"Freeze Points" metric

[jrenaut]

Out of curiosity, I put temperature on the main screen of my Garmin 510 (about a year old, firmware updated in the last month or so). Very unscientific test, but the Garmin didn’t do too well. It was about 55 when I turned it on, which may be perfectly reasonable in the laundry room where it was sitting for a few hours. Over the course of my 15 minute ride, it dropped steadily until it maybe was close to the actual temperature. It was in my pocket for 10-15 minutes while I retrieved the kids, and it was back up in the 50s when I turned it back on. For the entirety of the ride home, a little more than 15 minutes, it was dropping.

This would suggest that if your ride is less than about 25 minutes, the temperature data is completely meaningless. But as I said, not a terribly scientific test.

[hozn]

Yeah, the Garmins take a looong time to adjust. Some people put them in the freezer to help accelerate the adjustment. Mine is steady since it sits in the shed on the bike all night. But both my Garmin 510 and 1000 do read low in winter time (unsure in summer; I don’t look at the temp). Both are consistently 5-7º colder than weather.com temps. E.g. it was 34º when I left the office and my Garmin said 28º.

[DismalScientist]

@hozn 134757 wrote:

Some people put them in the freezer to help accelerate the adjustment.

Thermodynamic doping? Is there no limit to what level the BAFS Freds won’t sink?:rolleyes:

[Tim Kelley]

@DismalScientist 134763 wrote:

Thermodynamic doping? Is there no limit to what level the BAFS Freds won’t sink?:rolleyes:

Strava KOMs are easier to get in the summer due to less dense air…

[Steve O]

@Tim Kelley 134819 wrote:

Strava KOMs are easier to get in the summer due to less dense air…

And the humidity (humid air is less dense than dry air)

And since air gets thinner at higher altitudes, you tend to speed up towards the top of the hill. Thus longer, higher hills have faster KOMs.

[Tim Kelley]

http://velonews.competitor.com/2013/03/bikes-and-tech/technical-faq/technical-faq-why-is-riding-in-the-cold-so-hard_277626

[vern]

@Steve O 134825 wrote:

And the humidity (humid air is less dense than dry air)

And since air gets thinner at higher altitudes, you tend to speed up towards the top of the hill. Thus longer, higher hills have faster KOMs.

Not thinner at higher altitudes, but less dense. The molecules are not packed as tightly together.

[Anonymous]

For the record, “sometimes the wind is a headwind and sometimes it’s a tailwind so it all evens out” is true in the same sense that “sometimes you’re going up the hills and sometimes you’re going down the hills, so it all evens out” is true.:rolleyes:

//just returned from a mostly SE out and mostly NW back “recreational ride”//

[bentbike33]

@Amalitza 134854 wrote:

just returned from a mostly SE out and mostly NW back “recreational ride”

You did that backwards, for today anyway.

[Anonymous]

oh, it’s backwards pretty much every day

[Steve O]

So, my understanding is that a metric like this is intended to reward those who ride in the gnarliest weather more than those who ride in the clementest weather (I made that word up).

The problem is defining “gnarliest.” Is it the cold? Wind? Precip? Ice? Perfect storm combos of these?

One way to get at whether the weather is bad is to use ourselves as proxy. Lookie here:
[ATTACH=CONFIG]10952[/ATTACH]

There isn’t much correlation between temperature and number of daily riders. The day with the fewest riders was Jan 23, which I think most of us would agree was the gnarliest riding day this year. But not the coldest.

There’s a pretty strong correlation between bad weather and number of riders, so we could devise a formula or bonus system or something based on that rather than trying to parse and define the weather itself.

If we think FS participants are not a good proxy (I would argue that, as a group, we are a decent statistical sample for this purpose), then we can use the Arlington counter data. Staff is pretty convinced that the rider numbers fluctuate in strong correlation to weather conditions: gnarlier weather; fewer riders.

Then we don’t have to debate whether a strong headwind is gnarlier than a polar vortex, or if sleet is worse than snow.

