{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
Peter Norvig, Oct 2017
Last update: May 2024
Data update: Oct 2024
\n", "\n", "# Bicycling Statistics\n", "\n", "Bicycling is a great way to get some exercise, enjoy the outside, and go places. This notebook tracks [my cycling performance](https://www.strava.com/athletes/575579) against various goals:\n", "- **Distance**: I do about 6,000 miles a year.\n", "- **Climbing**: In 2022, I climbed to *space* (100 km of total elevation gain).\n", "- **Explorer Tiles**: In 2022, I started tracking the 1-mile-square [explorer tiles](https://rideeverytile.com/) I have visited.\n", "- **Wandering**: In 2020, I started using [Wandrer.earth](https://wandrer.earth/athletes/3534/) to track what new roads I have ridden.\n", "- **Eddington Number**: I've done 69 miles or more on 69 different days. So 69 is my Eddington Number.\n", "- **Speed**: I'm not going particularly fast, but I am interested in understanding how my speed varies with the steepness of the hill.\n", "\n", "This notebook is mostly for my own benefit, but if you're a cyclist you're welcome to adapt it to your own data, and if you're a data scientist, you might find it an interesting example of exploratory data analysis. The companion notebook [**BikeCode.ipynb**](BikeCode.ipynb) has the implementation details." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%run BikeCode.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Yearly Totals\n", "\n", "Here are my overall stats for each year since I started keeping track in mid-2014. I have done 6,000 miles per year since 2016, except for 2020 when an injury kept me sidelined for two months (also, Covid). The columns keep track of the total **hours** on the bike, distance traveled in **miles**, and total **feet** climbed. Then there are some columns that are dervided from these: **mph** is **miles / hour**; **vam** is vertical meters ascended per hour (or **feet × 0.3048 / hours**); **fpmi** is **feet / miles**; **pct** is the grade in percent (or **feet × 100 / miles / 5280**), and finally **kms** and **meters** are the metric equivalents of **miles** and **feet**.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearhoursmilesfeetmphvamfpmipctkmsmeters
2023541.68631624310011.66137.038.00.7310162.4474097.0
2022532.93602836232311.31207.060.01.149699.05110436.0
2021490.53606419663412.36122.032.00.619756.9859934.0
2020438.8853419477712.1766.018.00.348593.6728888.0
2019476.32601614979712.6396.025.00.479679.7445658.0
2018475.93610115864212.82102.026.00.499816.5148354.0
2017567.33735620209612.97109.027.00.5211835.8061599.0
2016486.38633920145313.03126.032.00.6010199.4561403.0
2015419.95545220985912.98152.038.00.738772.2763965.0
2014191.03246911848112.92189.048.00.913972.6236113.0
\n", "
" ], "text/plain": [ " year hours miles feet mph vam fpmi pct kms meters\n", " 2023 541.68 6316 243100 11.66 137.0 38.0 0.73 10162.44 74097.0\n", " 2022 532.93 6028 362323 11.31 207.0 60.0 1.14 9699.05 110436.0\n", " 2021 490.53 6064 196634 12.36 122.0 32.0 0.61 9756.98 59934.0\n", " 2020 438.88 5341 94777 12.17 66.0 18.0 0.34 8593.67 28888.0\n", " 2019 476.32 6016 149797 12.63 96.0 25.0 0.47 9679.74 45658.0\n", " 2018 475.93 6101 158642 12.82 102.0 26.0 0.49 9816.51 48354.0\n", " 2017 567.33 7356 202096 12.97 109.0 27.0 0.52 11835.80 61599.0\n", " 2016 486.38 6339 201453 13.03 126.0 32.0 0.60 10199.45 61403.0\n", " 2015 419.95 5452 209859 12.98 152.0 38.0 0.73 8772.27 63965.0\n", " 2014 191.03 2469 118481 12.92 189.0 48.0 0.91 3972.62 36113.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here's the same data on a per day basis, assuming I ride 6 days a week, 50 weeks a year:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearhoursmilesfeetmphvamfpmipctkmsmeters
20231.821.1810.311.66137.038.00.7333.9247.0
20221.820.11207.711.31207.060.01.1432.3368.1
20211.620.2655.412.36122.032.00.6132.5199.8
20201.517.8315.912.1766.018.00.3428.696.3
20191.620.1499.312.6396.025.00.4732.3152.2
20181.620.3528.812.82102.026.00.4932.7161.2
20171.924.5673.712.97109.027.00.5239.5205.3
20161.621.1671.513.03126.032.00.6034.0204.7
20151.418.2699.512.98152.038.00.7329.2213.2
20140.68.2394.912.92189.048.00.9113.2120.4
\n", "
" ], "text/plain": [ " year hours miles feet mph vam fpmi pct kms meters\n", " 2023 1.8 21.1 810.3 11.66 137.0 38.0 0.73 33.9 247.0\n", " 2022 1.8 20.1 1207.7 11.31 207.0 60.0 1.14 32.3 368.1\n", " 2021 1.6 20.2 655.4 12.36 122.0 32.0 0.61 32.5 199.8\n", " 2020 1.5 17.8 315.9 12.17 66.0 18.0 0.34 28.6 96.3\n", " 2019 1.6 20.1 499.3 12.63 96.0 25.0 0.47 32.3 152.2\n", " 2018 1.6 20.3 528.8 12.82 102.0 26.0 0.49 32.7 161.2\n", " 2017 1.9 24.5 673.7 12.97 109.0 27.0 0.52 39.5 205.3\n", " 2016 1.6 21.1 671.5 13.03 126.0 32.0 0.60 34.0 204.7\n", " 2015 1.4 18.2 699.5 12.98 152.0 38.0 0.73 29.2 213.2\n", " 2014 0.6 8.2 394.9 12.92 189.0 48.0 0.91 13.2 120.4" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "daily" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Climbing \n", "\n", "In 2022 my friend [A. J. Jacobs](https://ajjacobs.com/) set a goal of **walking to space**: climbing a total elevation equal to the distance from the Earth's surface to the top of the atmoshere. [A group](https://www.facebook.com/groups/260966686136038) of about 40 of us joined the quest. The boundary of \"space\" is vague, but the [Kármán line](https://en.wikipedia.org/wiki/K%C3%A1rm%C3%A1n_line) is 100 kilometers; in 2022 I surpassed 100 kilometers of climbing (over 1,100 feet per day), but most years I'm closer to 60 kilometers (about 600 feet per day)." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Explorer Tiles\n", "\n", "\n", "The [OpenStreetMap](https://www.openstreetmap.org/) world map is divided into **[explorer tiles](https://www.statshunters.com/faq-10-what-are-explorer-tiles)** of approximately 1 mile square. Sites like [Veloviewer](https://veloviewer.com), [Statshunter](https://www.statshunters.com/), [RideEveryTile](https://rideeverytile.com/), and [SquadRats](https://squadrats.com/map) challenge bicyclist/hikers to record which tiles they have passed through. The process is gamified to highlight the following statistics:\n", "- The largest **square** (an *n* × *n* array of visited tiles). \n", "- The maximum **cluster** (a set of contiguous interior visited tiles, where \"interior\" means surrounded by visited tiles).\n", "- The **total** number of visited tiles.\n", " \n", "\n", "Since I live on a penninsula, it is not easy for me to form a large square, and I sometimes have to work hard to connect different parts of my map into my main cluster (such as connecting San Francisco and Marin). Here are a few key points along the way:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 datesquareclustertotalcomment
09/21/20241413943496Michael J. Fox ride in Sonoma
04/28/20241412753382Livermore
02/25/20241411963279Expanding through Santa Cruz and to the South
01/01/20241410563105Start of this year
12/08/20231410423084Benicia ride connects East Bay and Napa clusters
11/05/2023149322914Alum Rock ride gets 14x14 max square
06/30/2023136892640Rides in east Bay fill in holes
04/14/2023136302595Black Sands Beach low-tide hike connects Marin to max cluster
03/04/2023135832574Almaden rides connects Gilroy to max cluster
10/22/2022133962495Alviso levees to get to 13x13 max square
10/16/2022123932492Milpitas ride connects East Bay to max cluster
09/08/2022113002487First started tracking tiles
\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Wandering \n", "\n", "The website [**Wandrer.earth**](https://wandrer.earth) tracks the distinct roads a user has biked on. It provides a fun incentive to get out and explore new roads. The site is gamified in a way that there is a reward for first reaching 25% of the road-miles in each city, and further rewards for higher percentages. (You get no credit for repeating a road you've already been on.) \n", "\n", "The wandrer.earth site does a good job of showing my current status, but it requires clicking around a bit, so I summarize it all in one place here. Each line gives the **name** of a place (and maybe the **county** it is in), the **total** number of miles of roads and trails in the place, the number of miles I have **done**, the percentage (**pct**) done, the **badge** I have been awarded, and the number of miles to go **to next badge**, or to the next **big badge** (the big badge points are at 25% of the roads in an area (a bonus of 25%) and at 90% of the roads (a bonus of 50%)).\n", "\n", "First the big places (counties and countries, etc.), then the small places (cities and state parks, etc.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nametotaldonepctbadgeto next badgeto big badge
San Mateo County2826.051,93068.30%50%189 mi to 75% (472 points)613 mi to 90% (2,026 points)
Santa Clara County7690.852,78336.18%25%1,063 mi to 50% (1,447 points)4,139 mi to 90% (7,985 points)
