Sunday, April 23, 2017

Commentary : The Environmental Impact of Pre-Ride Food Choice and Driving to a Bike Ride

This is an Earth Day special commentary that I wrote on my Facebook page. The post was inspired by a similar opinion post along the same lines written by Thorpe & Keith from the Keith Group. They provide a host of reasonable references from where the numbers were picked.

The environmental impact of "getting to a ride" by car is analyzed below. What is also interesting to think about is the environmental burden due to choice of diet, which makes big marginal differences depending on what you put in your mouth.

Attached below are some graphs from a simple estimation of CO2equivalent emissions for a cyclist commuting for a typical 100km weekend ride. 

In one set of graphs, the cyclist is assumed to have eaten a 'typical American' diet and vehicle emissions were derived from test data for assumed 90kph driving speed. 

In scenario 1, driving distance = 20km in a conventional 4 door automatic petrol engined 2L Honda Civic. 

In scenario 2, driving distance = 20km in an automatic petrol engined 3.6L Porsche Panamera 4 PDK (Euro 5). 

In scenario 3, I look purely at impact of driving a fuel efficient, ULS diesel powered 2L Skoda Octavia Hatchback (Euro 5) as support vehicle for the cyclist (commute to ride is neglected). However, due to support function, the Skoda will be driven the 100km at a slow speed matching the cyclist (let's say 30-33 kph). Only the diet for the cyclist is taken into account, while that of the driver's is neglected.

Please note that life cycle environental impact of production of the vehicles, the bicycles and the construction of a public road system that the cyclist and driver utilize are neglected in the analysis.

The comparison is interesting if you account for the slow speed fuel efficiency degradation of any conventional vehicle. If you assume a 15% fuel efficiency derate due to low speed vehicular losses in the Skoda, then on a per km basis, I believe it makes little difference to be driving a Civic fast to the ride or a Skoda slow as a support car - they are more or less equally polluting regardless of what the sticker fuel efficiency is.  Meanwhile, it is approx 30% more polluting to carry bikes on a Porche when driven at similar highway speeds as a Civic, no surprise there.

The takeway? Reduce driving, carpool if possible and keep the luxury car at home. When driving, drive at the sweet spot fuel consumption speed. In urban environments, it helps to live close to a cycle track.

And finally, reduce or eliminate the use of conventional fuel support cars on long rides as pollution is spread over a larger area. If that's not possible, choose lighter cars with smaller displacement engines and reduce the number of start-stops. Start-stops cause relatively more pollution than constant speed driving regime due to the repeated accelerations and decelerations the vehicle must go through.

What is also interesting is to look at the effect of the cyclist's diet for this ride. In the first set of graphs above, I have assumed the cyclist to eat a typical diet with a life cycle burden of 2.6 gCO2eq/kcal. The carbon intensity of that ride on that diet is an average of 30-40 times less than driving the respective cars on a per km basis. 

However, if the same cyclist chose to eat a high protein meat rich Paleo diet with a pollution burden of 5.4g CO2eq/kcal, the difference is halved. For example, in the case of driving the Civic to the Paleo fueled ride, driving is only 15 times more polluting than cycling relative to a normal diet. Stunning if you think about it.

What I'm pointing to (as others have pointed out) is that on a gram of CO2eq per km basis, the high meat rich diet can be worser off than the transportation fuel in terms of the embedded carbon intensity (what goes into the production of the fuels). The reason can be attributed to the carbon intensive nature of raising cattle to produce beef. 

So the takeaway from this second analysis is that when possible, substitute lesser energy intensive forms of protein in your diet for fueling the ride. And while it, keep the beef away from the support car driver!

Post script : 

Just because cyclists reduce or avoid consuming a meat rich diet does not necessarily mean that the product will not be produced. Supermarkets will still carry the meat and someone else will buy and consume it. So on a global level, perhaps we'll see the same environmental burden of meat consumption.

