Saturday, April 22, 2017

GIANT Duathlon 16/17 Season Finale : Results and Analysis

Image courtesy : Paul Venn / Race.ME

The final duathlon in the Giant Duathlon series was held on April 14, 2017 at the District One cycling track in Dubai. The race format was 3k-25k-3k for a total distance of 31k. 

In a previous post, I described the runing dynamics data from race 4. In this post, I will use a similar level of analysis of race performance and will be comparing to the data in race 4.

The full results are hosted on the Race.ME results page here and my splits are shown there. The top 30 in men's overall times are posted at the end of this post.

The strategy going into this race was simple - throttle down the first run a notch and put everything you have for the day into the cycling aspect. Because this was a fast cycling track, I knew that the most of the field would be thinking along those lines.

For comparison purposes, I made a table of my timings for all 5 duathlon races this season along with a key piece of information - my training stress balance as a function of TRIMP. This is shown in Figure 1.

Figure 1 : Comparison of race performances over 5 x GIANT duathlons during the 2016/17 season.

TSB is an algorithmic surrogate for "freshness" or "readiness to perform". It is understood that the more negative it is immediately before an event, the more fatigue you bring into the race.

However, surrogates are just that - surrogates. Trimp or TSS based training stress balances are helpful, but I think they do not capture the stress in an adult working man's life who needs to commit 40-50 hours on a day job per week. In future posts, I'll be examining some interesting areas of physiology which might capture that aspect.

Graphing all my race timings from race 1 through race 5 helps form a visual story of what went on this season. This is shown in Figure 2.

Figure 2 : Plot of splits in 5 x Giant Duathlons during the 2016/17 season.

As shown above, I improved overall race timing in race 5 by 6.3% compared to race 1 and it would be my best overall timing this season.

Furthermore, duathlons are influenced by course. In race 3, there was a +4.9% degradation in overall timing compared to race 1 which was held on the same track. 

The reason is attributed to the poor visibility and foggy conditions in race 3 which pretty much hampered the average cycling speeds due to the technical nature of the course.  I also described in a past post about an inadvertent cramp during the last run segment which lost me atleast a minute in that race. 

Between race 5 and race 2 (same course), I improved overall timing by 4.9% mainly due to high motivation and better conditioning. All those brick sessions and gym workouts have helped.

Looking at transition times, the trends is one that shows decreasing t1 and t2 times. It appears feasible that minor improvements can be made here considering that I pulled out of transition in race 4 an average of 45 seconds. 

But I should think the transitions are also influenced by architecture of the transition zones. Race organisers optimizing the length and entrance/exit of the transition during race 4 helped me shave 10-15 seconds compared to other courses.

Running Dynamics 

Shown in the plot below is a composite of run dynamics for race 5. It shows run power (W), form power (W), ground contact times (ms) and leg spring stiffness (KN/m) vs duration. I also show an estimated average power for the biking duration. Unfortunately, I did not measure biking power due to a pairing handicap on the Polar V800s.

Figure 3 : Composite plot showing power and running dynamics in each split during the final duathlon race of 2016/17 season.

As the running started out, there was an overreaching in power due to the initial excitement and surge. As things settled down, I moved into a rhythm of 256W average. GCT was 210ms for a cadence of 93.

Form power is a surrogate for the metabolic cost of perpendicular bouncing. The form power data in the middle of run 1 looks rubbish due to data loss but overall, I displayed a mean form power : total average power ratio of 0.24, i.e about 24% of power was devoted to vertical motion. How much of that can be improved upon is debatable. What I do want to emphasize is that in all cases, vertical oscillation data says I limited it to < 3 inches, which is good rough guideline.

The story of the second run is that all metrics, by virtue of accumulated fatigue, worsened relative to the first run. This is shown in Figures 4 and 5.

Running power fell by 11.7% and pace fell by 10.6%. There was in increase in ground contact time of 9.5%, possibly the body's response to limit metabolic cost.

The surrogate of energy cost of running, ECOR, increased by 0.73% which may not be statistically significant to corroborate the increase in ground contact time (GCT).

Running economy (RE), a ratio of speed generated to power to weight ratio saw a fraction of a decrease.

I also kept cadence nearly the same as the first run. This may have been a way to compensate for the fall in stride length and gait push-off power.

Also note in Figure 3 the overreach in power towards the end of run 2. That is me pushing myself up a short hill just before the finish line. Power then dropped on the downhill segment and climbed back up again slightly for the home stretch on grass. By then I was flattened.

Figure 4 : Tabulated running dynamics from race 5 of the Giant Duathlon series, season 2016/17.

Figure 5 : Tabulated % change in running dynamics metrics in run split 2 compared to run split 1 during race 5 of the Giant Duathlon. 

What I thought would be interesting is to compare the fatiguing aspects of the second run in race 5 against the numbers in race 4.

Figure 6 : Running dynamics compared, race 4 vs race 5.

As expected, I ran a faster race in race 4. A major reason for the slower running metrics in race 5 was from the strategy to go slower in the running to perform in cycling.

Which brings me to state that duathlon is a fascinating exercise in energy management.  The mental and physical exertion of the short format was pretty taxing on the body all season and the excellent competition from my peers in the 30-39 age group kept me on my toes. Kudos to all those guys.

Training, self-coaching and making improvements have been fun. Tracking training volume and fitness changes through good data collection and record keeping has helped quite a bit. Keeping a tab on data comes natural to me from my engineering background and a major effort going forward would be to cut down on the sheer number of metrics and focus on actionable aspects. Keeping it simple stupid works.

The top 30 times from race 5 is shown below. Most of the fastest times in the race were from those in my age category, shown highlighted in yellow. The deficit I have to make up to be among the top 5 is a matter of 8-10 minutes. Thinking about that gives me some chills, it's a big gulf to cover.

If some optimization is carefully distributed among the various segments of the duathlon, I believe it is possible to narrow down the deficit but the question is by how much. Moreover, duathlon is a changing landscape with old competitors leaving, new ones coming along etc. This makes for pleasant surprises in each race. Sometimes you're the hammer, but often times you're the nail.

I also understand that the organisers may be considering cutting down the number of GIANT duathlon races for the next season, which means the room for error gets smaller. It's a shame because folks like me want to do nothing with swimming and duathlon is a way to show my A game. A lower number of races will be challenging, but hopefully a motivating aspect as well.

Thank you for following.

Figure 7 : Top 30 in the overall men's standings in race 5 of the Giant Duathlon series. Total competitors = 165. 

Image of motor

Image of mundane object to apply motor

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