Alameda County5802.831,09818.93%none352 mi to 25% (1,803 points)352 mi to 25% (1,803 points)
Marin County2453.1828911.79%none324 mi to 25% (937 points)324 mi to 25% (937 points)
Santa Cruz County2700.3628610.58%none389 mi to 25% (1,064 points)389 mi to 25% (1,064 points)
San Francisco County1234.8512410.04%none185 mi to 25% (493 points)185 mi to 25% (493 points)
Napa County1677.571498.90%none270 mi to 25% (689 points)270 mi to 25% (689 points)
Sonoma County4955.303747.55%none865 mi to 25% (2,104 points)865 mi to 25% (2,104 points)
Contra Costa County5905.562434.12%none1,233 mi to 25% (2,709 points)1,233 mi to 25% (2,709 points)
California387818.83770.0199%none0.2 mi to 0.02% (0.2 points)96,877 mi to 25% (0.19M points)
USA6447239.43820.0013%none1,208 mi to 0.02% (1,208 points)1.6M mi to 25% (3.2M points)
Earth45515258.11850.0002%none9,018 mi to 0.02% (9,018 points)11M mi to 25% (23M points)
\n", "
" ], "text/plain": [ " name total done pct badge \\\n", " San Mateo County 2826.05 1,930 68.30% 50% \n", " Santa Clara County 7690.85 2,783 36.18% 25% \n", " Alameda County 5802.83 1,098 18.93% none \n", " Marin County 2453.18 289 11.79% none \n", " Santa Cruz County 2700.36 286 10.58% none \n", " San Francisco County 1234.85 124 10.04% none \n", " Napa County 1677.57 149 8.90% none \n", " Sonoma County 4955.30 374 7.55% none \n", " Contra Costa County 5905.56 243 4.12% none \n", " California 387818.83 77 0.0199% none \n", " USA 6447239.43 82 0.0013% none \n", " Earth 45515258.11 85 0.0002% none \n", "\n", " to next badge to big badge \n", " 189 mi to 75% (472 points) 613 mi to 90% (2,026 points) \n", " 1,063 mi to 50% (1,447 points) 4,139 mi to 90% (7,985 points) \n", " 352 mi to 25% (1,803 points) 352 mi to 25% (1,803 points) \n", " 324 mi to 25% (937 points) 324 mi to 25% (937 points) \n", " 389 mi to 25% (1,064 points) 389 mi to 25% (1,064 points) \n", " 185 mi to 25% (493 points) 185 mi to 25% (493 points) \n", " 270 mi to 25% (689 points) 270 mi to 25% (689 points) \n", " 865 mi to 25% (2,104 points) 865 mi to 25% (2,104 points) \n", " 1,233 mi to 25% (2,709 points) 1,233 mi to 25% (2,709 points) \n", " 0.2 mi to 0.02% (0.2 points) 96,877 mi to 25% (0.19M points) \n", " 1,208 mi to 0.02% (1,208 points) 1.6M mi to 25% (3.2M points) \n", " 9,018 mi to 0.02% (9,018 points) 11M mi to 25% (23M points) " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "big_places" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namecountytotaldonepctbadgeto next badgeto big badge
AthertonSMC56.3056100%99%
North Fair OaksSMC26.7027100%99%
LaderaSMC8.108.1100%99%
Kensington SquareSMC0.600.6100%99%
Los Trancos WoodsSMC5.305.3100%99%
Menlo OaksSMC3.503.5100%99%
Emerald Lake HillsSMC24.6025100%99%
Los Trancos OSPSMC0.300.3100%99%
West Menlo ParkSMC11.2011100%99%
Foothills OS PreserveSCC1.101.1100%99%
Palomar ParkSMC4.004.0100%99%
Portola ValleySMC48.2048100%99%
Windy Hill PreserveSMC4.104.1100%99%
Sequoia TractSMC11.0011100%99%
East Palo AltoSMC48.304899.97%99%
Menlo ParkSMC139.5013999.97%99%
LoyolaSCC18.301899.94%99%
Los AltosSCC138.2013899.87%99%
Redwood CitySMC240.5024099.66%99%
StanfordSCC82.538299.66%99%
Palo AltoSCC297.2029699.52%99%
San CarlosSMC99.009999.51%99%
Mountain ViewSCC208.1020799.49%99%
Los Altos HillsSCC91.309199.46%99%
Foster CitySMC150.0014999.14%99%
WoodsideSMC75.207599.08%99%
Sky LondaSMC10.421098.49%90%0.1 mi to 99% (1.1 points)
Burleigh Murray ParkSMC2.102.095.08%90%0.1 mi to 99% (0.3 points)
San Mateo HighlandsSMC18.001792.36%90%1.2 mi to 99% (3.0 points)
BelmontSMC98.108990.43%90%8.4 mi to 99% (18 points)
CupertinoSCC172.0013176.32%75%24 mi to 90% (110 points)24 mi to 90% (110 points)
Skyline Ridge OSPSMC0.800.675.41%75%0.1 mi to 90% (0.5 points)0.1 mi to 90% (0.5 points)
Portola Redwoods SPSMC2.902.174.07%50%0.0 mi to 75% (0.3 points)0.5 mi to 90% (1.9 points)
Rosie Riveter ParkCCC5.504.073.20%50%0.1 mi to 75% (0.6 points)0.9 mi to 90% (3.7 points)
Burlingame HillsSMC6.004.371.45%50%0.2 mi to 75% (0.8 points)1.1 mi to 90% (4.1 points)
Coal Creek PreserveSMC3.902.768.74%50%0.2 mi to 75% (0.6 points)0.8 mi to 90% (2.8 points)
NewarkALA147.0010068.00%50%10 mi to 75% (25 points)32 mi to 90% (106 points)
San Francisco Bay TrailSCC260.8017767.77%50%19 mi to 75% (45 points)58 mi to 90% (188 points)
Half Moon Bay State BeachSMC4.402.965.89%50%0.4 mi to 75% (0.8 points)1.1 mi to 90% (3.3 points)
ColmaSMC13.708.964.79%50%1.4 mi to 75% (2.8 points)3.5 mi to 90% (10 points)
MontaraSMC27.801761.15%50%3.9 mi to 75% (6.6 points)8.0 mi to 90% (22 points)
Russian Ridge PreserveSMC12.207.359.66%50%1.9 mi to 75% (3.1 points)3.7 mi to 90% (9.8 points)
Moss BeachSMC19.701259.66%50%3.0 mi to 75% (5.0 points)6.0 mi to 90% (16 points)
SunnyvaleSCC357.0020858.34%50%59 mi to 75% (95 points)113 mi to 90% (292 points)
BurlingameSMC88.405056.50%50%16 mi to 75% (25 points)30 mi to 90% (74 points)
San MateoSMC256.0014054.64%50%52 mi to 75% (78 points)91 mi to 90% (219 points)
SaratogaSCC180.009753.84%50%38 mi to 75% (56 points)65 mi to 90% (155 points)
El GranadaSMC49.202653.71%50%10 mi to 75% (15 points)18 mi to 90% (42 points)
HillsboroughSMC85.304553.12%50%19 mi to 75% (27 points)31 mi to 90% (74 points)
Monte SerenoSCC20.401153.04%50%4.5 mi to 75% (6.5 points)7.5 mi to 90% (18 points)
MillbraeSMC67.803552.05%50%16 mi to 75% (22 points)26 mi to 90% (60 points)
Los GatosSCC148.007752.05%50%34 mi to 75% (49 points)56 mi to 90% (130 points)
Castle Rock State ParkSCC11.205.751.20%50%2.7 mi to 75% (3.8 points)4.3 mi to 90% (9.9 points)
Half Moon BaySMC68.003450.56%50%17 mi to 75% (23 points)27 mi to 90% (61 points)
BrisbaneSMC40.902049.62%25%0.2 mi to 50% (2.2 points)17 mi to 90% (37 points)
EdenvaleSCC30.001447.65%25%0.7 mi to 50% (2.2 points)13 mi to 90% (28 points)
BarangarooNSW1.700.847.30%25%0.0 mi to 50% (0.1 points)0.7 mi to 90% (1.6 points)
GardnerSCC23.401146.93%25%0.7 mi to 50% (1.9 points)10 mi to 90% (22 points)
Long Ridge PreserveSMC11.005.045.10%25%0.5 mi to 50% (1.1 points)4.9 mi to 90% (10 points)
Presidio TerraceSFC2.801.243.90%25%0.2 mi to 50% (0.3 points)1.3 mi to 90% (2.7 points)
Hayward AcresALA3.501.543.53%25%0.2 mi to 50% (0.4 points)1.6 mi to 90% (3.4 points)
San LorenzoALA55.502340.95%25%5.0 mi to 50% (7.8 points)27 mi to 90% (55 points)
Lincoln ParkSFC4.501.839.60%25%0.5 mi to 50% (0.7 points)2.3 mi to 90% (4.5 points)
Purisima Creek PreserveSMC16.506.439.09%25%1.8 mi to 50% (2.6 points)8.4 mi to 90% (17 points)
Mt Tamalpais State ParkMAR31.701238.46%25%3.7 mi to 50% (5.2 points)16 mi to 90% (32 points)
BroadmoorSMC8.803.438.26%25%1.0 mi to 50% (1.5 points)4.6 mi to 90% (9.0 points)
South BeachSFC4.801.837.40%25%0.6 mi to 50% (0.8 points)2.5 mi to 90% (4.9 points)
MilpitasSCC224.008437.36%25%28 mi to 50% (40 points)118 mi to 90% (230 points)
Muir BeachMAR4.601.737.10%25%0.6 mi to 50% (0.8 points)2.4 mi to 90% (4.7 points)
PacificaSMC150.905636.80%25%20 mi to 50% (27 points)80 mi to 90% (156 points)
Lake StreetSFC3.901.436.80%25%0.5 mi to 50% (0.7 points)2.1 mi to 90% (4.0 points)
Spartan KeyesSCC64.302436.59%25%8.6 mi to 50% (12 points)34 mi to 90% (66 points)
AshlandALA35.101336.49%25%4.7 mi to 50% (6.5 points)19 mi to 90% (36 points)
San BrunoSMC114.004236.42%25%15 mi to 50% (21 points)61 mi to 90% (118 points)
Willow GlenSCC81.603036.23%25%11 mi to 50% (15 points)44 mi to 90% (85 points)
FremontALA780.2027835.58%25%113 mi to 50% (152 points)425 mi to 90% (815 points)
Communications HillSCC27.809.835.31%25%4.1 mi to 50% (5.5 points)15 mi to 90% (29 points)
Santa ClaraSCC348.0012335.26%25%51 mi to 50% (69 points)190 mi to 90% (364 points)
MITMAS9.603.334.70%25%1.5 mi to 50% (1.9 points)5.3 mi to 90% (10 points)
Millers PointNSW3.201.134.30%25%0.5 mi to 50% (0.7 points)1.8 mi to 90% (3.4 points)
ParkviewSCC42.501434.07%25%6.8 mi to 50% (8.9 points)24 mi to 90% (45 points)
Seven TreesSCC40.901434.06%25%6.5 mi to 50% (8.6 points)23 mi to 90% (43 points)
Union CityALA208.807033.36%25%35 mi to 50% (45 points)118 mi to 90% (223 points)
HaywardALA444.5014833.24%25%74 mi to 50% (97 points)252 mi to 90% (475 points)
BranhamSCC44.001533.21%25%7.4 mi to 50% (9.6 points)25 mi to 90% (47 points)
Stinson BeachMAR11.203.732.90%25%1.9 mi to 50% (2.5 points)6.4 mi to 90% (12 points)
Marin Headlands GGNRAMAR65.702131.90%25%12 mi to 50% (15 points)38 mi to 90% (71 points)
San MartinSCC35.301131.41%25%6.6 mi to 50% (8.3 points)21 mi to 90% (38 points)
Willow Glen SouthSCC63.302031.02%25%12 mi to 50% (15 points)37 mi to 90% (69 points)
South San FranciscoSMC185.305730.97%25%35 mi to 50% (45 points)109 mi to 90% (202 points)
Forest of Nisene Marks SPSCC44.001430.70%25%8.5 mi to 50% (11 points)26 mi to 90% (48 points)
CampbellSCC119.003630.48%25%23 mi to 50% (29 points)71 mi to 90% (130 points)
Golden Gate ParkSFC40.801229.40%25%8.4 mi to 50% (10 points)25 mi to 90% (45 points)