However, if you account for the fact that products are sold because of relative consumer demand, if one segment of the market shifts outlook and reduces meat consumption, we can assume that somewhere else in the chain, production maybe cut. I'm not sure if its as simple an analysis as that and what timescales it takes to shift mindset overall. Something to think about though.

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

Sunday, April 2, 2017

Actionable Intelligence for Running Part 8 : Effect of Shoe Type and Footpod Mounting Position on LSS and Power

Update, April 6 2017 : Since writing this post, several comments have been triggered on Stryd's Facebook community

Stryd developers acknowledged the data sharing exercise and this led to a conversation about the "sweetspot" in mounting height where this device has been validated. The sweetspot is somewhere 10cm and below. Andrew Coggan PhD is also pretty active in these discussions :

*  *  *

In Part 3 of this series, I had conducted an experiment with the Stryd to understand what had a bigger effect on LSS - running cadence or ground contact time (GCT). While high step frequency is related to high LSS, I found that lesser GCT's have a stronger correlation with high LSS than was higher cadence. I also found that at a fixed step rate, there existed a monotonic positive relationship between speed and LSS. 

Since then, I've analyzed much more data and plugged them into my own spreadsheet models. I'm fairly certain to have cracked the math behind LSS, i.e knowing what parameters affect LSS.

However, before I put my entire weight in the LSS train, it was important for me to answer one question. Does shoe type and where you mount the Stryd footpod affect the reported values? This a fundamental topic not only with the Stryd but any consumer device that measures athletic performance.

The reason it's fundamental is that when you track your own changes longitudinally over the course of a season or several seasons, you need to be sure that the % change in your own performance is greater than the noise introduced by the device you're using to measure with. If instead, the noise is greater than the % change, any change you say you made through training is unclear or baseless scientifically.

Discovering what is the "noise" in the Stryd LSS data is the subject of the post. Results are organized into two sections - 1) with the Stryd correctly oriented with the pointed edge facing down, and 2) an earlier test with the Stryd oriented in the upward direction. In each of these orientations, 2D position (X,Y) of the footpod was varied on the laces. I also tested with 3-4 different brands of shoes at two different running speeds.

Readers will find the results in section one with the Stryd in the correct orientation useful, as it is the recommended installation position. However, it will also be interesting to see what happens when the footpod is oriented in the wrong direction.

Actionable intelligence is highlighted in blue.

Motivation for Experiment

Attached below is a table of run data I have consolidated spanning 2 months. I've also included body weight and landing style as best as I recall.

Fig 1 : Collection of some run data from Jan 24-March 31,2017. Click to zoom.

Some runs didn't make sense in the context of a "few parameters".

A low GCT 800m track race run on clay track in Asics spikes yielded lower LSS (9.8kN/m) than during a slow jog on a tiled footpath in the Under Armor Bandit 2 (10.8 kN/m).

Meanwhile, a 2:53 interval run done in Nike Lunarlon Lunarglides on a purpose built concrete bike path showed high LSS (12 kN/m) but a similar GCT interval done in Mizuno's at faster pace was 0.5 kN/m lower.

More recently, a high speed VO2 max test done in a test lab with Mizuno's at somewhat lower power output than the track race also showed high LSS (10.6 kN/m). Puzzling that the slow jog in the Under Armor shoes displayed a slightly great amount of LSS for a GCT that was 50% higher than that in the VO2 test.

The complete list of LSS values are within -6% / +16% of the mean value of LSS in the table. One question to answer would be whether this much spread in the reported LSS is normal in the context of wearing different kinds of shoes. In that respect, it would also be interesting to study how different shoes and footpod mounting positions affect the reported LSS.

Design of Experiment

Fig 2 : Experimental equipment included 4 shoes, a Stryd and a Cybex 770 Treadmill which has it's own display of power.

Step 1. Round up 4 running shoes/sneakers from my collection (all I have). Label them A-D. Some basic characteristics about these shoes are shown below. Because I do not own a low drop shoe, the best I could find was a pair of Fox Motion sneakers from 2011/2012 which had a drop of 8mm. 

Fig 3 : Description of shoes used in the experiment.