[Vicegrip]

Miles ridden per day rather than riders might be a good metric to look at if doable. On a snow day many people can sleaze a wobbly 1.1. Grade on a curve? On days when the miles totals goes down measurably increase the point value.

[jrenaut]

Weighting for conditions sounds cool, though I’m not sure it would really make a difference. Making up numbers for an example – let’s say on an average day all participants ride 1000 miles. On a day when we all ride 1500 miles, you score .75 points per mile. On a day when we all ride 500 miles, you score 2 points per mile. I suspect our high mileage riders are out there no matter what.

Or maybe we look at your average day vs the overall average? Everyone rides 1000 miles a day avg, you ride 10 average. Today everyone rode 500 but you still hit your 10, so you have a bonus of 5? That might be interesting. Kind of a “the weather does not affect me” chart.

[hozn]

I think we could come up with a reasonably good “gnarly” score that gives weights to different factors. There is probably a relatively easy way to factor in headwind too, though if we do this by day then wind cardinality (?) does seem to zero itself out and simply wind speed becomes the weight.

The thing we probably can’t factor in easily is the presence of snow on the ground. If anyone knows a good data source for that …? Maybe we could add a relative factor that just assumes that days with fewer riders are by definition gnarlier. Maybe a bit of a chicken/egg problem with calculations

Edit: I realize now (after reading) that my second suggestion is exactly what vicegrip proposed

[vvill]

@Steve O 134925 wrote:

The problem is defining “gnarliest.”

The idea of this first implementation of Freeze Points was to award points based on temperature and a consistent amount of mileage rather than purely miles and a daily bonus (which I feel is perfectly fine for the National Bike Challenge but could be made into something more winter specific). It’s a long contest, and there are a lot of cold riding days, so I wanted to capture that sort of “spirit”. You can of course ride 1.0 miles on the days where you wouldn’t normally ride at all and there’s nothing wrong with that, but I think if you really embrace winter riding, you’ll end up only sleazing on days where you’re too tired or busy, rather than when the conditions are less favourable (although I don’t necessarily condone crazy-riding-on-ice-with-23s… hi Subby!)

There would be no curves based on mileage/temperature if it was just supposed to gnarliest, because let’s face it, riding 250+ miles in freezing temperatures on Jan 1 would just blow a gnarliest score out of the water (…hi Eric!) Another element was time – I don’t think everyone can make the time to ride mega miles, but most people who are regular commuters, utility riders or even just regular recreational riders will easily reach the “sweet spot” of mileage. The current system makes it impossible for time-crunched folks to ever match the really high mileage riders, and I think something like Freeze Points gives everyone a better chance to achieve higher scores.

I certainly don’t think the Freeze Points metric is perfect (having seen the full output, I think it’s weighted a little too much based on temperature) – but I’m happy it’s generated so much discussion and to that end, I enjoy reading this thread. I like that people are thinking of other metrics (and I agree with hozn that we could surely devise a “gnarliest” one based on time of day, precip, wind, temperature, mileage, average speed, VAM, and whatever else, if that’s what we want – is it?). I, like many here I imagine, really enjoyed seeing a lot of the Individual/Team leaderboards and data exploration charts when they came up but after a while I couldn’t help thinking we could derive even more.

The idea of a self-scaling metric is interesting, although yeah a day’s “clemency” rating can’t be finalized until everyone’s submitted their ride data and obviously it will be skewed with weekends, holidays, etc. and travelers who are riding outside of the DC area. The nice thing about weather is it’s out of our control, so it’s a more absolute measure, but OTOH a self-scaling metric does avoid having to weigh up the relative influence of each data input – I would agree with Vicegrip that miles/day would be more meaningful than riders/day, although maybe I’d cap the miles that each rider could contribute at about 40-50.

I agree that the temperature vs. riders graph is mostly interesting because it shows how little influence temperature has on this crazy group of riders we have! I think frozen precipitation on roads/trails overnight would be the biggest factor influencing the number of riders the next day.

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