JFK50 2015 – Good, By The Numbers

“A good decision is based upon knowledge and not on numbers.” – Plato

Context

You always remember your first time. Heightened expectations. Fear of the unknown. The butterflies in your stomach & sweetness in your palate come back every time you think about how that day exceeded all of your expectations and left you broken for days unable to walk down a flight of stairs.

The JFK 2015 wasn’t like that at all.

With a full 2015 racing calendar, the JFK50 was a late “bolt-on” to a very full year: with the Ultra Trail Hurricana,  NYC Marathon & the North Face 50K DC in the can, I wasn’t expecting my body to do much else this year. Cut to me finding out that the JFK50 was still not filled up ( OMG ) – I had a shot to redeem myself from my 2014 run ( my first 50 miler – please read this one if you want lots of good course info ) – there was unfinished business to attend to.

Could I get my time under 10 hours? Could I run the first set of hills? Could I run more of the course and walk less? Am I rested enough to try and push the pace? There were so many questions to answer?

What’s In A Number

Spoiler alert: I didn’t break 10 hours – not even close. And honestly, it turns out I didn’t care. This was the real shock to me. It’s fun to break time goals and set PRs – this time, however, the quality of the experience was just so much better – I had a BETTER time with a SLOWER time – wtf? That makes no sense to my tiny little man-brain.

Fortunately, I have plenty of magnets, wires and tubes at my fingertips to get to the bottom of this conundrum. So let’s dig in.

DRY ( Don’t Repeat Yourself )

First, let’s see what I have – Garmin Connect does a pretty good job of summarizing the basics ( both on the web page and the iOS app ). However, if I want to compare today with the race last year, it doesn’t quite fit the bill.

garmin_dataStrava is also pretty cool – I recently learned it does this automatic comparison graph of the same race segments. This shows that my overall pace was getting slower – useful, but not exactly insightful. I knew my finishing time was slower – this just isn’t making me feel any better. strava_diffs

Big Data In Search of a Problem

Strava. Garmin. We all do it. And I mean we ALL do it. SINTEF ( Norweigen for Science and Tech Shit ) estimates that 90% of all of the data in the world has been created in the last two years. Two years?!?! Crazy, right? The interesting thing is that most of us don’t really use that data to help us figure out what the hell just happened. Data is to be created, tracked and forgotten.

With my Garmin 2014 and 2015 data available for the same GPS device, for the same race, I was determined to find any pattern or insights given my limited knowledge of statistics and pie charts. Hmmmm….pie……

Simple_Simpson_(Promo_Picture)
“Did someone say Pie Charts!?”

WARNING: I am NOT a data scientist so some of these results may be…a bit…suspect.

Moving, Waiting, Thinking & Eating

The first thing I wanted to do was remove any noise ( i.e. “stoppage time” ). While it’s clear that you can be more efficient around aid stations, I didn’t really care about this in my analysis – in fact, in spite of FEELING like I stopped more in 2015, the data suggests that I stopped LESS often. I did NOT expect that. It’s actually about the same by a few minutes so not really that interesting. g1

Volatility and Standard Deviation

Here’s where things get interesting. My coach always talks about “even effort”. If this is true, I’d expect my average moving pace to have less volatility over time and hence, I’d be more efficient over a longer distance ( i.e. less bonks). And that’s exactly what happened. There is a 20% decrease in moving pace volatility – this is exactly why I had a better quality of experience – less surge pacing ( surge pricing? ) and not as much crashing. The numbers do play out.g2

 

 

Perception vs. Reality

Another insight: last year, I got stuck on the switchbacks and my perception was that I was behind the curve on pacing – this caused me to run faster my first 5-10 miles on the path. Given the post-race pain in 2014, I tried to correct for this in 2015. You can see this play out in the graph below – the weird thing is that I had less traffic on the switchbacks in 2015 and with less pressure on, I didn’t put on the gas on the canal.