Step 2. Choose 2 treadmill belt speeds to carry out test. Speed 1 = 10kph. Speed 2 = 12kph. Speed 1 would be run for 5 minutes and Speed 2 would be run for 3 minutes preserving a basic duration vs intensity relationship. 

Step 3. Choose two mounting positions for the Stryd footpod. Position in 2D space was defined as the X-Y distance of the footpod on the shoe. X distance was measured from medial malleolus of the tibia-foot interface to the center of footpod. Y distance was measured from the floor up to center of footpod. 

Fig 4 : Definition of X and Y footpod mounting positions.

The X-Y positions on all 4 shoes were as follows :

Fig 5 : Data table of X-Y positions of footpod. Click to zoom.

Step 4. Randomly assign the shoe A-D to run the test in. Within each shoe selected, randomly assign running speed with mounting position 1. The experiment table below will speak for itself. 

Note that in the interest of time, all shoes were tested twice to check repeatability except for shoe D. All shoes were tested atleast once with mounting position 2. All shoes were tested a total of 5 times each, except for shoe D. 18 tests were conducted in all, spanning more than 2.5 hours. 

Fig 6 : Protocol for randomized testing. Click to zoom.

Step 5. "As far as possible", all run tests for a specific speed would be carried out at similar cadence and the same running style. This was done to control step rate.  Note that I did not use a metronome which would have been a better way to control step rate. I accept that there would be some variation in step rate.

Step 6. All tests would be conducted using the Styrd App on treadmill mode. Treadmill would be set to 1% incline to mimic the stress of running outdoors.

Step 7. Collect data into post processors for analysis.

Part I

- With Stryd Footpod Mounted With Pointed End Facing Down (Recommended Position)
- Controlled Cadence

Results and Discussion 

In this test, I used only shoes A, B and C due to lack of time.

I controlled cadence using a metronome tone set to 90 cadence (180 steps per minute). Results table is shown in Fig.7.

Fig 7 & 8 : Results table for controlled cadence tests using 3 shoes and 2 footpod mounting heights with the Stryd installed in the recommended orientation. Cadence was controlled in this test using a metronome tone set to 90 cadence. 

Conclusion : LSS registered increased values at increased mounting height regardless of shoe or running speed.

Sample std deviation (10kph belt speed) = 0.93kN/m
Sample std deviation (12kph belt speed) = 0.65kN/m
%Variation from mean (10kph belt speed) = -9% / + 12.3%
%Variation from mean (12 kph belt speed) = -4.2% / + 8.9%

Variation appears to decrease with speed, although this test is limited to two speeds.

If the Stryd can show this much variation just from moving it's position higher on the shoe, I think it is best I control this position on a given shoe for my training going forward. However, when changing shoes, I understand that the absolute position from center of pod to the ground can still change due to the different exterior designs among shoes.  

This also means comparing my LSS longitudinally across a season to track improvements is a bit troublesome until I know that the pod had more or less the same ground height when installed on the shoe I chose to wear.

I'm happy to hear about the discoveries with your own device if variations are any different.

Part II (Optional Reading)

- With Stryd Footpod Mounted With Pointed End Facing Up (Not The Recommended Position)
- Uncontrolled Cadence

Results and Discussion 

II.A Effect of Mounting Position 

A 3 minute test conducted at 10kph belt before the 18 tests showed that freely chosen step rate = 178 spm. The step rate objective was to try as far as possible to stick with one cadence for a specific speed, without frying myself at cadences I'm not used to.

The full results table is shown below and sorted by shoe tested and then by ascending order of speed and ascending order of GCT. This makes repeatability and mounting position comparisons easy. Click to zoom in.

Fig 9 : Results table sorted by shoe type and ascending order of speed and GCT.  Click to zoom.

Eyeballing the data, within each subset of shoe tests, the highest LSS value reported by Stryd was for mounting position 2. It would then seem that the position of the device somehow influences LSS. However, if the data is organized in ascending order of GCT, the high values of LSS correspond to the lowest GCT values. In other words, lesser the time spent with feet on ground, higher LSS. It is possible that the combination of shoe type and the GCT is manifested in the difference in LSS values.