SeacliffSFC4.101.229.30%25%0.8 mi to 50% (1.1 points)2.5 mi to 90% (4.5 points)
Butano State ParkSMC15.204.529.28%25%3.1 mi to 50% (3.9 points)9.2 mi to 90% (17 points)
Dawes PointNSW1.800.529.20%25%0.4 mi to 50% (0.5 points)1.1 mi to 90% (2.0 points)
FairviewALA34.401029.18%25%7.2 mi to 50% (8.9 points)21 mi to 90% (38 points)
Bay Area Ridge TrailSMC395.6011529.02%25%83 mi to 50% (103 points)241 mi to 90% (439 points)
San JoseSCC2618.7074928.59%25%561 mi to 50% (692 points)1,608 mi to 90% (2,917 points)
Daly CitySMC148.104228.42%25%32 mi to 50% (39 points)91 mi to 90% (165 points)
San LeandroALA230.606528.18%25%50 mi to 50% (62 points)143 mi to 90% (258 points)
CherrylandALA20.905.827.97%25%4.6 mi to 50% (5.6 points)13 mi to 90% (23 points)
SausalitoMAR32.709.127.90%25%7.2 mi to 50% (8.9 points)20 mi to 90% (37 points)
GilroySCC188.905126.88%25%44 mi to 50% (53 points)119 mi to 90% (214 points)
El Corte de Madera OSPSMC34.549.326.85%25%8.0 mi to 50% (9.7 points)22 mi to 90% (39 points)
DublinALA225.946126.84%25%52 mi to 50% (64 points)143 mi to 90% (256 points)
Mokelumne HillCAL14.703.926.80%25%3.4 mi to 50% (4.1 points)9.3 mi to 90% (17 points)
Presidio National ParkSFC43.501226.70%25%10 mi to 50% (12 points)28 mi to 90% (49 points)
Castro ValleyALA192.505126.48%25%45 mi to 50% (55 points)122 mi to 90% (219 points)
GuernevilleSON22.705.524.08%none0.2 mi to 25% (5.9 points)0.2 mi to 25% (5.9 points)
Mt Tamalpais WatershedMAR102.872322.55%none2.5 mi to 25% (28 points)2.5 mi to 25% (28 points)
Presidio HeightsSFC6.501.421.60%none0.2 mi to 25% (1.8 points)0.2 mi to 25% (1.8 points)
PanhandleSFC7.301.520.60%none0.3 mi to 25% (2.1 points)0.3 mi to 25% (2.1 points)
Balboa TerraceSFC3.400.618.20%none0.2 mi to 25% (1.1 points)0.2 mi to 25% (1.1 points)
Polk GulchSFC4.000.718.20%none0.3 mi to 25% (1.3 points)0.3 mi to 25% (1.3 points)
Cole ValleySFC1.700.318.00%none0.1 mi to 25% (0.5 points)0.1 mi to 25% (0.5 points)
Bodega BaySON28.905.017.23%none2.2 mi to 25% (9.5 points)2.2 mi to 25% (9.5 points)
Forest HillSFC6.101.015.90%none0.6 mi to 25% (2.1 points)0.6 mi to 25% (2.1 points)
Northern WaterfrontSFC5.600.915.50%none0.5 mi to 25% (1.9 points)0.5 mi to 25% (1.9 points)
Aquatic Park Fort MasonSFC6.401.015.40%none0.6 mi to 25% (2.2 points)0.6 mi to 25% (2.2 points)
Little HollywoodSFC3.700.615.20%none0.4 mi to 25% (1.3 points)0.4 mi to 25% (1.3 points)
Clarendon HeightsSFC6.000.914.20%none0.6 mi to 25% (2.1 points)0.6 mi to 25% (2.1 points)
Fisherman's WharfSFC6.200.913.80%none0.7 mi to 25% (2.2 points)0.7 mi to 25% (2.2 points)
Mill ValleyMAR92.201213.43%none11 mi to 25% (34 points)11 mi to 25% (34 points)
Sutro HeightsSFC7.100.913.20%none0.8 mi to 25% (2.6 points)0.8 mi to 25% (2.6 points)
Ashbury HeightsSFC3.700.513.00%none0.4 mi to 25% (1.4 points)0.4 mi to 25% (1.4 points)
Corte MaderaMAR51.006.612.90%none6.2 mi to 25% (19 points)6.2 mi to 25% (19 points)
AlamedaALA206.702612.41%none26 mi to 25% (78 points)26 mi to 25% (78 points)
DogpatchSFC5.100.612.30%none0.6 mi to 25% (1.9 points)0.6 mi to 25% (1.9 points)
Cow HollowSFC12.001.411.90%none1.6 mi to 25% (4.6 points)1.6 mi to 25% (4.6 points)
Golden Gate HeightsSFC17.801.910.70%none2.5 mi to 25% (7.0 points)2.5 mi to 25% (7.0 points)
Pacific HeightsSFC18.001.910.70%none2.6 mi to 25% (7.1 points)2.6 mi to 25% (7.1 points)
Financial DistrictSFC9.401.010.20%none1.4 mi to 25% (3.7 points)1.4 mi to 25% (3.7 points)
San RamonALA306.46299.36%none48 mi to 25% (125 points)48 mi to 25% (125 points)
Mission BaySFC13.801.28.60%none2.3 mi to 25% (5.7 points)2.3 mi to 25% (5.7 points)
BerkeleyALA260.30207.84%none45 mi to 25% (110 points)45 mi to 25% (110 points)
EmeryvilleALA28.102.27.72%none4.9 mi to 25% (12 points)4.9 mi to 25% (12 points)
PleasontonALA344.91267.52%none60 mi to 25% (147 points)60 mi to 25% (147 points)
AlbanyALA42.703.06.96%none7.7 mi to 25% (18 points)7.7 mi to 25% (18 points)
HealdsburgSON56.593.66.42%none11 mi to 25% (25 points)11 mi to 25% (25 points)
CambridgeMAS180.80116.20%none34 mi to 25% (79 points)34 mi to 25% (79 points)
Central WaterfrontSFC10.200.66.00%none1.9 mi to 25% (4.5 points)1.9 mi to 25% (4.5 points)
LivermoreALA448.52245.46%none88 mi to 25% (200 points)88 mi to 25% (200 points)
San RafaelMAR260.009.63.70%none55 mi to 25% (120 points)55 mi to 25% (120 points)
\n", "
" ], "text/plain": [ " name county total done pct badge \\\n", " Atherton SMC 56.30 56 100% 99% \n", " North Fair Oaks SMC 26.70 27 100% 99% \n", " Ladera SMC 8.10 8.1 100% 99% \n", " Kensington Square SMC 0.60 0.6 100% 99% \n", " Los Trancos Woods SMC 5.30 5.3 100% 99% \n", " Menlo Oaks SMC 3.50 3.5 100% 99% \n", " Emerald Lake Hills SMC 24.60 25 100% 99% \n", " Los Trancos OSP SMC 0.30 0.3 100% 99% \n", " West Menlo Park SMC 11.20 11 100% 99% \n", " Foothills OS Preserve SCC 1.10 1.1 100% 99% \n", " Palomar Park SMC 4.00 4.0 100% 99% \n", " Portola Valley SMC 48.20 48 100% 99% \n", " Windy Hill Preserve SMC 4.10 4.1 100% 99% \n", " Sequoia Tract SMC 11.00 11 100% 99% \n", " East Palo Alto SMC 48.30 48 99.97% 99% \n", " Menlo Park SMC 139.50 139 99.97% 99% \n", " Loyola SCC 18.30 18 99.94% 99% \n", " Los Altos SCC 138.20 138 99.87% 99% \n", " Redwood City SMC 240.50 240 99.66% 99% \n", " Stanford SCC 82.53 82 99.66% 99% \n", " Palo Alto SCC 297.20 296 99.52% 99% \n", " San Carlos SMC 99.00 99 99.51% 99% \n", " Mountain View SCC 208.10 207 99.49% 99% \n", " Los Altos Hills SCC 91.30 91 99.46% 99% \n", " Foster City SMC 150.00 149 99.14% 99% \n", " Woodside SMC 75.20 75 99.08% 99% \n", " Sky Londa SMC 10.42 10 98.49% 90% \n", " Burleigh Murray Park SMC 2.10 2.0 95.08% 90% \n", " San Mateo Highlands SMC 18.00 17 92.36% 90% \n", " Belmont SMC 98.10 89 90.43% 90% \n", " Cupertino SCC 172.00 131 76.32% 75% \n", " Skyline Ridge OSP SMC 0.80 0.6 75.41% 75% \n", " Portola Redwoods SP SMC 2.90 2.1 74.07% 50% \n", " Rosie Riveter Park CCC 5.50 4.0 73.20% 50% \n", " Burlingame Hills SMC 6.00 4.3 71.45% 50% \n", " Coal Creek Preserve SMC 3.90 2.7 68.74% 50% \n", " Newark ALA 147.00 100 68.00% 50% \n", " San Francisco Bay Trail SCC 260.80 177 67.77% 50% \n", " Half Moon Bay State Beach SMC 4.40 2.9 65.89% 50% \n", " Colma SMC 13.70 8.9 64.79% 50% \n", " Montara SMC 27.80 17 61.15% 50% \n", " Russian Ridge Preserve SMC 12.20 7.3 59.66% 50% \n", " Moss Beach SMC 19.70 12 59.66% 50% \n", " Sunnyvale SCC 357.00 208 58.34% 50% \n", " Burlingame SMC 88.40 50 56.50% 50% \n", " San Mateo SMC 256.00 140 54.64% 50% \n", " Saratoga SCC 180.00 97 53.84% 50% \n", " El Granada SMC 49.20 26 53.71% 50% \n", " Hillsborough SMC 85.30 45 53.12% 50% \n", " Monte Sereno SCC 20.40 11 53.04% 50% \n", " Millbrae SMC 67.80 35 52.05% 50% \n", " Los Gatos SCC 148.00 77 52.05% 50% \n", " Castle Rock State Park SCC 11.20 5.7 51.20% 50% \n", " Half Moon Bay SMC 68.00 34 50.56% 50% \n", " Brisbane SMC 40.90 20 49.62% 25% \n", " Edenvale SCC 30.00 14 47.65% 25% \n", " Barangaroo NSW 1.70 0.8 47.30% 25% \n", " Gardner SCC 23.40 11 46.93% 25% \n", " Long Ridge Preserve SMC 11.00 5.0 45.10% 25% \n", " Presidio Terrace SFC 2.80 1.2 43.90% 25% \n", " Hayward Acres ALA 3.50 1.5 43.53% 25% \n", " San Lorenzo ALA 55.50 23 40.95% 25% \n", " Lincoln Park SFC 4.50 1.8 39.60% 25% \n", " Purisima Creek Preserve SMC 16.50 6.4 39.09% 25% \n", " Mt Tamalpais State Park MAR 31.70 12 38.46% 25% \n", " Broadmoor SMC 8.80 3.4 38.26% 25% \n", " South Beach SFC 4.80 1.8 37.40% 25% \n", " Milpitas SCC 224.00 84 37.36% 25% \n", " Muir Beach MAR 4.60 1.7 37.10% 25% \n", " Pacifica SMC 150.90 56 36.80% 25% \n", " Lake Street SFC 3.90 1.4 36.80% 25% \n", " Spartan Keyes SCC 64.30 24 36.59% 25% \n", " Ashland ALA 35.10 13 36.49% 25% \n", " San Bruno SMC 114.00 42 36.42% 25% \n", " Willow Glen SCC 81.60 30 36.23% 25% \n", " Fremont ALA 780.20 278 35.58% 25% \n", " Communications Hill SCC 27.80 9.8 35.31% 25% \n", " Santa Clara SCC 348.00 123 35.26% 25% \n", " MIT MAS 9.60 3.3 34.70% 25% \n", " Millers Point NSW 3.20 1.1 34.30% 25% \n", " Parkview SCC 42.50 14 34.07% 25% \n", " Seven Trees SCC 40.90 14 34.06% 25% \n", " Union City ALA 208.80 70 33.36% 25% \n", " Hayward ALA 444.50 148 33.24% 25% \n", " Branham SCC 44.00 15 33.21% 25% \n", " Stinson Beach MAR 11.20 3.7 32.90% 25% \n", " Marin Headlands GGNRA MAR 65.70 21 31.90% 25% \n", " San Martin SCC 35.30 11 31.41% 25% \n", " Willow Glen South SCC 63.30 20 31.02% 25% \n", " South San Francisco SMC 185.30 57 30.97% 25% \n", " Forest of Nisene Marks SP SCC 44.00 14 30.70% 25% \n", " Campbell SCC 119.00 36 30.48% 25% \n", " Golden Gate Park SFC 40.80 12 29.40% 25% \n", " Seacliff SFC 4.10 1.2 29.30% 25% \n", " Butano State Park SMC 15.20 4.5 29.28% 25% \n", " Dawes Point NSW 1.80 0.5 29.20% 25% \n", " Fairview ALA 34.40 10 29.18% 25% \n", " Bay Area Ridge Trail SMC 395.60 115 29.02% 25% \n", " San Jose SCC 2618.70 749 28.59% 25% \n", " Daly City SMC 148.10 42 28.42% 25% \n", " San Leandro ALA 230.60 65 28.18% 