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My Running Life…In Graphs

Posted by kevinwolz on September 22, 2011

I have been waiting a long time to do this, but I just never had enough data to make it possible. Now, the time has come. The following post will give you intense insights on my life, training, and even the world in general. Get ready for your new love of graphs.

 

Let’s start with some basic graphs just to make sure my data is accurate.

I Run Faster When I Try Harder…Duh

X-axis: Subjective Effort

Y-axis: Average Pace

Insight Classification: Intuitive

Explanation: Okay, so this one tells us something pretty intuitive: As my effort increases, my average pace drops (smaller bars mean faster pace, ignore colors). Obvious, but nonetheless rewarding. Note: the effort scale is subjectively entered after each run and ranges from 1-10.

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Oops…You Might Not Be Able To Trust That First Graph!

X-axis: Subjective Effort

Y-axis: Cumulative Duration of Runs

Insight Classification: Statistical Limitation

Explanation: Before we make any dramatic conclusions from that first graph, we need to know if the data set is large enough and unbiased. That’s where this graph comes in. It shows the cumulative duration of runs that I have logged at particular effort levels. What it tells us is that the first graph may not be a fair comparison for two reasons: 1) The data sets for higher efforts are much smaller and may therefore not be enough for statistical analysis.2) See how the color switches from blue to yellowish to red as the effort gets harder? Well that’s telling you that I race at a higher effort than I run workouts, which are furthermore run at higher efforts than easy runs. Therefore, the conclusion from the first graph that I run faster when I try harder may not be completely unbiased by circumstance. Perhaps we could try to separate out races, workouts, recovery runs, etc.?

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I Run Faster When I Feel Better

X-axis: Subjective Run Quality

Y-axis: Average Pace

Insight Classification: Intuitive

Explanation: This graph is fairly similar to the first one. An important part of my log entry after each run is a quality rating of how I felt on that run. Just because you feel good doesn’t mean you should run fast, but clearly it helps.

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It Takes Me Longer To Run Farther

X-axis: Distance

Y-axis: Duration

Insight Classification: Intuitive

Explanation: Sure, another intuitive relationship, but nonetheless interesting to see that the trend is so strong and with relatively little variation. Anyone want to bet that the slop of that trend is about 7.5?? Also, notice how the different bar colors group together in this graph. This is no coincidence. See the legend to make sense of  it. A question for my fellow runners: Could there be any better proof that “Badger Miles” are in fact accurate over the long-term? However, if I had implemented badger Miles over the last three years, we wouldn’t be able to analyze all these amazing graphs now would we?

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Now let’s move on to some interesting graphs that reveal some things about our climate.

My Running Log Is A Weather Station

X-axis: Temperature

Y-axis: Cumulative Duration of Runs

Insight Classification: Climate

Explanation: Okay, this one’s a bit more complicated. On the x-axis we have the temperature that I recorded for a run, and on the y-axis we have the cumulative duration of runs (in hours) that took place at certain temperatures. Individual chunks within each bar represent individual runs. So what does this graph say? Well, assuming that I ran almost everyday in the last three years, this curve tells us that annual temperature in the midwest falls into a bell curve or “normal” distribution. Intuitive, once you understand the data. It will be interesting to see how this curve shifts in the coming years…

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By Far The Coolest Graph My Data Can Make

X-axis: Date

Y-axis: Temperature

Insight Classification: Climate

Explanation: This is an amazing documentation of the annual temperature variation in the Midwest. You think I could generate climate models solely based on my running log?? Makes you think about the power of crowd-sourcing data sets. Check out that ONE run below zero…Gadz, remember that fartlek? You took the bus home.

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And finally, let’s move on to the real analysis: My Training

For this portion, almost all graphs will compare pace with some variable. While pace is not the only measure of fitness, it is a pretty good general proxy in this context. Keep in mind that a lower pace is faster (i.e. smaller bars/values are better).

My Running Life

X-axis: Date

Y-axis: Daily Distance

Insight Classification: Life & Training.

Explanation: What a masterpiece. Some interesting questions to ask:

1) What was I DOING on the day of that huge spike?! Well, that would be when I raced my own half-marathon and then went back to run the last bit of Mom’s with her as well…and then cooled down.