At the same time, the corresponding step rates unfortunately change as well. But the good news is that cadence variation is less than 3% so I did an almost good job. If mounting position is considered as the base position, then % variations in SPM, GCT and LSS with a given shoe and speed are as follows :

Fig 10 : % Change in LSS from footpod mounting positions 1 and 2 at a given running speed. Click to zoom.

With tests conducted on shoes A and B, variation in Stryd reported SPM was less than +/- 2.5% while in shoes C and D, SPM was limited to +/- 1%. Variation in reported GCT in mounting position 2 relative to mounting position 1 were less than 5% in all 4 shoes. Variation in corresponding LSS were all within +10%.

With shoes C and D, the change in LSS is the same as in shoes A and B even though cadence was extremely close. Therefore, whether it is the mounting position itself that is influencing GCT and LSS is not clear from the data above. It appears something else is influencing the modeled LSS. 

II.B Effect of Shoe Type

The results table is organized based on speed to help in shoe comparison.

Fig 11 : Results table sorted by speed. Click to zoom.

Eyeballing the data table, there doesn't seem to be any appreciable effect from shoe, except for line items where the footpod was mounted higher. This is my first hypothesis.

Digging further, I was interested to find a couple of things.

First, what is the standard deviation of Stryd reported speed, SPM, GCT and LSS sorted by treadmill belt speed with different mounting positions included in data? Results are tabulated below.

Fig 12 : Standard deviation of pace, SPM, GCT and LSS at 10 and 12kph belt speed between 4 different shoes. Click to zoom.

Second, what is the standard deviation of Stryd reported speed, SPM, GCT and LSS sorted by treadmill belt speed at a fixed mounting position? For this, I had to remove the data for mounting position 2 so that all shoes were compared on mounting position 1 (closer to ground).

Fig 13 : Standard deviation of pace, SPM, GCT and LSS at 10 and 12kph belt speed between 4 different shoes for a given mounting height. Click to zoom.

Comparing both the tables in Figs 10 and 11, the standard deviation in reported LSS gets small at a given mounting position. Infact, it is not entirely true that the X-Y distance at mounting position 1 for all shoes are the same, so it is possible the absolute mounting position has a finite influence here. The right way to conduct this experiment would have been to fix the absolute mounting position for all shoes, but I doubt this is really possible since different shoes have different exterior structural design.

Because the data indicates it is possible that shoes can have an influence on the reported LSS values, it is necessary to express the uncertainty in GCT and LSS.

Fig 14 : 95% confidence intervals calculated for GCT and LSS at 10 and 12 kph belt speeds. Click to zoom.

The influence of shoes could be due to the relative mounting position differences amongst them. I'm not entirely sure at this point. Whatever it might be, the findings here tell me that it is important to establish an uncertainty range when I talk about my own LSS to others : 

At 10kph and average 173spm : 

GCT falls between 256.1 +/- 3 ms (95% confidence).
LSS falls between 10.47 +/- 0.248 kN/m (95% confidence). 
Power falls between 196.7 +/- 1W (95% confidence).

At 12kph and average 177spm : 

GCT falls between 229.7 +/- 2 ms (95% confidence).
LSS falls between 10.46 +/- 0.317 kN/m (95% confidence).
Power falls between 233.5 +/- 1.8W (95% confidence).

The influence of shoe type on GCT, LSS and power is finite (but small) within the context of mounting positions and shoes tested by me. The other finding is that as speed increases, the influence of shoe type and mounting position increases, which widens the 95% confidence interval on GCT, LSS and Power. At both speeds, the effect of my shoes and mounting positions are extremely small on power. 

Since weight, footpod mounting position and shoes seem to have some influence on LSS, I would prefer to express LSS as :

 [ LSS +/- 3% (LSS) kN/m ] ÷ Weight

II.C Interaction Effects on LSS and Power

The above investigations ignore interaction effects of shoe and mounting positions with other parameters like step rate and GCT for example.