25% \n", " Cherryland ALA 20.90 5.8 27.97% 25% \n", " Sausalito MAR 32.70 9.1 27.90% 25% \n", " Gilroy SCC 188.90 51 26.88% 25% \n", " El Corte de Madera OSP SMC 34.54 9.3 26.85% 25% \n", " Dublin ALA 225.94 61 26.84% 25% \n", " Mokelumne Hill CAL 14.70 3.9 26.80% 25% \n", " Presidio National Park SFC 43.50 12 26.70% 25% \n", " Castro Valley ALA 192.50 51 26.48% 25% \n", " Guerneville SON 22.70 5.5 24.08% none \n", " Mt Tamalpais Watershed MAR 102.87 23 22.55% none \n", " Presidio Heights SFC 6.50 1.4 21.60% none \n", " Panhandle SFC 7.30 1.5 20.60% none \n", " Balboa Terrace SFC 3.40 0.6 18.20% none \n", " Polk Gulch SFC 4.00 0.7 18.20% none \n", " Cole Valley SFC 1.70 0.3 18.00% none \n", " Bodega Bay SON 28.90 5.0 17.23% none \n", " Forest Hill SFC 6.10 1.0 15.90% none \n", " Northern Waterfront SFC 5.60 0.9 15.50% none \n", " Aquatic Park Fort Mason SFC 6.40 1.0 15.40% none \n", " Little Hollywood SFC 3.70 0.6 15.20% none \n", " Clarendon Heights SFC 6.00 0.9 14.20% none \n", " Fisherman's Wharf SFC 6.20 0.9 13.80% none \n", " Mill Valley MAR 92.20 12 13.43% none \n", " Sutro Heights SFC 7.10 0.9 13.20% none \n", " Ashbury Heights SFC 3.70 0.5 13.00% none \n", " Corte Madera MAR 51.00 6.6 12.90% none \n", " Alameda ALA 206.70 26 12.41% none \n", " Dogpatch SFC 5.10 0.6 12.30% none \n", " Cow Hollow SFC 12.00 1.4 11.90% none \n", " Golden Gate Heights SFC 17.80 1.9 10.70% none \n", " Pacific Heights SFC 18.00 1.9 10.70% none \n", " Financial District SFC 9.40 1.0 10.20% none \n", " San Ramon ALA 306.46 29 9.36% none \n", " Mission Bay SFC 13.80 1.2 8.60% none \n", " Berkeley ALA 260.30 20 7.84% none \n", " Emeryville ALA 28.10 2.2 7.72% none \n", " Pleasonton ALA 344.91 26 7.52% none \n", " Albany ALA 42.70 3.0 6.96% none \n", " Healdsburg SON 56.59 3.6 6.42% none \n", " Cambridge MAS 180.80 11 6.20% none \n", " Central Waterfront SFC 10.20 0.6 6.00% none \n", " Livermore ALA 448.52 24 5.46% none \n", " San Rafael MAR 260.00 9.6 3.70% none \n", "\n", " to next badge to big badge \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0.1 mi to 99% (1.1 points) \n", " 0.1 mi to 99% (0.3 points) \n", " 1.2 mi to 99% (3.0 points) \n", " 8.4 mi to 99% (18 points) \n", " 24 mi to 90% (110 points) 24 mi to 90% (110 points) \n", " 0.1 mi to 90% (0.5 points) 0.1 mi to 90% (0.5 points) \n", " 0.0 mi to 75% (0.3 points) 0.5 mi to 90% (1.9 points) \n", " 0.1 mi to 75% (0.6 points) 0.9 mi to 90% (3.7 points) \n", " 0.2 mi to 75% (0.8 points) 1.1 mi to 90% (4.1 points) \n", " 0.2 mi to 75% (0.6 points) 0.8 mi to 90% (2.8 points) \n", " 10 mi to 75% (25 points) 32 mi to 90% (106 points) \n", " 19 mi to 75% (45 points) 58 mi to 90% (188 points) \n", " 0.4 mi to 75% (0.8 points) 1.1 mi to 90% (3.3 points) \n", " 1.4 mi to 75% (2.8 points) 3.5 mi to 90% (10 points) \n", " 3.9 mi to 75% (6.6 points) 8.0 mi to 90% (22 points) \n", " 1.9 mi to 75% (3.1 points) 3.7 mi to 90% (9.8 points) \n", " 3.0 mi to 75% (5.0 points) 6.0 mi to 90% (16 points) \n", " 59 mi to 75% (95 points) 113 mi to 90% (292 points) \n", " 16 mi to 75% (25 points) 30 mi to 90% (74 points) \n", " 52 mi to 75% (78 points) 91 mi to 90% (219 points) \n", " 38 mi to 75% (56 points) 65 mi to 90% (155 points) \n", " 10 mi to 75% (15 points) 18 mi to 90% (42 points) \n", " 19 mi to 75% (27 points) 31 mi to 90% (74 points) \n", " 4.5 mi to 75% (6.5 points) 7.5 mi to 90% (18 points) \n", " 16 mi to 75% (22 points) 26 mi to 90% (60 points) \n", " 34 mi to 75% (49 points) 56 mi to 90% (130 points) \n", " 2.7 mi to 75% (3.8 points) 4.3 mi to 90% (9.9 points) \n", " 17 mi to 75% (23 points) 27 mi to 90% (61 points) \n", " 0.2 mi to 50% (2.2 points) 17 mi to 90% (37 points) \n", " 0.7 mi to 50% (2.2 points) 13 mi to 90% (28 points) \n", " 0.0 mi to 50% (0.1 points) 0.7 mi to 90% (1.6 points) \n", " 0.7 mi to 50% (1.9 points) 10 mi to 90% (22 points) \n", " 0.5 mi to 50% (1.1 points) 4.9 mi to 90% (10 points) \n", " 0.2 mi to 50% (0.3 points) 1.3 mi to 90% (2.7 points) \n", " 0.2 mi to 50% (0.4 points) 1.6 mi to 90% (3.4 points) \n", " 5.0 mi to 50% (7.8 points) 27 mi to 90% (55 points) \n", " 0.5 mi to 50% (0.7 points) 2.3 mi to 90% (4.5 points) \n", " 1.8 mi to 50% (2.6 points) 8.4 mi to 90% (17 points) \n", " 3.7 mi to 50% (5.2 points) 16 mi to 90% (32 points) \n", " 1.0 mi to 50% (1.5 points) 4.6 mi to 90% (9.0 points) \n", " 0.6 mi to 50% (0.8 points) 2.5 mi to 90% (4.9 points) \n", " 28 mi to 50% (40 points) 118 mi to 90% (230 points) \n", " 0.6 mi to 50% (0.8 points) 2.4 mi to 90% (4.7 points) \n", " 20 mi to 50% (27 points) 80 mi to 90% (156 points) \n", " 0.5 mi to 50% (0.7 points) 2.1 mi to 90% (4.0 points) \n", " 8.6 mi to 50% (12 points) 34 mi to 90% (66 points) \n", " 4.7 mi to 50% (6.5 points) 19 mi to 90% (36 points) \n", " 15 mi to 50% (21 points) 61 mi to 90% (118 points) \n", " 11 mi to 50% (15 points) 44 mi to 90% (85 points) \n", " 113 mi to 50% (152 points) 425 mi to 90% (815 points) \n", " 4.1 mi to 50% (5.5 points) 15 mi to 90% (29 points) \n", " 51 mi to 50% (69 points) 190 mi to 90% (364 points) \n", " 1.5 mi to 50% (1.9 points) 5.3 mi to 90% (10 points) \n", " 0.5 mi to 50% (0.7 points) 1.8 mi to 90% (3.4 points) \n", " 6.8 mi to 50% (8.9 points) 24 mi to 90% (45 points) \n", " 6.5 mi to 50% (8.6 points) 23 mi to 90% (43 points) \n", " 35 mi to 50% (45 points) 118 mi to 90% (223 points) \n", " 74 mi to 50% (97 points) 252 mi to 90% (475 points) \n", " 7.4 mi to 50% (9.6 points) 25 mi to 90% (47 points) \n", " 1.9 mi to 50% (2.5 points) 6.4 mi to 90% (12 points) \n", " 12 mi to 50% (15 points) 38 mi to 90% (71 points) \n", " 6.6 mi to 50% (8.3 points) 21 mi to 90% (38 points) \n", " 12 mi to 50% (15 points) 37 mi to 90% (69 points) \n", " 35 mi to 50% (45 points) 109 mi to 90% (202 points) \n", " 8.5 mi to 50% (11 points) 26 mi to 90% (48 points) \n", " 23 mi to 50% (29 points) 71 mi to 90% (130 points) \n", " 8.4 mi to 50% (10 points) 25 mi to 90% (45 points) \n", " 0.8 mi to 50% (1.1 points) 2.5 mi to 90% (4.5 points) \n", " 3.1 mi to 50% (3.9 points) 9.2 mi to 90% (17 points) \n", " 0.4 mi to 50% (0.5 points) 1.1 mi to 90% (2.0 points) \n", " 7.2 mi to 50% (8.9 points) 21 mi to 90% (38 points) \n", " 83 mi to 50% (103 points) 241 mi to 90% (439 points) \n", " 561 mi to 50% (692 points) 1,608 mi to 90% (2,917 points) \n", " 32 mi to 50% (39 points) 91 mi to 90% (165 points) \n", " 50 mi to 50% (62 points) 143 mi to 90% (258 points) \n", " 4.6 mi to 50% (5.6 points) 13 mi to 90% (23 points) \n", " 7.2 mi to 50% (8.9 points) 20 mi to 90% (37 points) \n", " 44 mi to 50% (53 points) 119 mi to 90% (214 points) \n", " 8.0 mi to 50% (9.7 points) 22 mi to 90% (39 points) \n", " 52 mi to 50% (64 points) 143 mi to 90% (256 points) \n", " 3.4 mi to 50% (4.1 points) 9.3 mi to 90% (17 points) \n", " 10 mi to 50% (12 points) 28 mi to 90% (49 points) \n", " 45 mi to 50% (55 points) 122 mi to 90% (219 points) \n", " 0.2 mi to 25% (5.9 points) 0.2 mi to 25% (5.9 points) \n", " 2.5 mi to 25% (28 points) 2.5 mi to 25% (28 points) \n", " 0.2 mi to 25% (1.8 points) 0.2 mi to 25% (1.8 points) \n", " 0.3 mi to 25% (2.1 points) 0.3 mi to 25% (2.1 points) \n", " 0.2 mi to 25% (1.1 points) 0.2 mi to 25% (1.1 points) \n", " 0.3 mi to 25% (1.3 points) 0.3 mi to 25% (1.3 points) \n", " 0.1 mi to 25% (0.5 points) 0.1 mi to 25% (0.5 points) \n", " 2.2 mi to 25% (9.5 points) 2.2 mi to 25% (9.5 points) \n", " 0.6 mi to 25% (2.1 points) 0.6 mi to 25% (2.1 points) \n", " 0.5 mi to 25% (1.9 points) 0.5 mi to 25% (1.9 points) \n", " 0.6 mi to 25% (2.2 points) 0.6 mi to 25% (2.2 points) \n", " 0.4 mi to 25% (1.3 points) 0.4 mi to 25% (1.3 points) \n", " 0.6 mi to 25% (2.1 points) 0.6 mi to 25% (2.1 points) \n", " 0.7 mi to 25% (2.2 points) 0.7 mi to 25% (2.2 points) \n", " 11 mi to 25% (34 points) 11 mi to 25% (34 points) \n", " 0.8 mi to 25% (2.6 points) 0.8 mi to 25% (2.6 points) \n", " 0.4 mi to 25% (1.4 points) 0.4 mi to 25% (1.4 points) \n", " 6.2 mi to 25% (19 points) 6.2 mi to 25% (19 points) \n", " 26 mi to 25% (78 points) 26 mi to 25% (78 points) \n", " 0.6 mi to 25% (1.9 points) 0.6 mi to 25% (1.9 points) \n", " 1.6 mi to 25% (4.6 points) 1.6 mi to 25% (4.6 points) \n", " 2.5 mi to 25% (7.0 points) 2.5 mi to 25% (7.0 points) \n", " 2.6 mi to 25% (7.1 points) 2.6 mi to 25% (7.1 points) \n", " 1.4 mi to 25% (3.7 points) 1.4 mi to 25% (3.7 points) \n", " 48 mi to 25% (125 points) 48 mi to 25% (125 points) \n", " 2.3 mi to 25% (5.7 points) 2.3 mi to 25% (5.7 points) \n", " 45 mi to 25% (110 points) 45 mi to 25% (110 points) \n", " 4.9 mi to 25% (12 points) 4.9 mi to 25% (12 points) \n", " 60 mi to 25% (147 points) 60 mi to 25% (147 points) \n", " 7.7 mi to 25% (18 points) 7.7 mi to 25% (18 points) \n", " 11 mi to 25% (25 points) 11 mi to 25% (25 points) \n", " 34 mi to 25% (79 points) 34 mi to 25% (79 points) \n", " 1.9 mi to 25% (4.5 points) 1.9 mi to 25% (4.5 points) \n", " 88 mi to 25% (200 points) 88 mi to 25% (200 points) \n", " 55 mi to 25% (120 points) 55 mi to 25% (120 points) " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "small_places" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As part of my wandering, in April 2022 I was able to get to 25% of every city that rings the San Francisco Bay and is below San Francisco or Oakland (see map [with](ring2.jpeg) or [without](ring1.jpeg) roads traveled; as soon as you get 25% of a city, it lights up with a color).\n", "\n", "I live at the border of Santa Clara County (SCC) and San Mateo County (SMC), so I ride in both. Wandrer.earth says that Jason Molenda is a whopping 1,700 miles ahead of me in SCC and Megan Gardner is 1,000 miles ahead of me in SMC. Barry Mann is the leader in total miles in the two counties, and Megan leads in average percent. Kudos to all of them! However, I do occupy a small section of the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front) for the two counties together: no single rider on wandrer.earth has done more than me in *both* counties. Here are the leaders (as of December 2023), where the dotted line indicates the Pareto front." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameInitialsSMC %SCC %SMC milesSCC milesTotal milesAvg %
