On Bicycles, and.... what else is there?

Last time I described the procedure for constructing a look-up table for time conversion for road segments of a particular road grade. The road grade is the result of taking the route profile for a course, extracted for example from barometric altimetry + GPS data (Garmin Edge 500, Garmin Edge 800), and convolving it with a smoothing function to reduce grade changes which occur over substantially less distance than 25 meters. The approach is based on an equation fit to data from Minetti, et al, published in the Journal of Applied Physiology in 2002. Here's the model, where metabolic cost is measured in energy per unit distance. Although metabolic heat is more conventionally reported in calories, I prefer joules in order to facilitate conversion between power, work, and heat. I assumed running for grades up to 12%, and walking for grades larger than 12%, which is consistent with my typical practice (perhaps not Gary "the" Gellin, who could run up a sheer vertical wal

To convert times for running to an "effective cycling" time the approach I want to take is to convert the running time to a cycling time of similar metabolic cost. Minetti measured metabolic cost for running on a treadmill tilted at different angles. This completely neglects wind resistance, yielding the running analog of rolling resistance, overcoming gravity, and drivetrain efficiency: For cycling I have the standard equations, which I won't fully enumerate here. But in still air there's a wind resistance component proportional to area, air density, and speed cubed, a gravity component and a rolling resistance component each proportional to weight (gravity and mass), speed, and the sum of the road grade and the coefficient of rolling. Additionally there is a drivetrain loss component which is typically if inaccurately modeled as proportional to total power (in reality the fractional loss decreases at higher power). There's several levels of approximation

In the Low-Key Hillclimbs this year, cyclists were scored using a factor to adjust for how spread out they were in time. On steeper climbs, riders tend to be proportionately more spread out, but on more gradual climbs, they tend to be more closely bunched together. Rather than apply a correction explicitly based on any particular characteristic of the road, a "slope factor" was derived from the rider data to yield a similar statistical distribution for any pair of weeks for riders doing both weeks. This worked well for cyclists, but we also allow runners in the Low-Key Hillclimbs. We don't want it to turn into a running race, with teams recruiting runners to boost their scores, so instead of scoring runners on their own scale we score runners the same as their time would have been riding. Since only on extreme grades is running faster than cycling, this results in runners scoring relatively less. It's fun when running to do as well as one can against the riders,

Dirt climbs are fun, and while I've not ridden up this one (I've hiked it, and I've ridden down part of it), I decided to make a profile for it since it was recommended to me by a friend. Montara Mountain is often overlooked when cyclists think of large climbs in the Bay area. The reason is that unlike many other large hills, there's no paved roads to the top. However, this was not always the case, and the "Planet of the Apes" route follows what was the inland road ("San Pedro Mountain Road") between Pacifica to the north and Half Moon Bay to the south. This road was replaced by the Devil's Slide portion of Highway 1, and nature quickly asserted control. The "road" is now in many places little more than single-track, it's paved past evident only in broken patches of crumbling asphalt. This route begins at Grant Ranch, the southern extreme of Planet of the Apes. It then climbs steeply to a "Y", a left the route to Pa

This year I went through a bit of a slump in posting to this blog: I was involved in a coding project which in the end turned out to be essentially unsuccessful since I had issues with dealing with the Strava API I was unable to resolve, and other priorities took over. In any case, I still had things I wanted to say here, so went back to posting after the slump. Here's my accumulated posts each year, by month, since I started way back in 2008: I'm never sure for how long I'll go on with this, but I find it personally valuable to go back and see what my perceptions and feelings were at various points in the past. For example, 2008 was when I first started running after a long, long delay, and I like going back and reading race reports I posted then. Or I refer to equations I derived relating factors such as mass, aerodynamics, and rolling resistance to cycling speed. I'll stick to it to some degree as long as I find it valuable. Facebook and Twitter, which have