The two mounting positions I arbitrarily labelled 1 and 2 have different absolute X-Y values. Even though differences are small, there is a difference. Therefore, I thought it would be best to express the position of the footpod as a mounting height Y and ignore the X position which is assumed to have no bearing in what the footpod measures.

To get an understanding of relative effects of key inputs on LSS, I assumed there are 4 independant factors within the experiment that would affect LSS either by themselves or through interaction effects. I did not consider speed as independant as I figured speed would be a manifestation of step rate and GCT.

Key factors considered :
Shoe type 1-4
Mounting heights, Y
Device reported step rate
Device reported GCT

Interaction Effects Considered

Main Effects 
Shoe alone
Mounting height

Shoe + Mounting pos
Shoe + SPM
Shoe + GCT
Mounting height + SPM
Mounting height + GCT

Shoe + Mounting height + SPM
Shoe + Mounting height + GCT
Shoe + SPM + GCT
Mounting height + SPM + GCT (s)

Shoe + Mounting height + SPM + GCT

A pareto chart (influence chart) of the interaction effects on LSS (kN/m) at a default and widely used significance level of alpha = 0.05 is as follows :

Fig 13 : Pareto chart of standalone + interaction effects on LSS organized by importance. Only GCT, Shoe+GCT and Shoe+GCT+Mounting Height (Y) play a significant role at α = 0.05. Click to zoom.

Fig 14 : Normal plot of standardized effects. Significant factors like GCT, Shoe+GCT and Shoe+GCT+Mounting Height (Y) are negatively correlated to LSS. Click to zoom.

The interaction study might be proof of the earlier assessment that as standalone factors, shoe and footpod mounting height haved played no significant role in the reported LSS in the experiment. However, the combination of shoe type, mounting height and GCT seem to be influencing LSS more significantly than other factors.  

One disadvantage of this study is that I do not include the leg attack angle as a parameter. Infact, from a model I have developed, I know that leg attack angles influences LSS a great deal! It could be that while mounted on different shoes, the landing angle the accelerometer thinks in is different. 

Shoe type and mounting height, in so far as this set of data is concerned, have negligible effect on reported power for the two speeds considered. 

The study also helps prove an earlier post in Part 3 that :

1) GCT is a predominant factor in the LSS model, even more so than step rate (pareto chart Fig 13). 
2) GCT and LSS are negatively correlated. Lower GCT equates to higher LSS. 

If building LSS is considered "free speed"then focusing on injury limiting biomechanics that yield lower GCT appears fruitful. 

I also understand that any exercise intervention to improve my own LSS should bring out positive LSS changes (post intervention) greater than the maximum uncertainty range I have indicated here, which is 3%. This is done so that I can safely consider the changes in LSS are not just from day to day running variations in GCT and higher order shoe and footpod influences.

My interpretations are open to discussion.

In the next post, I'll be taking Stryd to the mountains. Stay tuned!

Sunday, March 26, 2017

NYUAD Ultimate Athletics Track Meet : 800m and 400m

On the 16th of March, I decided to drive over to NYUAD and test myself in the Ultimate Athletics 800m and 400m timed events. Medals were up for grabs for the 1st - 3rd places. 

Long story short, I managed a podium in the men's seniors category in both the 800m and 400m events, spaced about 45 minutes apart. I went limping back home and was happy to shampoo the sand off from my hair. 

To be fair, it was a heck of a windy day (Beufort scale 4) so there wasn't much of a turnout at the event. Which was good because I was wearing an ugly pair of Hypersprint 6 neon track shoes by Asics and was praying that no one would see me. 

But those who turned up were pretty solid runners. In the 800m, I ran with one of the top teenage female running stars in Dubai - Megan Dingle - and was hanging on for dear life. Definitely felt 100 years older. 

In the 400m, I was served some solid African competition but in hindsight, that turned out to be a great testosterone booster because I turned out my fastest 400m. In both events, I managed a PR from the last indoor meet at the same place.