Megan GardnerMG99.3119.3627951465426059.335
Barry MannBM77.9130.7021922324451654.305
Peter NorvigPN67.1435.4518892683457251.295
Brian FeinbergBF36.7645.3010343429446341.030
Jason MolendaJM7.6056.092144245445931.845
\n", "
" ], "text/plain": [ " Name Initials SMC % SCC % SMC miles SCC miles Total miles \\\n", " Megan Gardner MG 99.31 19.36 2795 1465 4260 \n", " Barry Mann BM 77.91 30.70 2192 2324 4516 \n", " Peter Norvig PN 67.14 35.45 1889 2683 4572 \n", " Brian Feinberg BF 36.76 45.30 1034 3429 4463 \n", " Jason Molenda JM 7.60 56.09 214 4245 4459 \n", "\n", " Avg % \n", " 59.335 \n", " 54.305 \n", " 51.295 \n", " 41.030 \n", " 31.845 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pareto_front(leaders)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Eddington Number\n", "\n", "The physicist/bicyclist [Sir Arthur Eddington](https://en.wikipedia.org/wiki/Arthur_Eddington), a contemporary of Einstein defined the [**Eddington Number**](https://www.triathlete.com/2011/04/training/measuring-bike-miles-eddington-number_301789) as the largest integer **E** such that you have cycled at least **E** miles on at least **E** days.\n", "\n", "My Eddington number progress over the years, in both kilometers and miles:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearEd_kmEd_mi
202410469
202310167
20229666
20219365
20208762
20198056
20187754
20177351
20166747
20156142
20144635
\n", "
" ], "text/plain": [ " year Ed_km Ed_mi\n", " 2024 104 69\n", " 2023 101 67\n", " 2022 96 66\n", " 2021 93 65\n", " 2020 87 62\n", " 2019 80 56\n", " 2018 77 54\n", " 2017 73 51\n", " 2016 67 47\n", " 2015 61 42\n", " 2014 46 35" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ed_progress(rides)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My current Eddington Number is **104** in kilometers and **69** in miles (I've ridden at least 69 miles on at least 69 different days, but not 70 miles on 70 different days). My number is above [the median for Strava](https://swinny.net/Cycling/-4687-Calculate-your-Eddington-Number), but not nearly as good as Eddington himself: his number was **84** (in miles) when he died at age 62, and his roads, weather, bicycles, and navigation aids were not nearly as nice as mine, so hip hip and bravo zulu to him. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many more rides will I need to reach higher Eddington numbers? I call that the *Eddington Gap*:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
kmskms gapmilesmiles gap
10537011
10677121
107157225
108197332
109257436
110337545
111417648
112527751
113577856
\n", "
" ], "text/plain": [ " kms kms gap miles miles gap\n", " 105 3 70 11\n", " 106 7 71 21\n", " 107 15 72 25\n", " 108 19 73 32\n", " 109 25 74 36\n", " 110 33 75 45\n", " 111 41 76 48\n", " 112 52 77 51\n", " 113 57 78 56" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ed_gaps(rides)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I need 3 rides of 105 km to increase my metric Eddington number to 105 kms, and I need 11 rides of 70 miles to increase my Imperial number. \n", "\n", "A 70 mile ride is getting somewhat long for me, so I might switch from Eddington numbers to number of metric centuries:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "126" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count(rides.kms >= 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some properties of Eddington numbers:\n", "- Your Eddington number is monotonic: it can never decrease over time. \n", "- To improve from an Eddington number of *n* to *n* + 1 can take as few as 1 ride, or as many as *n* + 1 rides.\n", " + *Suppose you have done 9 rides, each of exactly 10 miles. Your Eddington number is 9.*\n", " + *You would need 1 ride of 10 miles to improve from a number of 9 to 10.*\n", " + *You would then need 11 rides of 11 miles to improve from a number 10 to 11.*\n", "- Your metric Eddington number will always be greater than or equal to your imperial Eddington number.\n", "- Your metric Eddington number will never be more than 1.61 times your imperial Eddington number.\n", "- Of two riders, it is possible that one has a higher metric number and the other a higher imperial number.\n", "\n", "**Note:** the definition of Eddington Number seems precise, but what exactly does ***day*** mean? The New Oxford dictionary has three senses:\n", "\n", "1. *a period of 24 hours;*\n", "2. *a unit of time, reckoned from one midnight to the next;*\n", "3. *the part of a day when it is light.* \n", "\n", "I originally assumed sense 2, but I wanted to accept sense 1 for what [bikepackers](https://bikepacking.com/) call a [sub-24-hour overnight](https://oneofsevenproject.com/s24o-bikepacking-guide/) (S24O): a ride to a camping site in the afternoon, pitching a tent for the night, and returning back home the next morning. And then COVID struck, the camping sites closed, so why not allow an S24O where I sleep in my own home? I realize Eddington had a lot more hardships than we have (World War I, the 1918 pandemic, and World War II, for example), but I hope he would approve of this modest accomodation on my part." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hill-Index: Speed versus Grade on Short Climbs\n", "\n", "The Eddington number reminds me of the [**h-index**](https://en.wikipedia.org/wiki/H-index) metric for scientific publications. I invented another metric:\n", "\n", "> *Your **hill-index** is the maximum integer **h** where you can regularly climb an **h** percent grade at **h** miles per hour.*\n", "\n", "I'll plot grade versus speed for segments (not rides) with two best-fit curves: a blue quadratic and an orange cubic. I'll also superimpose a red dotted line where grade = speed." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('pct', 'mph', segments[segments.pct > 2], \n", " 'Miles per hour versus segment grade in percent')\n", "plt.plot((2, 6, 7), (2, 6, 7), 'ro:');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both best-fit curves are above the red circle at 6% and below the red circle for 7%, so **my hill-index is 6**. We also see that I can cruise at 14 mph on a 2% grade, but only about 7 mph at 6% grade, and around 5.5 mph on 8% grades." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " # Speed versus Grade on Long Rides\n", "\n", "The plot above tell me how fast I should expect to climb a particular hill, but what about average time on longer rides? Here's a plot of my speed versus steepness (measured in feet climbed per mile rather than in percent)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('fpmi', 'mph', rides, 'Speed (miles per hour) versus Ride Grade (feet per mile)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, I average a little under 14 mph when the overall route is fairly flat, with a lot of variability, depending more on my level of effort (and maybe the wind) than on the grade of the road. But when the grade is steeper than 50 ft/mile, my speed falls off quickly: down to 12mph at 80 ft/mile; 11 mph at 100 ft/mile; and around 10 mph at 120 ft/mile. Note that 120 ft/mile is only 2.3% grade, but if you figure a typical route is 1/3 up, 1/3 down, and 1/3 flat, then that's 7% average grade on the up part.