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Showing posts from November, 2011

Low-Key scoring algorithm: addition of variance normalization

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As always happens in fall, the Low-Key Hillclimbs have taken up a large chunk of my time, leaving less time for blog posts. But it was worth it: the series was an unqualified success, with every climb coming off well, the last few finding valuable seams in the weather. At Hamilton riders experienced moderate rain on the descent, and for some towards the end of the climb, but it was warm enough that the long descent was still tolerable in the wet. One aspect of the series worthy of revision, however, is the scoring system. Never before were artifacts in the median-time-normalized scoring more obvious. So for 2012, I am finally overcoming inertia and changing from the median-based scoring we've essentially used since 2006. I've described in preceding posts a scheme to calculate a reference "effective" time for each climb. With this scheme, instead of taking a median each week, we take a geometric mean where effective times for riders (adjusted for male, female, h

week-to-week stability of proposed 2012 Low-Key scoring formula

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In two previous posts, I described an attempt to revise the scoring code for the Low-Key Hillclimbs. The scoring has placed a priority on simplicity. At first, we normalized times to the fastest man and woman each week. But then everyone's score was exceptionally sensitive to the fastest rider. Then I switched to using the median time for normalization, first separately for men and women, then using combing them with an empirically determined conversion factor for women. But while median is less sensitive to any single individual showing up, nevertheless the most challenging climbs tend to attract fewer beginner riders, deflating the scores for these weeks. So the alternative approach is to iteratively rate each climb using a reference time based on the rating of riders who show up, and assign each rider a rating based on the reference times (and their results) of the climbs they do. A concern about this approach is that if I use all available information equally, I re-rate

testing 2012 Low-Key Hillclimbs scoring code

I seem to have debugged the new Low-Key Hillclimbs scoring algorithm, so tested it on 2011 data for the completed first six weeks. Recall the method is to calculate a rider's rating (not used for overall rankings) based on the natural logarithm of the ratio of his time each week to that climb's reference time. Meanwhile the climb's reference time is calculated as the average the natural logs of the times of the riders in the climb, subtracting their ratings. These "averages" are weighted by heuristic statistical weights which assign more importance to riders who did more climbs, and to a lesser extent to climbs with more riders. Each of these factors depends on the others, so the solution is done self-consistently until it converges, in this case until the sum of the squares of the reference times changes by less than 10 -6 seconds 2 . This took 8 iterations in my test. To avoid contaminating the results I check for annotations that a rider has experienc

proposed 2012 Low-Key Hillclimbs scoring algorithm description

The whole key to comparing scores from week-to-week is to come up with a set of reference times for each week. Then the rider's score is 100 × this reference time / the rider's time, where times have first been adjusted if the rider is a woman or a hybrid-electric rider. Presently this reference time is the time of the median rider finishing the climb that week. But if riders who would normally finish in more than the median time don't show up one week, for example Mix Canyon Road, everyone there gets a lower than normal score. That's not fair. So instead we can do an iterative calculation. Iterative calculations are nice because you can simplify a complicated problem by converting it into a series of simpler problem. The solution of each depends on the solution of every other. But if you solve them in series, then solve them again, then again, eventually you approach the self-consistent solution which you would have gotten with a single solution of the full, un

San Francisco: City of Passive-Aggressive Losers

The San Francisco mayor's election was yesterday, and it looks like Ed Lee won it with around 30% of eligible voters voting. Quoting the San Francisco Examiner, referencing critics: "...the career bureaucrat would be nothing more than a shill for powerful City Hall insiders. Lee also was dogged by accusations of voter manipulation by an independent expenditure committee that supported the mayor and other backers laundering campaign donations, which prompted a District Attorney’s Office investigation..." He attracted a huge number of donations, driving up the amount the city needed to pay in public financing. His donations were largely from out-of-city donors, many laundered through low-income workers to circumvent the $500 donation limit. Then there were the nominally unaffiliated supporters, for example those who produced and distributed free copies of the book of his life story. Meanwhile, he violated the law by refusing the disclose details of public contacts wi

Natural Selection Voting Theory

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In nature, if you can't do what it takes to survive, you die, your genes are eliminated from the pool, and someone else takes your place. Maybe what takes your place is better, maybe not. But if not, it will also die, be eliminated, until eventually something able to do what it takes comes along and so, by this process, things generally improve over time. This is my theory of voting. Rule #1: if the incumbent isn't doing a good job, vote them out. So often in elections I hear about the "lesser of two evils". "I don't like the incumbent XXX, but he's better than YYY." Sorry: the rule of natural selection says I vote XXX out of office anyway. Maybe YYY is even worse. But then I vote YYY out at the first opportunity. Eventually corrupt and unqualified candidates will stop running. Eventually you get someone good in office. But if you vote "lesser of two evils", things will never change. You'll always have candidates who suck

New scoring scheme for Low-Key 2012?

Low-Key scoring has gone through various phases. In the 1990's, we scored based on fastest rider. The fastest man and the fastest women each week would score 100. Those slower would score based on the percentage of the fastest rider's score. This was super-simple, but when an exceptionally fast rider would show up, everyone else would score lower than normal. Additionally, this was frustrating for the fastest rider (typically Tracy Colwell among the men), since no matter how hard he or she pushed himself, the result would be the same 100 points. So with Low-Key 2.0 in 2006, we switched to using the median rider (again treatng men and women separately). The median is much less sensitive to whether a particular individual shows up or not, so scores were now more stable. However, there was still an issue with women, and most especially with our hybrid-electric division, since smaller turnouts in these again made the score sensitive to who showed up. So in 2010 I updated t

Riding the Diabolical Duo at Mount Vaca

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approaching the Low-Key finish (Cara Coburn photo) Yesterday I rode the Diabolical Duo at Mount Vaca. First: Mix Canyon Road. Coordinator Barry Burr did an excellent job organizing this one, definitely the "road trip" ride for many in the 2011 Low-Key Hillclimb schedule. For my car pool it wasn't a big deal: one hour from San Francisco, even stopping for gas. Rides like Alba Road, Bonny Doon, Henry Coe, Jamison Creek Road, and Hicks Road we've done in the past are all substantially further, with plenty more of comparable distance. But most of our riders live closer to San Jose than to San Francisco, and for them the trip was further. But even from San Jose this trip was worth it. A big part of it was our Strava event: The Diabolical Duo . The Low-Key Hillclimb covered just the first part of this: to complete the Duo, riders needed to also climb nearby Gates Canyon Road. Inspiration for the Duo event came from The Toughest Ascent Blog . I won't even t