A couple of preliminary photos from the action (check out the massive number of spectators!) :

The results were encouraging for someone who has had virtually no track running since last September 2016. I had great hopes to set up the Stryd powermeter and record data from the run. Unfortunately, the device didn't wake itself up during the second shorter run (the 400m), meaning I only captured data from the first 800m run. Bit of an annoyance.

From the data I did capture for the 800m, things went as follows :

Time : 2:30"
Pace : 5.3537 m/s
Cadence : 102 spm
Estimated VO2 : 55 ml/kg/min
Power to weight ratio : 5.30 W/kg 
Form Power : 64W
Vertical Oscillation : 6 cm
Leg Spring Stiffness : 10 kN/m
Ground Contact Time : 176ms
Run Effectiveness (RE) = 1.01 m/s / W/kg
Energy Cost of Running = 0.98 kJ/kg/km

A couple of things from the data :

1) You notice that even though power picks up in the first 20seconds of the race, the other variables such as GCT, LSS, VO etc take time to activate. This is bizarre and I label it a lag from the Powercenter screenshot. I grow discouraged from the behavior of Stryd on short, fast track runs. 

2) In the final lap, about 200m from the finish was a nice burst of 20kph headwind smack against the face. This was where I slowed down a bit but consciously picked up my legs in order not to fall behind (or fall off!). The delta in wattage from initial part of the race to this point was about 110W.

3) Knowing from a previous RAK half marathon race that my FTP maybe between 200-205W, the intensity factor of the 800m was approximately 330W ave / 200W = 1.65. 

Race results, in old school paper form. 

Highlight of the night was to get introduced to some solid fast twitchers from Zimbabwe (if I recall correctly). Somehow this 63kg slow twitcher was keeping up but it was a hard effort. I'll look forward to more, NYUAD!

Saturday, March 18, 2017

Actionable Intelligence for Running Part 7 : Running Power Characteristics During a Duathlon

In Part 6 of this series, I inspected data from a VO2 lab test data and graphed it's relationship to my corresponding power to weight ratio for 6 different running speeds. What I discovered what the non-linear nature of VO2 (rising and settling dynamics) and the inability of a linear equation to predict instantaneous oxygen cost from a power to weight ratio. 

In general, what was encouraging to see the was the proportional rise in both VO2 and power to weight ratio as speed increased and this credits the Stryd footpod as a steady state "running cost" predictor even though it is an accelerometer / bouce meter, i.e it does not directly measure mechanical power but algorithmically outputs power based on components of velocity extracted from acceleration data.   

I also ended the post by stating that the Stryd does not account for outdoor wind resistance nor for the effect of temperature and humidity, so using an indoor treadmill based correlational equation is likely to underpredict the true cost of running outdoors, especially against winds pushing past Beufort scale #6.

Readers familiar with my previous posts on the GIANT Duathlon Series will know that this race frequently brings some top athletes from the region to the start line. The race format is 3K run, 25K bike, 3K run. This is my third season as a duathlete.

In this post, I'd like to inspect running power during the two running splits of Race#4 held on March 10, 2017. 

Equipment and Personal Data

Running Shoes : Mizuno Wave Ronin 2 (Pre-2010, yes I hold onto old stuff)
Shoes Weight (pair) : 7.5 oz.
Heel to Toe Drop : 9mm
Footpod : Stryd 
Body Weight (unclad) : 63.5 kg
Training (conditioning) : 8-10 hours weekly

Fig 1 : Mizuno Wave Ronin 2

The Course 

The Data

Elsewhere, I will describe in a short race report the feelings and effort going through this race. In a nutshell, it's been one of my best performances to date, having placed 8th in my age category. However, it keeps getting more difficult race by race to move up and 1:17:36 is nothing to boast about.

Fig 2 : GIANT Duathlon 2017 Race 4 results (30-39 Age Category)

The Stryd and Stages powermeter will not automatically pair to the Polar V800 as sport mode changes in a duathlon. Further, the V800 does not have a power feature within running. Therefore, I relied on offline data saved on the footpod for post-processing.