Less burnout as a result, but also less speed. Perception drives reality and I’d say that 15 mile section was the main time-gap driver between the two races – even at lower volatility in 2015. At last, we have a culprit!

g5

 

Psychology & Pacing

OK, so now I’m just showing you some pretty graphs. I really can’t believe how much faster I was in 2014 – think I was really running scared given the aggressive cut-offs. This year, with a pacing band (an awesome idea) I was less stressed about making the cut-offs – I knew exactly where I was (even when the volunteers gave you wrong information – that’s a NO NO – thank you for your service but please never do this). The result: running slower. The psychology is on display here.

g3

Mind Games

23278487116_66a0c55415_o

Even with all of this data, of course you can’t capture everything. Fortunately I have a few engineering friends who made a few adjustments to my hardware before the race. What follows is likely a glimpse into our AMAZING future:

g10
Data capture appears consistent with facial expressions.
g7
All systems appear to be operating normally?

Other Factors

23222007611_df33a082eb_oWhile it’s clear that all of this data tells me “something”, it clearly doesn’t tell me everything. There were a few other factors obviously at play: some personal & some environmental:

  • Weather: while the weather was perfect temperature-wise, last year starting in the cold definitely helped me with speed.
  • Weight: heavier this year also likely slowed me down (both in beer miles AND in shoe weight – Bondi Bondi Wide vs my Clifton’s last year).
  • Wife: running with my wife was super fun – having fun does not a race make. Not for me anyway. The psychology is different and that impacts performance.
  • Weaves ( err, leaves ): lastly, the leaves – don’t get me started on the leaves. They had serious drag on the feet this year compared to last year. Slippery and playful.

g8

 

Nutrition / Notes

After the UTHC (65K), I was determined to eat less gels. While they really do the job in terms of calories, I think at lower intensities they simply aren’t required and that proved itself this time around. I only ate 5-10 gels ( not a lot ) and then everything else off the tables. A few other gems/notes:

  • WIFE, She Badass! – The wife kept me company for most of the race. Even with an injury she finished in style. Stubborn? Badass? Coach? No matter how you slice it, I’m one lucky bastard to have her in my corner.
  • Dunkin’ Donut Holes – Jelly Donuts – OK, these were awesome. Conservatively, I had 5 or 6 one station.
  • Red Velvet Cake – Redeemed myself from missing it last year. OMG this icing – I want to be coated in this when I’m buried. Or just before. Or now. Ok, now.
  • Training – Super taper worked as before. More rest than you think you need really pays off.
  • 50% Liquid Nutrition – The last 20 miles – Coca Cola (lots of it), Water, Broth was my primary calorie source the last 20 miles.
  • Generally, real solid food in the first half and all liquid calories in the second half – this played really well. The only exception was the mile 44 hot potato station – which ONE AGAIN, pumped me up for 2 crazy fast miles once they digested (46-48) – this happened both years.
  • Soft-Flasks: I haven’t used these in a race and they worked great. Not only do they let you avoid carrying a bladder ( don’t need it given the support ) – they collapse into nothing when you need the extra space for gear carry. Definitely going to use this more often.
  • Astro-Glide Lube – Don’t judge me – it works – zero chafing. Zero.
  • Bondi B Wide Shoes – Great for roads but still too heavy for this distance. No major blisters however some small ones due to lateral movement on the trails (made all the more slippery by leaves).
  • Performance Enhancing Kokopelli – the Trail Runner Nation podcast community would be very proud indeed. For best use, display prominently on left or right unsheathed calf.
  • After-party/triage units. Wow. I totally missed this last year. They had medical teams, pizza, massage therapists – really a first rate operation with people that were not ONLY qualified but also cared deeply about the race ( one doctor was in the 500 mile club ).

Conclusion

The numbers don’t lie: I had a great fucking time and my finishing time doesn’t mean sweet fuck all*. A beautiful day, with beautiful people doing some truly EPIC shit – if only all days were this perfect.

See you in 2016….

References

 

*Honesty check – the time sort of does matter because it means I don’t time qualify for group C without a sub-11 hour finish (I think). So if I want to come back in 2016 I should keep myself honest and crush a full, flat marathon. Worst case, I’ll raise money for the many great charities attached to the race – I should do this anyway – we all should.

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