Typically climbs are up a single road (like Old La Honda Road), or up a relatively unambiguous path to a summit (like Diablo North Gate Road to Summit Road). But in the San Francisco Bay area, like many other places, there are often ridge roads into which roads up the hillside intersect. As Baird Webel pointed out to me when I was at Stanford, these side roads will typically seek out relatively low points in the ridge, so by turning when hitting the ridge road, the climb can be extended. He wasn't claiming this as an opportunity, but rather as a fate: don't take the ridge road for granted as the top of the climb. Another Baird quite, "We need to descend into that ?!?!", when looking down into the smog-shrouded Silicon Valley was similarly memorable, but that's a digression. Anyway, I typically ignore these sorts of options as "climbs". From the top of Old La Honda Road, for example,the climb can be extended without much traffic interference by turn

Last year the Tour of California, for the first time, rode the east side of Patterson Pass, the last climb in the stage into Livermore. The result was domination of the Strava leader board as the group blasted over the climb, down the other side, into the city for a few finishing circuits, then to the sprint which was dominated by Peter Sagan. The Strava segment: Here's my rendering of the profile data: It begins gradually, traversing up the windmill-capped hill until the "oh-my-God" climb which leads to the pass. Although the view of this section is dramatic, the profile shows the grade is only 12%, which can cause distress but is not in the league of the steepest climbs in the Bay Area. Using my climb rating index , it rates 74% as high as Old La Honda Road. I've ridden this climb in two major events: the Devil Mountain Double and the Patterson Pass Road Race. In the former, it comes between Mount Diablo and climbing the west side of Mount Hamilton. It

The 2012 Low-Key Hillclimbs are done! And it was an extremely successful year. This was the year of the Junior, as the men's overall was taken by Adrien Costa, who capped off his successful year with the win in the Mount Hamilton Thanksgiving climb, beating Irish Hillclimb champion Ryan Sherlock in a tactical race. Adrien set the Strava KOM (beating a false match to Jacob Berkman's Mt Hamilton Road Race data; the road race doesn't do the time-sucking summit road), and broke David_Wyandt's long-standing record from 1998. Ryan, finishing second, also beat David's time, not giving him three of the top five times. To be fair, it's off-season for Ryan, but then again it's also off-season for Adrien. After third place Eric Wohlberg, fourth place also went to a junior, Andrew Biscardi. This is the best year yet for Low-Key juniors, the previous best arguably Menso de Jung's excellent season in 2006. In the women's rankings, it was Lisa Penzel who

A big motivation for Kennedy was that it would mix things up a bit: not only the experience, but also the scoring. While the results in typical climbs can get almost frustratingly predictable, on the dirt skill, confidence, and balance can play a substantial role. I like to compare the scores for riders who did climbs in any given pair of weeks, to see how their scores different (RMS average difference). Here's the result: climbs: w1: Montebello w2: Quimby Road (Murillo start) w3: Morgan Territory Road (S) w4: Hwy 9 from Boulder Creek w5: Hwy 84 - West Alpine w6: Soda Springs w7: Kennedy Trail ----------------------------------- rms score delta (riders doing each) ----------------------------------- w1 w2 w3 w4 w5 w6 w7 w1 . 4.73 2.99 5.29 6.04 3.40 6.88 w1 w2 4.73 . 4.96 7.91 5.77 3.33 6.48 w2 w3 2.99 4.96 . 4.07 6.41 3.09 6.72 w3 w4 5.29 7.91 4.07 . 5.66 5.59 7.46 w4 w5 6.04 5.77 6.41 5.66

Here's a summary of the method I used to extract times from GPS data on the Kennedy Fire Trail run. First I defined a course. This was done with a series of "lines": line segments the riders needed to cross in a specified direction. This is in contrast to Strava's approach of defining a course via points. For matching, Strava treats the start and end point as special, requiring the activity to pass them with close proximity. Intermediate points need to be matched in a much cruder fashion, and only then a certain fraction of the points. My approach, however, required all intermediate "lines" to be crossed. I defined these points using latitude-longitude coordinate pairs, which are easily extracted from Google Maps (select point, right-click, "What's Here?"). Split times were specified as pairs of lines, where the start line is 0, and the finish line is the last line. For example, my course had split times from 0 to 1 (an initial 40 mete