2)Why does a cyclic pattern emerge? Each cycle represents on training season, of which there are two per year: cross country and track. Each cycle starts with low mileage to recover after the previous season, steadily ramps up mileage as fitness increases, peaks mid-season, and finally tapers off at the end of the season for top race performance.

3) Why is there a weird dip in the last big cycle (last spring)? That would be what happens when you have tendonitis in your IT band.

4) Why the heck is there a huge gap at the end of the gap? This one has two answers: Costa Rica and Panama.

5) Why can I so easily find and explain the variations in my training? Because RunningAhead is AWESOME!

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I Love Long Runs!

X-axis: Distance

Y-axis: Pace

Insight Classification: Training

Explanation: The colors really come into play on this graph. Most importantly, green represents longruns, which is a technical term that applies to the one run per week that accounts for about 20% of your weekly mileage and aims at increasing aerobic capacity. Yes, to runners, longrun is one word. Contrary to what you might think, this graph shows that I like to run my longest runs the fastest (except of course for those yellow ones which represent interval workouts, but those are kind of their own category). I firmly believe in the idiom that “long, slow runs produce long, slow runners.” Not all runners feel this way, and I would be VERY interested to compare this graph with some of my fellow runners (ahem…Sean).

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Things Don’t Always Stay The Same

X-axis: Date of Longruns

Y-axis: Pace

Insight Classification: Training

Explanation: This graph singles out longruns that I have run over the last three years and charts their pace. During my senior year of high school (first year on the left), you can see that the vast majority of my longruns were at a sub-7-minute pace. Those were the days! I had more runner’s highs on those longruns at Argonne than I ever have in my life. However, once college hit, you can see a pretty clear shift in average pace, with many more slower longruns. Why is this? There are many reasons that have caused this switch, including crappy places to run (i.e. C-U), coaching philosophy, and teammate philosophy. Unfortunately, none of those variables are quantitative, and therefore we can’t graph them. :.[

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There’s Always The Confusing One

X-axis: Week

Y-axis: Average Weekly Pace

Insight Classification: Training

Explanation: This graph kind of confuses me. I think there are so many different forces at work here that individual patterns are hard to make out, especially since this graph averages together all the workout categories. Nevertheless, you can still see the transition from high school to college almost exactly, as in the previous graph. Also, there seems to be a cyclic pattern similar to the first graph in this section, with the only difference that the cycles are exactly opposite. This means that, just as mileage increases with fitness, average pace decreases. Interesting. Jake, I bet you never thought that the effect you had on my training could be so distinctly characterized by the simple intersection of a graph and the x-axis…

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Usually How My Biology Lab Reports Look…With Insignificant Data

X-axis: Temperature

Y-axis: Pace

Insight Classification: Training

Explanation: This was the graph that I have been most excited to  make. I was certain that temperature would have a strong (negative) influence on pace, but this graph shows that there is really nothing going on between these two variables. However, all hope is not lost. My temperature data set is only about one year old because I had previously (naïvely) ignored this input in my logs. Therefore, I think there is just insignificant data to draw any conclusions on this relationship yet. So, stay tuned…

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Runner’s Highs Are Rare

X-axis: Subjective Run Quality

Y-axis: Cumulative Duration of Runs

Insight Classification: Life

Explanation: WOW, what a beautiful bell curve! Gaussian distributions don’t just occur in textbooks! This graph basically shows the frequency at which I assign particular quality values to my runs. You can see that those 10/10 runs where I get intense runner’s highs are pretty rare. Fortunately, those really crappy 1/10 and 2/10 runs are also rare!

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I LOVE GRAPHS. And I bet you now do too…

 

The previous graphs are based on self-documented training data of long-distance running over the last three years. All credit for data storage, manipulation, analysis, and graph creation goes to the amazing Eric at RunningAhead.com, the BEST online running log ever made. Any success I have experienced in my training over the last few years is in no small part due to the amazing analysis and documentation I can do using the ever-improving and free website that he as created. The running community is truly indebted to his kindness and genius. Thanks Eric, this one’s for you.

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