\n", "\n", "I can use this to predict the time of a ride. For example, if I'm in La Honda and want to get to Pescadero, which way is faster: the [coast route](https://www.google.com/maps/dir/La+Honda,+California/Pescadero,+California/@37.2905834,-122.3896683,12z/data=!4m19!4m18!1m10!1m1!1s0x808faed4dc6265bd:0x51a109d3306a7219!2m2!1d-122.274227!2d37.3190255!3m4!1m2!1d-122.4039496!2d37.3116594!3s0x808f062b7d7585e7:0x942480c22f110b74!1m5!1m1!1s0x808f00b4b613c4c1:0x43c609077878b77!2m2!1d-122.3830152!2d37.2551636!3e1) (15.7 miles, 361 ft climb), or the [creek route](https://www.google.com/maps/dir/La+Honda,+California/Pescadero,+California/@37.2905834,-122.3896683,12z/data=!4m19!4m18!1m10!1m1!1s0x808faed4dc6265bd:0x51a109d3306a7219!2m2!1d-122.274227!2d37.3190255!3m4!1m2!1d-122.3658887!2d37.2538867!3s0x808f00acf265bd43:0xb7e2a0c9ee355c3a!1m5!1m1!1s0x808f00b4b613c4c1:0x43c609077878b77!2m2!1d-122.3830152!2d37.2551636!3e1) (13.5 miles, 853 ft climb)? We can estimate:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Coast: 70 min, Creek: 64 min.'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f'Coast: {estimate(15.7, 361)} min, Creek: {estimate(13.5, 853)} min.'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This predicts the shorter but steeper creek route would be about 6 minutes faster (whereas Google Maps predicts the creek route would be 80 minutes, 2 more than the coast route—I guess Google lacks confidence in my climbing ability). This is all good to know, but other factors (like the scenery and whether I want to stop at the San Gregorio store) are probably more important in making the choice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# VAM\n", "\n", "Climbing speed is measured by [VAM](https://en.wikipedia.org/wiki/VAM_%28bicycling%29), which stands for *velocità ascensionale media* (for native Campagnolo speakers) or *vertical ascent in meters per hour* (for SRAM) or 平均上昇率 (for Shimano), or *Vm/h* (for physicists). The theory is that for fairly steep climbs, most of your power is going into lifting against gravity, so your VAM should be about constant no matter what the grade. (For flatish segments power is spent on wind and rolling resistance, and for the very steepest of climbs, in my experience, power goes largely to cursing *sotto voce*, as they say in Italian.) \n", "\n", "Here's a plot of my VAM versus grade over short segments:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show('pct', 'vam', segments, 'VAM (vertical meters per hour) versus segment grade in percent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Champion cyclists can do over 1800 meters/hour over a 10 km climb, and can sustain [1400 meters/hour for 7 hours](https://www.strava.com/activities/4996833865). My VAM numbers range mostly from 400 to 800 meters/hour, and I can sustain the higher numbers for only a couple of minutes:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlehoursmilesfeetmphvamfpmipctkmsmeters
Camaritas climb0.010.104810.001463.0480.09.090.1615.0
Paloma Climb0.020.14827.001250.0586.011.090.2325.0
Klamath Dr.0.020.12776.001173.0642.012.150.1923.0
Entrance Way Hill Repeats0.020.10765.001158.0760.014.390.1623.0
Davenport Kicker0.020.247412.001128.0308.05.840.3923.0
Valparaiso steep0.040.181454.501105.0806.015.260.2944.0
Invernes to Firecrest Climb0.040.281437.001090.0511.09.670.4544.0
Kings Mountain final sprint0.040.311357.751029.0435.08.250.5041.0
Limantour Spit0.090.473035.221026.0645.012.210.7692.0
Tunitas flattens0.050.421668.401012.0395.07.490.6851.0
Tunitas flattens0.050.421668.401012.0395.07.490.6851.0
Cemetery Sprint0.010.08338.001006.0412.07.810.1310.0
Skyline Bump at OLH0.020.216310.50960.0300.05.680.3419.0
Laning Bump0.030.24948.00955.0392.07.420.3929.0
Faught Turn0.020.226011.00914.0273.05.170.3518.0
Westridge 3min0.080.372404.62914.0649.012.290.6073.0
Valparaiso steep0.050.181453.60884.0806.015.260.2944.0
Sharon Park steep part0.030.21867.00874.0410.07.760.3426.0
Old La Honda Mile 10.130.993707.62868.0374.07.081.59113.0
Joaquin0.090.332543.67860.0770.014.580.5377.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " Camaritas climb 0.01 0.10 48 10.00 1463.0 480.0 \n", " Paloma Climb 0.02 0.14 82 7.00 1250.0 586.0 \n", " Klamath Dr. 0.02 0.12 77 6.00 1173.0 642.0 \n", " Entrance Way Hill Repeats 0.02 0.10 76 5.00 1158.0 760.0 \n", " Davenport Kicker 0.02 0.24 74 12.00 1128.0 308.0 \n", " Valparaiso steep 0.04 0.18 145 4.50 1105.0 806.0 \n", " Invernes to Firecrest Climb 0.04 0.28 143 7.00 1090.0 511.0 \n", " Kings Mountain final sprint 0.04 0.31 135 7.75 1029.0 435.0 \n", " Limantour Spit 0.09 0.47 303 5.22 1026.0 645.0 \n", " Tunitas flattens 0.05 0.42 166 8.40 1012.0 395.0 \n", " Tunitas flattens 0.05 0.42 166 8.40 1012.0 395.0 \n", " Cemetery Sprint 0.01 0.08 33 8.00 1006.0 412.0 \n", " Skyline Bump at OLH 0.02 0.21 63 10.50 960.0 300.0 \n", " Laning Bump 0.03 0.24 94 8.00 955.0 392.0 \n", " Faught Turn 0.02 0.22 60 11.00 914.0 273.0 \n", " Westridge 3min 0.08 0.37 240 4.62 914.0 649.0 \n", " Valparaiso steep 0.05 0.18 145 3.60 884.0 806.0 \n", " Sharon Park steep part 0.03 0.21 86 7.00 874.0 410.0 \n", " Old La Honda Mile 1 0.13 0.99 370 7.62 868.0 374.0 \n", " Joaquin 0.09 0.33 254 3.67 860.0 770.0 \n", "\n", " pct kms meters \n", " 9.09 0.16 15.0 \n", " 11.09 0.23 25.0 \n", " 12.15 0.19 23.0 \n", " 14.39 0.16 23.0 \n", " 5.84 0.39 23.0 \n", " 15.26 0.29 44.0 \n", " 9.67 0.45 44.0 \n", " 8.25 0.50 41.0 \n", " 12.21 0.76 92.0 \n", " 7.49 0.68 51.0 \n", " 7.49 0.68 51.0 \n", " 7.81 0.13 10.0 \n", " 5.68 0.34 19.0 \n", " 7.42 0.39 29.0 \n", " 5.17 0.35 18.0 \n", " 12.29 0.60 73.0 \n", " 15.26 0.29 44.0 \n", " 7.76 0.34 26.0 \n", " 7.08 1.59 113.0 \n", " 14.58 0.53 77.0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'vam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On segments that are at least a kilometer long my VAM tops out at about 800 meters/hour:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlehoursmilesfeetmphvamfpmipctkmsmeters
Old La Honda Mile 10.130.993707.62868.0374.07.081.59113.0
Westridge0.140.683854.86838.0566.010.721.09117.0
Old La Honda (Bridge to Stop)0.483.3312556.94797.0377.07.145.36383.0
Old La Honda (Bridge to Stop)0.513.3312556.53750.0377.07.145.36383.0
Westridge0.160.683854.25733.0566.010.721.09117.0
Tunitas steep0.251.205994.80730.0499.09.451.93183.0
Old La Honda Mile 10.160.993706.19705.0374.07.081.59113.0
Woodside Climb0.131.7129513.15692.0173.03.272.7590.0
Huddart0.170.923855.41690.0418.07.931.48117.0
Top of Groton Rd heading west0.130.922917.08682.0316.05.991.4889.0
Watts (Sonoma)0.141.203138.57681.0261.04.941.9395.0
Tunitas steep0.271.205994.44676.0499.09.451.93183.0
Canon to No Cycling0.090.751988.33671.0264.05.001.2160.0
Canon to No Cycling0.090.751988.33671.0264.05.001.2160.0
Coe Second Switchback to flat0.221.004834.55669.0483.09.151.61147.0
Lower Redwood Gulch0.221.034744.68657.0460.08.721.66144.0
Lobitas Creek0.200.964304.80655.0448.08.481.54131.0
West Alpine switchback0.150.783225.20654.0413.07.821.2698.0
Kaboom Portola Rd0.050.6710213.40622.0152.02.881.0831.0
Huddart0.190.923854.84618.0418.07.931.48117.0
Kings Greer to Skyline0.783.9215365.03600.0392.07.426.31468.0
Woodside Climb0.151.7129511.40599.0173.03.272.7590.0
Stage Rd0.191.013735.32598.0369.06.991.63114.0
Try not to fall back0.210.714103.38595.0577.010.941.14125.0
Sand Gill Sharon-top0.070.8513612.14592.0160.03.031.3741.0
Sand Gill Sharon-top0.070.8513612.14592.0160.03.031.3741.0
Mt Eden climb0.141.022727.29592.0267.05.051.6483.0
Tunitas lower climb0.221.304215.91583.0324.06.132.09128.0
Kings Greer to Skyline0.813.9215364.84578.0392.07.426.31468.0
Haskins0.301.515665.03575.0375.07.102.43173.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " Old La Honda Mile 1 0.13 0.99 370 7.62 868.0 374.0 \n", " Westridge 0.14 0.68 385 4.86 838.0 566.0 \n", " Old La Honda (Bridge to Stop) 0.48 3.33 1255 6.94 797.0 377.0 \n", " Old La Honda (Bridge to Stop) 0.51 3.33 1255 6.53 750.0 377.0 \n", " Westridge 0.16 0.68 385 4.25 733.0 566.0 \n", " Tunitas steep 0.25 1.20 599 4.80 730.0 499.0 \n", " Old La Honda Mile 1 0.16 0.99 370 6.19 705.0 374.0 \n", " Woodside Climb 0.13 1.71 295 13.15 692.0 173.0 \n", " Huddart 0.17 0.92 385 5.41 690.0 418.0 \n", " Top of Groton Rd heading west 0.13 0.92 291 7.08 682.0 316.0 \n", " Watts (Sonoma) 0.14 1.20 313 8.57 681.0 261.0 \n", " Tunitas steep 0.27 1.20 599 4.44 676.0 499.0 \n", " Canon to No Cycling 0.09 0.75 198 8.33 671.0 264.0 \n", " Canon to No Cycling 0.09 0.75 198 8.33 671.0 264.0 \n", " Coe Second Switchback to flat 0.22 1.00 483 4.55 669.0 483.0 \n", " Lower Redwood Gulch 0.22 1.03 474 4.68 657.0 460.0 \n", " Lobitas Creek 0.20 0.96 430 4.80 655.0 448.0 \n", " West Alpine switchback 0.15 0.78 322 5.20 654.0 413.0 \n", " Kaboom Portola Rd 0.05 0.67 102 13.40 622.0 152.0 \n", " Huddart 0.19 0.92 385 4.84 618.0 418.0 \n", " Kings Greer to Skyline 0.78 3.92 1536 5.03 600.0 392.0 \n", " Woodside Climb 0.15 1.71 295 11.40 599.0 173.0 \n", " Stage Rd 0.19 1.01 373 5.32 598.0 369.0 \n", " Try not to fall back 0.21 0.71 410 3.38 595.0 577.0 \n", " Sand Gill Sharon-top 0.07 0.85 136 12.14 592.0 160.0 \n", " Sand Gill Sharon-top 0.07 0.85 136 12.14 592.0 160.0 \n", " Mt Eden climb 0.14 1.02 272 7.29 592.0 