A followup to my run report... a look at VAM. VAM on running is a very different beast than on cycling. With cycling, once overcoming gravity becomes the dominanant task, wind resistance and rolling resis\ tance playing substantially minority roles, a relatively constant VAM can be maintained as long as there's an available gear to support a cadence in the target ra\ nge, at least until holding the front wheel on the road becomes a challenge. With running, the analog of rolling resistance is much larger, so there is no point \ where it can be trivially ignored, and by that point running becomes impractical and movement is more by walking or even scrambling. So VAM without the context o\ f road grade is meaningless. I repeat the analysys I did for Soda Springs Road, which is to plot VAM and road grade, each smoothed by a 30-second Gaussian convolution, on the same plot. H\ ere's the result: What I see is, on these axes, VAM and road grade track nicely until towards the

Yesterday was the first-ever "self-ride" in Low-Key Hillclimb history. Since GPS has become sufficiently ubiquitous, it was a good opportunity to use a course which would be otherwise inaccessible to us. In this case it was Kennedy Fire Road. Profile For this purpose I wrote some Perl code which downloaded Strava activities using their rest API, the simple version 1 streams method. I'll provide more details on this next post. But in summary there were a series of "lines" which the data needed to cross in the correct direction in sequence. I had a start line, a first checkpoint 40 meters later to check for riders accidentally triggering the start before they'd actually started riding, a next checkpoint at a plateau a bit more then half-way, a third checkpoint at the dip before the final steep climb, then the finish. I had split times associated with each of these checkpoints. I went to the trail head with Lisa Penzel, who gave me a ride there from t

Last post I described my perception of the Low-Key Hillclimb up Soda Springs Road. I did the climb without obvious metrology: I relied only upon perceived exertion and the timer on my Garmin. It's interesting to see how data match perception. My PowerTap wheel it far too heavy to haul up the hill, but a good surrogate for post-ride power analysis is VAM (vertical ascent rate). This works especially well with a hill like Soda Springs road which sustains a relatively constant steep grade the entire way. I took my Garmin Edge 500 reported altitude as a function of time and differentiated it. That creates a highly noisy signal, so I convolved that with a Gaussian of sigma 30 seconds. I considered points only from the point I started riding, so points at the beginning and end wouldn't get smoothed with points from before the start or after the finish, which would cause sag in my VAM rate early and late in the ride. I then did a similar thing with road grade, which I extracte

My original plan was to run the Soda Springs Road Low-Key Hillclimb : a steady 5.3 mile slog at 8.5%. But Cara suggested I should do a long, flat run in preparation for the California International Marathon, which I'm running, in three weeks. Specificity suggests running up steep hills isn't the best preparation for that. So I registered instead as a rider. Never before had I done a Low-Key with such poor preparation. I'd been focusing on running since August, and as my running recovery and distance grew, my riding numbers diminished in proportion until even my short rides to and from the train I take to work were gone: replaced with runs or walks. I knew from past years the effect running can have on my riding. I'd do a Low-Key Hillclimb on Saturday, run Sunday and Tuesday, then ride Old La Honda with the noon ride on Wednesday before easy rides Thursday and Friday in preparation for the next Low-Key. On my Wednesday climb I'd be dreadfully uncompetitive, u

In the Low-Key Hillclimbs this year I've introduced a parameter I call the "slope factor". After calculating scores as 100 multipled by the ratio of a reference time to a rider's time, I raise the (score/100) to a power equal to the "slope-factor" (it's a slope on a log-log plot). If the scores are more compressed than average (given the riders who turned out for that week), the slope factor will be larger than one, and the adjustment will spread the scores out more. If they're more spread out than average, the slope factor will be less than one, and the scores will be compressed. This was done based on the observation, consistent with physics, that steeper climbs tend to result in a larger relative separation in times, while more gradual climbs tend to result in a more compressed separation. Here's a plot of slope factors calculated for the scores so far this year, plotted versus the average grade of each road. Each point is marked by the

