound up riding 6:15 in hot and windy conditions. You are disappointed, perhaps.

When you do a placement analysis, you find that the average ride time in your age group was 6:42. Then, you realize you had a top-10 bike split.

You also make some comparisons to the previous year, finding that ride times this year were about 15 minutes slower compared to the previous year. Maybe now you aren’t quite so disappointed.

There are many factors to consider in your post-race sleuthing—beyond the space and scope of this article. I hope that this has given you useful tips to get started as your own data detective. If you are honest in your analysis, you will find valuable clues to break the case of your best race day.

The post How to Do a Proper Post-Race Data Analysis appeared first on TrainingPeaks.

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