267.0 \n", " Tunitas lower climb 0.22 1.30 421 5.91 583.0 324.0 \n", " Kings Greer to Skyline 0.81 3.92 1536 4.84 578.0 392.0 \n", " Haskins 0.30 1.51 566 5.03 575.0 375.0 \n", "\n", " pct kms meters \n", " 7.08 1.59 113.0 \n", " 10.72 1.09 117.0 \n", " 7.14 5.36 383.0 \n", " 7.14 5.36 383.0 \n", " 10.72 1.09 117.0 \n", " 9.45 1.93 183.0 \n", " 7.08 1.59 113.0 \n", " 3.27 2.75 90.0 \n", " 7.93 1.48 117.0 \n", " 5.99 1.48 89.0 \n", " 4.94 1.93 95.0 \n", " 9.45 1.93 183.0 \n", " 5.00 1.21 60.0 \n", " 5.00 1.21 60.0 \n", " 9.15 1.61 147.0 \n", " 8.72 1.66 144.0 \n", " 8.48 1.54 131.0 \n", " 7.82 1.26 98.0 \n", " 2.88 1.08 31.0 \n", " 7.93 1.48 117.0 \n", " 7.42 6.31 468.0 \n", " 3.27 2.75 90.0 \n", " 6.99 1.63 114.0 \n", " 10.94 1.14 125.0 \n", " 3.03 1.37 41.0 \n", " 3.03 1.37 41.0 \n", " 5.05 1.64 83.0 \n", " 6.13 2.09 128.0 \n", " 7.42 6.31 468.0 \n", " 7.10 2.43 173.0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments[segments.kms >= 1], 'vam', n=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can also look at VAM numbers for complete rides. I would expect the ride VAM to be half the segment VAM (or less) since most of my rides are circuits where I return to the start, and thus no more than half the ride is climbing. Sure enough, the best I can do is about 400 meters/hour:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Sun, 11/29/20152015Mt. Hamilton3.6837.00490210.05406.0132.02.5159.531494.0
Fri, 4/2/20212021Everesting 5: climb 2×(OLH + WOLH)3.2731.4843449.63405.0138.02.6150.651324.0
Mon, 3/29/20212021Everesting 1: Mt Diablo2.6022.2234068.55399.0153.02.9035.751038.0
Tue, 3/30/20212021Everesting 2: Kings + WOLH + OLH3.3435.99437710.78399.0122.02.3057.911334.0
Sun, 12/1/20132013Mt. Hamilton3.7837.5649219.94397.0131.02.4860.431500.0
Sat, 11/25/20172017Mt. Hamilton3.6936.6548069.93397.0131.02.4858.971465.0
Fri, 10/30/20152015OLH / West Alpine3.4839.51450511.35395.0114.02.1663.571373.0
Sat, 4/26/20142014OLH / Tunitas Creek5.2658.69674211.16391.0115.02.1894.432055.0
Sat, 4/18/20152015Tunitas + Lobitos Creeks5.2461.27661111.69385.0108.02.0498.582015.0
Wed, 10/14/20152015Half Moon Bay6.1372.97764411.90380.0105.01.98117.412330.0
Sun, 6/4/20172017Sequoia Challenge6.2966.52752010.58364.0113.02.14107.032292.0
Sat, 7/25/20152015Palo Alto, California4.0443.62481910.80364.0110.02.0970.181469.0
Sat, 10/11/20142014OLH / Tunitas5.0958.29604411.45362.0104.01.9693.791842.0
Sat, 8/13/20162016Petaluma / Point Reyes4.5054.75528612.17358.097.01.8388.091611.0
Fri, 8/28/20152015Pescadaro via OLH5.3166.01613712.43352.093.01.76106.211871.0
Sun, 4/4/20212021Everesting 7: Mill Creek / Morrison Canyon3.0829.3835179.54348.0120.02.2747.271072.0
Sat, 2/10/20242024Seacliff, etc.4.7263.41536513.43346.085.01.60102.031635.0
Wed, 6/18/20142014Sierra to the Sea Day 44.9657.64556111.62342.096.01.8392.741695.0
Sun, 6/3/20182018The Sequoia5.9764.92667710.87341.0103.01.95104.462035.0
Sat, 5/9/20152015OLH2.5032.33278812.93340.086.01.6352.02850.0
\n", "
" ], "text/plain": [ " date year title hours \\\n", " Sun, 11/29/2015 2015 Mt. Hamilton 3.68 \n", " Fri, 4/2/2021 2021 Everesting 5: climb 2×(OLH + WOLH) 3.27 \n", " Mon, 3/29/2021 2021 Everesting 1: Mt Diablo 2.60 \n", " Tue, 3/30/2021 2021 Everesting 2: Kings + WOLH + OLH 3.34 \n", " Sun, 12/1/2013 2013 Mt. Hamilton 3.78 \n", " Sat, 11/25/2017 2017 Mt. Hamilton 3.69 \n", " Fri, 10/30/2015 2015 OLH / West Alpine 3.48 \n", " Sat, 4/26/2014 2014 OLH / Tunitas Creek 5.26 \n", " Sat, 4/18/2015 2015 Tunitas + Lobitos Creeks 5.24 \n", " Wed, 10/14/2015 2015 Half Moon Bay 6.13 \n", " Sun, 6/4/2017 2017 Sequoia Challenge 6.29 \n", " Sat, 7/25/2015 2015 Palo Alto, California 4.04 \n", " Sat, 10/11/2014 2014 OLH / Tunitas 5.09 \n", " Sat, 8/13/2016 2016 Petaluma / Point Reyes 4.50 \n", " Fri, 8/28/2015 2015 Pescadaro via OLH 5.31 \n", " Sun, 4/4/2021 2021 Everesting 7: Mill Creek / Morrison Canyon 3.08 \n", " Sat, 2/10/2024 2024 Seacliff, etc. 4.72 \n", " Wed, 6/18/2014 2014 Sierra to the Sea Day 4 4.96 \n", " Sun, 6/3/2018 2018 The Sequoia 5.97 \n", " Sat, 5/9/2015 2015 OLH 2.50 \n", "\n", " miles feet mph vam fpmi pct kms meters \n", " 37.00 4902 10.05 406.0 132.0 2.51 59.53 1494.0 \n", " 31.48 4344 9.63 405.0 138.0 2.61 50.65 1324.0 \n", " 22.22 3406 8.55 399.0 153.0 2.90 35.75 1038.0 \n", " 35.99 4377 10.78 399.0 122.0 2.30 57.91 1334.0 \n", " 37.56 4921 9.94 397.0 131.0 2.48 60.43 1500.0 \n", " 36.65 4806 9.93 397.0 131.0 2.48 58.97 1465.0 \n", " 39.51 4505 11.35 395.0 114.0 2.16 63.57 1373.0 \n", " 58.69 6742 11.16 391.0 115.0 2.18 94.43 2055.0 \n", " 61.27 6611 11.69 385.0 108.0 2.04 98.58 2015.0 \n", " 72.97 7644 11.90 380.0 105.0 1.98 117.41 2330.0 \n", " 66.52 7520 10.58 364.0 113.0 2.14 107.03 2292.0 \n", " 43.62 4819 10.80 364.0 110.0 2.09 70.18 1469.0 \n", " 58.29 6044 11.45 362.0 104.0 1.96 93.79 1842.0 \n", " 54.75 5286 12.17 358.0 97.0 1.83 88.09 1611.0 \n", " 66.01 6137 12.43 352.0 93.0 1.76 106.21 1871.0 \n", " 29.38 3517 9.54 348.0 120.0 2.27 47.27 1072.0 \n", " 63.41 5365 13.43 346.0 85.0 1.60 102.03 1635.0 \n", " 57.64 5561 11.62 342.0 96.0 1.83 92.74 1695.0 \n", " 64.92 6677 10.87 341.0 103.0 1.95 104.46 2035.0 \n", " 32.33 2788 12.93 340.0 86.0 1.63 52.02 850.0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'vam') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the Data\n", "\n", "\n", "Some more ways to look at the data, both rides and segments." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearhoursmilesfeetmphvamfpmipctkmsmeters
count552.000000552.000000552.000000552.000000552.000000552.000000552.000000552.000000552.000000552.000000
mean2017.1250003.41405843.6533331846.40036212.986413157.52536241.4692030.78514570.238152562.789855
std2.7099251.49103217.9766261511.7806171.33001989.67662927.1479600.51403228.924464460.785323
min2012.0000001.54000020.96000068.0000008.55000010.0000003.0000000.05000033.72000021.000000
25%2015.0000002.22750029.210000744.00000012.16000081.00000020.0000000.37000047.000000226.750000
50%2017.0000002.89500037.1600001378.00000013.145000152.00000036.0000000.69000059.795000420.000000
75%2018.0000004.50250058.8850002369.25000013.792500217.25000055.2500001.05500094.750000722.250000
max2024.0000008.140000102.4100007644.00000016.750000406.000000153.0000002.900000164.7800002330.000000
\n", "
" ], "text/plain": [ " year hours miles feet mph \\\n", "count 552.000000 552.000000 552.000000 552.000000 552.000000 \n", "mean 2017.125000 3.414058 43.653333 1846.400362 12.986413 \n", "std 2.709925 1.491032 17.976626 1511.780617 1.330019 \n", "min 2012.000000 1.540000 20.960000 68.000000 8.550000 \n", "25% 2015.000000 2.227500 29.210000 744.000000 12.160000 \n", "50% 2017.000000 2.895000 37.160000 1378.000000 13.145000 \n", "75% 2018.000000 4.502500 58.885000 2369.250000 13.792500 \n", "max 2024.000000 8.140000 102.410000 7644.000000 16.750000 \n", "\n", " vam fpmi pct kms meters \n", "count 552.000000 552.000000 552.000000 552.000000 552.000000 \n", "mean 157.525362 41.469203 0.785145 70.238152 562.789855 \n", "std 89.676629 27.147960 0.514032 28.924464 460.785323 \n", "min 10.000000 3.000000 0.050000 33.720000 21.000000 \n", "25% 81.000000 20.000000 0.370000 47.000000 226.750000 \n", "50% 152.000000 36.000000 0.690000 59.795000 420.000000 \n", "75% 217.250000 55.250000 1.055000 94.750000 722.250000 \n", "max 406.000000 153.000000 2.900000 164.780000 2330.000000 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rides.describe() # Summary statistics for the rides" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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hoursmilesfeetmphvamfpmipctkmsmeters
count141.000000141.000000141.000000141.000000141.000000141.000000141.000000141.000000141.000000
mean0.1417020.932979268.9787237.444539641.893617353.5035466.6948941.50106481.978723
std0.1737110.989832295.0606593.557336211.923595186.8163203.5379741.59226789.957408
min0.0100000.08000021.0000002.120000111.00000018.0000000.3500000.1300006.000000
25%0.0500000.330000104.0000004.830000503.000000219.0000004.1400000.53000032.000000
50%0.0900000.600000166.0000006.190000630.000000333.0000006.3000000.97000051.000000
75%0.1500001.190000303.0000009.800000724.000000462.0000008.7600001.91000092.000000
max1.3900007.3800001887.00000019.7900001463.000000839.00000015.89000011.870000575.000000