I wanted to compare how passing on last Sunday's Low-Key Hillclimb compared with expectations. So this time I made a few changes to my simulation relative to the week before: I used actual rider start times, including missing riders, rather than assuming every rider showed up and started at even intervals. I used actual rider ranking scores, using maximum score in a ride this year, or maximum score in the most recent ranking year available for the rider with a 1%/year depreciation rate. For riders who had not before done a Low-Key, I assumed a modified normal distribution with a 14.4% sigma (I modify it to an asymmetric probability distribution which can only asymptotically approach zero). I changed the rider week-to-week variation to 3.76%, which I extracted from scoring data this year weeks 1-5 by comparing variation in rider scores for the same rider on different weeks. This number is the rms variation from one week to another divided by sqrt(2), since I assume week-to-wee

I had some questions about the Low-Key scores from a rider who didn't understand why he scored better in one week than in another. I can't answer that question, but I decided to look again to see if I could see any bias in the results. For example, did strong riders tend to score better in one week versus the other, which would unfairly advantage strong riders who had done well in the higher-scoring than the lower-scoring week? The way I like to check this is by plotting scores one week versus the other. This isn't rigorous statistical analysis, more what Flannery and Press call "chi by eye": check to see if trends are evident to visual inspection. Week 2 was a mass start up the steepest climb so far: Quimby Road. Weeks 4 and 5 were individual starts on roads which included both relatively flat portions and significant, but not particularly steep climbs. First I compare Quimby (week 2) versus Boulder Creek to Saratoga Gap (week 4). These were highly dissim

Last chance to take positions on the election... First, I'll address the Proposition 30-38 issue. In my discussion of the California Propositions, I essentially treated these separately. Proposition 38 is a classic "split the vote" play. It's a very effective tactic: put a second proposition on the ballot mutually exclusive with a first. Make it just different enough from the first that some will prefer it, yet some will prefer the first. Voters who wish to express a preference for the first will vote against the second, while those who wish to express a preference for the second will vote against the first. As a result, even if only a small minority prefer "none of the above", it can be very easy for both propostions to fail to receive a voter majority. Both fail, which was the point all along of introducing the second measure. In this case, I like each enough I vote for both. I encourage others to do the same. Onto some candates. US House of R

The US Half Marathon was my second "training race" for the Sacramento International Marathon four weeks from today, where my goal is to qualify for Boston. My idea was to do a 10 km race in October, a half-marathon (21.1 km) in November, then the full marathon in December (42.2 km). This is a nice factor-of-two progression in race distances. At first my goal was to use these races to practice my marathon pace, but this plan didn't last long. As each race approached, I was drawn to the attraction of distance-cspecific time goals: 40 minutes for the 10 km, then 90 minutes for the half-marathon. Unfortunately my 10 km race yielded a disappointing 41:46, well off my target. Going into the half I was forced to admit my 1:30 goal was probably unrealistic. While I like to believe that my running fitness is better than it was last August when I ran 1:30:56 in the San Francisco Giants Half Marathon (as "Fred"). But that course, while sharing many of the elements

Low-Key 2012 week 4 , the time trial from Boulder Creek to Saratoga Gap, was an amazing success. For all the concerns expressed about passing, concerns I did my best to dispel, it simply wasn't a problem. Many of us were left scratching our heads over why people were so worried. I posted a simulation of the passing simulation here. This was based on certain simplifying assumptions: Riders left at uniform one-minute intervals. This turned out to not be the case, as starters allowed late-comers to start on 30-second intervals. Of course, these late-starters left a 1:30 gap ahead of them, partially mitigating the effect of the now 30-second gap behind them. Additionally, many riders failed to show up, leaving gaps in the start sequence. Riders were ranked based on a previous result. This assumption neglected that for some riders it was their first event. For these riders, placement was somewhat arbitrary. Secondly, volunteers who rode were started at times which allowed the