Below is a composite plot showing running power and biomechanical characteristics of the race. Note that cycling has been ignored except to trace an average cycling power for that duration.

Fig 3 : Composite plot showing running power, form power, ground contact time, vertical oscillation and leg spring stiffness from the two running splits of the Giant duathlon Race#4 on March 10, 2017.

Focusing in on the two run splits, the performance variables are tabulated below :

Fig 4 : Performance tabulation of key power and biomechanical variables during the two running splits of GIANT Duathlon Race# 4.
Please note that speed was calculated from the distance vs time relationship from the clocked results of the race and not from the footpod.


All highlighted items - speed, avg. power, power to weight ratio, form power to avg power ratio, cadence and LSS suffered in the second run leg. Considering these facts, the effect of fatigue during short high intensity sprint duathlon is clear to see.

The delta between these variables (those in Run 2 minus those in Run 1) expressed in percentage are as follows :

Fig 5 : Calculated percentage differences in running performance variables in Run 2 compared to Run 1.

1. A 10.5% decrease in run power resulted in a 10.7% decrease in pace in run # 2. Knowing the proportional relationship between VO2 and running power from Part 6, I conclude that the internal running engine ran a bit out of steam. 

Fatigue is multifactorial, not just cardiovascular. There was a short duration decrease in power about 1/4th of the way into run # 2 which in reality coincided with a slight tightening of the right leg muscle where I had to throttle down power to 180W for a few seconds.

However, there were no cramps and no stopping to loosen the legs. The salt instake for the day was good considering the ingestion of both a GU gel worth of 180mg of sodium and an aerodynamic water bottle filled with water + 380mg serving of sodium in 50g of electrolyte mix. 

Some others who have seen my data comment that this is an example of predominantly "metabolic fatigue".

2. Form power, i.e the cost of "perpendicular bouncing" as a percentage of total external running power, was 8% higher in run # 2 than run # 1 (external running power does not account for the swing in upper and lower body limbs).

3. Ground contact time (GCT) was 9.76% greater in run # 2.

4. Leg spring stiffness (LSS), extensively discussed in Part 1, Part 2 and Part 3 of this series, was 2.9% higher in run # 2. Cadence decreased and GCT increased between the two runs, but as previously discovered by experiment, the effect of change of GCT on LSS is greater than is the effect of cadence on LSS (Part 2)

5. Run effectiveness (m/s over W/kg), a surrogate for running economy, decreased by a tiny fraction of a % in run # 2 compared to run # 1. 

Within each of the splits, the behavior of the RE trend (fraction of instantaneous RE over average RE) was a mildy increasing one for run # 1 and flat for run # 2. Please note that the calculated value of RE is sensitive to the data and abnormal spikes in speed or drop in power will result in higher than usual RE values.

My conclusions from the above data study for sprint duathlon are as follows :

In an ideal scenario :

a) Leg turnover would be similar in the two run legs. Longer swing times during the start of the second run affect GCT which consequently has bigger impact upon LSS than the lowering of step rate alone.

b) Vertical "bouncing" as a fraction of total power would be reduced in the second run so that more of the horizontal component makes up the total power. However, I  must confess my understanding on components of power is not on par with the coaching community. I believe one has to bounce to an extent to generate the potential energy needed to activate the storage potential of the leg spring, so an optimum must be struck between too little bouncing and too much bouncing. Too much bouncing is understandable since energy is being used to elevate and lower the center of mass and perhaps some of that could be acively focused instead on moving forward. Form power is something to continue experimenting with.

c) Nutrition points would be more optimally placed during the race to allow proper absorption by body before the demands of the second run. Ideally, this would allow a more even power to weight ratio between both the run splits. Race strategy is knowing exactly at what points to ingest and that can have a pronounced effect on performance during the second run split.

d) Based on 400m and 800m track results, I have the potential for RE > 1.00. What that will mean for performance in the second run split is something to be tried out. Racing is always learning by trial and error. In these short high intensity events, you always have to push your body past the limit to place well but you also have to throttle things down a notch to first finish !