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" ], "text/plain": [ " hours miles feet mph vam \\\n", "count 141.000000 141.000000 141.000000 141.000000 141.000000 \n", "mean 0.141702 0.932979 268.978723 7.444539 641.893617 \n", "std 0.173711 0.989832 295.060659 3.557336 211.923595 \n", "min 0.010000 0.080000 21.000000 2.120000 111.000000 \n", "25% 0.050000 0.330000 104.000000 4.830000 503.000000 \n", "50% 0.090000 0.600000 166.000000 6.190000 630.000000 \n", "75% 0.150000 1.190000 303.000000 9.800000 724.000000 \n", "max 1.390000 7.380000 1887.000000 19.790000 1463.000000 \n", "\n", " fpmi pct kms meters \n", "count 141.000000 141.000000 141.000000 141.000000 \n", "mean 353.503546 6.694894 1.501064 81.978723 \n", "std 186.816320 3.537974 1.592267 89.957408 \n", "min 18.000000 0.350000 0.130000 6.000000 \n", "25% 219.000000 4.140000 0.530000 32.000000 \n", "50% 333.000000 6.300000 0.970000 51.000000 \n", "75% 462.000000 8.760000 1.910000 92.000000 \n", "max 839.000000 15.890000 11.870000 575.000000 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "segments.describe() # Summary statistics for the segments" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Sun, 5/22/20162016Canada2.1936.68133216.75185.036.00.6959.02406.0
Wed, 9/13/20172017Healdburg / Jimtown2.1334.4591216.17131.026.00.5055.43278.0
Sun, 8/25/20242024Petaluma–Santa Rosa + Napa5.2284.26296616.14173.035.00.67135.57904.0
Sat, 1/25/20142014Woodside1.5625.08124316.08243.050.00.9440.35379.0
Sat, 4/11/20152015Woodside1.5424.73103516.06205.042.00.7939.79315.0
Mon, 5/27/20242024Saratoga4.8377.25174915.99110.023.00.43124.30533.0
Sun, 7/11/20212021San Jose4.1065.10108615.8881.017.00.32104.75331.0
Sun, 1/18/20152015Woodside1.6426.02125715.87234.048.00.9141.87383.0
Fri, 6/24/20162016Foothill Expway1.5925.1162315.79119.025.00.4740.40190.0
Sun, 1/26/20142014Canada Rd2.1033.12144615.77210.044.00.8353.29441.0
Fri, 1/6/20122012Omarama to Wanaka New Zealand4.4870.35326215.70222.046.00.88113.19994.0
Sun, 4/12/20152015Palo Alto Cycling2.0331.76121015.65182.038.00.7251.10369.0
Sun, 10/15/20172017Los Gatos2.8644.71143715.63153.032.00.6171.94438.0
Sun, 8/5/20182018Bike Ride Northwest Day 13.5855.77182415.58155.033.00.6289.73556.0
Sun, 2/28/20162016Woodside Loop1.7326.9384315.57149.031.00.5943.33257.0
Sun, 6/26/20162016Los Gatos3.2850.78118115.48110.023.00.4481.71360.0
Mon, 1/19/20152015Canada Rd, etc.2.9545.64183615.47190.040.00.7673.43560.0
Sun, 1/19/20142014Palo Alto, CA1.6225.01120115.44226.048.00.9140.24366.0
Sun, 12/6/20152015Canada Rd2.2534.67123715.41168.036.00.6855.78377.0
Tue, 6/18/20132013work etc (headwinds)2.0631.4880915.28120.026.00.4950.65247.0
\n", "
" ], "text/plain": [ " date year title hours miles feet \\\n", " Sun, 5/22/2016 2016 Canada 2.19 36.68 1332 \n", " Wed, 9/13/2017 2017 Healdburg / Jimtown 2.13 34.45 912 \n", " Sun, 8/25/2024 2024 Petaluma–Santa Rosa + Napa 5.22 84.26 2966 \n", " Sat, 1/25/2014 2014 Woodside 1.56 25.08 1243 \n", " Sat, 4/11/2015 2015 Woodside 1.54 24.73 1035 \n", " Mon, 5/27/2024 2024 Saratoga 4.83 77.25 1749 \n", " Sun, 7/11/2021 2021 San Jose 4.10 65.10 1086 \n", " Sun, 1/18/2015 2015 Woodside 1.64 26.02 1257 \n", " Fri, 6/24/2016 2016 Foothill Expway 1.59 25.11 623 \n", " Sun, 1/26/2014 2014 Canada Rd 2.10 33.12 1446 \n", " Fri, 1/6/2012 2012 Omarama to Wanaka New Zealand 4.48 70.35 3262 \n", " Sun, 4/12/2015 2015 Palo Alto Cycling 2.03 31.76 1210 \n", " Sun, 10/15/2017 2017 Los Gatos 2.86 44.71 1437 \n", " Sun, 8/5/2018 2018 Bike Ride Northwest Day 1 3.58 55.77 1824 \n", " Sun, 2/28/2016 2016 Woodside Loop 1.73 26.93 843 \n", " Sun, 6/26/2016 2016 Los Gatos 3.28 50.78 1181 \n", " Mon, 1/19/2015 2015 Canada Rd, etc. 2.95 45.64 1836 \n", " Sun, 1/19/2014 2014 Palo Alto, CA 1.62 25.01 1201 \n", " Sun, 12/6/2015 2015 Canada Rd 2.25 34.67 1237 \n", " Tue, 6/18/2013 2013 work etc (headwinds) 2.06 31.48 809 \n", "\n", " mph vam fpmi pct kms meters \n", " 16.75 185.0 36.0 0.69 59.02 406.0 \n", " 16.17 131.0 26.0 0.50 55.43 278.0 \n", " 16.14 173.0 35.0 0.67 135.57 904.0 \n", " 16.08 243.0 50.0 0.94 40.35 379.0 \n", " 16.06 205.0 42.0 0.79 39.79 315.0 \n", " 15.99 110.0 23.0 0.43 124.30 533.0 \n", " 15.88 81.0 17.0 0.32 104.75 331.0 \n", " 15.87 234.0 48.0 0.91 41.87 383.0 \n", " 15.79 119.0 25.0 0.47 40.40 190.0 \n", " 15.77 210.0 44.0 0.83 53.29 441.0 \n", " 15.70 222.0 46.0 0.88 113.19 994.0 \n", " 15.65 182.0 38.0 0.72 51.10 369.0 \n", " 15.63 153.0 32.0 0.61 71.94 438.0 \n", " 15.58 155.0 33.0 0.62 89.73 556.0 \n", " 15.57 149.0 31.0 0.59 43.33 257.0 \n", " 15.48 110.0 23.0 0.44 81.71 360.0 \n", " 15.47 190.0 40.0 0.76 73.43 560.0 \n", " 15.44 226.0 48.0 0.91 40.24 366.0 \n", " 15.41 168.0 36.0 0.68 55.78 377.0 \n", " 15.28 120.0 26.0 0.49 50.65 247.0 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'mph') # Fastest rides (of more than 20 miles, that I sampled into database)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlehoursmilesfeetmphvamfpmipctkmsmeters
PCH Pescadero to Bean Hollow0.142.775119.79111.018.00.354.4616.0
Highway 1 Cascanoa to Cascade0.091.618917.89301.055.01.052.5927.0
Vickrey Fruitvale0.060.996816.50345.069.01.301.5921.0
Highway 9 Mantalvo0.030.453515.00356.078.01.470.7211.0
Highway 9 Mantalvo0.030.453515.00356.078.01.470.7211.0
The Boneyard0.101.4813514.80411.091.01.732.3841.0
Vickrey Fruitvale0.070.996814.14296.069.01.301.5921.0
Sand Hill Alpine to 2800.121.6718013.92457.0108.02.042.6955.0
Canada to College0.101.3711913.70363.087.01.652.2036.0
Foothill Homestead0.091.2212613.56427.0103.01.961.9638.0
The Boneyard0.111.4813513.45374.091.01.732.3841.0
Kaboom Portola Rd0.050.6710213.40622.0152.02.881.0831.0
Woodside Climb0.131.7129513.15692.0173.03.272.7590.0
Sand Hill Alpine to 2800.131.6718012.85422.0108.02.042.6955.0
Alpine Westridge0.060.769912.67503.0130.02.471.2230.0
Alpine Westridge0.060.769912.67503.0130.02.471.2230.0
Stanford Ave0.050.638512.60518.0135.02.561.0126.0
Canada to College0.111.3711912.45330.087.01.652.2036.0
Sand Hill 280 to horse0.040.499512.25724.0194.03.670.7929.0
Stevens Country Park0.101.2211212.20341.092.01.741.9634.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi \\\n", " PCH Pescadero to Bean Hollow 0.14 2.77 51 19.79 111.0 18.0 \n", " Highway 1 Cascanoa to Cascade 0.09 1.61 89 17.89 301.0 55.0 \n", " Vickrey Fruitvale 0.06 0.99 68 16.50 345.0 69.0 \n", " Highway 9 Mantalvo 0.03 0.45 35 15.00 356.0 78.0 \n", " Highway 9 Mantalvo 0.03 0.45 35 15.00 356.0 78.0 \n", " The Boneyard 0.10 1.48 135 14.80 411.0 91.0 \n", " Vickrey Fruitvale 0.07 0.99 68 14.14 296.0 69.0 \n", " Sand Hill Alpine to 280 0.12 1.67 180 13.92 457.0 108.0 \n", " Canada to College 0.10 1.37 119 13.70 363.0 87.0 \n", " Foothill Homestead 0.09 1.22 126 13.56 427.0 103.0 \n", " The Boneyard 0.11 1.48 135 13.45 374.0 91.0 \n", " Kaboom Portola Rd 0.05 0.67 102 13.40 622.0 152.0 \n", " Woodside Climb 0.13 1.71 295 13.15 692.0 173.0 \n", " Sand Hill Alpine to 280 0.13 1.67 180 12.85 422.0 108.0 \n", " Alpine Westridge 0.06 0.76 99 12.67 503.0 130.0 \n", " Alpine Westridge 0.06 0.76 99 12.67 503.0 130.0 \n", " Stanford Ave 0.05 0.63 85 12.60 518.0 135.0 \n", " Canada to College 0.11 1.37 119 12.45 330.0 87.0 \n", " Sand Hill 280 to horse 0.04 0.49 95 12.25 724.0 194.0 \n", " Stevens Country Park 0.10 1.22 112 12.20 341.0 92.0 \n", "\n", " pct kms meters \n", " 0.35 4.46 16.0 \n", " 1.05 2.59 27.0 \n", " 1.30 1.59 21.0 \n", " 1.47 0.72 11.0 \n", " 1.47 0.72 11.0 \n", " 1.73 2.38 41.0 \n", " 1.30 1.59 21.0 \n", " 2.04 2.69 55.0 \n", " 1.65 2.20 36.0 \n", " 1.96 1.96 38.0 \n", " 1.73 2.38 41.0 \n", " 2.88 1.08 31.0 \n", " 3.27 2.75 90.0 \n", " 2.04 2.69 55.0 \n", " 2.47 1.22 30.0 \n", " 2.47 1.22 30.0 \n", " 2.56 1.01 26.0 \n", " 1.65 2.20 36.0 \n", " 3.67 0.79 29.0 \n", " 1.74 1.96 34.0 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'mph') # Fastest segments (there are no descent segments in the database)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlehoursmilesfeetmphvamfpmipctkmsmeters
West Alpine full1.397.3818875.31414.0256.04.8411.87575.0
Kings Greer to Skyline0.783.9215365.03600.0392.07.426.31468.0
Kings Greer to Skyline0.813.9215364.84578.0392.07.426.31468.0
Old La Honda (Bridge to Stop)0.483.3312556.94797.0377.07.145.36383.0
Old La Honda (Bridge to Stop)0.513.3312556.53750.0377.07.145.36383.0
Alma Mountain Charlie0.533.128755.89503.0280.05.315.02267.0
Kings half way0.462.898206.28543.0284.05.374.65250.0
Kings half way0.502.898205.78500.0284.05.374.65250.0
Alpine Portola to top Joaquin0.573.528016.18428.0228.04.315.66244.0
Alpine Portola to top Joaquin0.583.528016.07421.0228.04.315.66244.0
Tunitas steep0.271.205994.44676.0499.09.451.93183.0
Tunitas steep0.251.205994.80730.0499.09.451.93183.0
Haskins0.301.515665.03575.0375.07.102.43173.0
Haskins0.311.515664.87557.0375.07.102.43173.0
Coe Second Switchback to flat0.221.004834.55669.0483.09.151.61147.0
Lower Redwood Gulch0.221.034744.68657.0460.08.721.66144.0
Alpine Willowbrook to Joaquin0.292.274617.83485.0203.03.853.65141.0
Alpine Willowbrook to Joaquin0.282.274618.11502.0203.03.853.65141.0
Lobitas Creek0.200.964304.80655.0448.08.481.54131.0
Tunitas lower climb0.221.304215.91583.0324.06.132.09128.0
\n", "
" ], "text/plain": [ " title hours miles feet mph vam fpmi pct \\\n", " West Alpine full 1.39 7.38 1887 5.31 414.0 256.0 4.84 \n", " Kings Greer to Skyline 0.78 3.92 1536 5.03 600.0 392.0 7.42 \n", " Kings Greer to Skyline 0.81 3.92 1536 4.84 578.0 392.0 7.42 \n", " Old La Honda (Bridge to Stop) 0.48 3.33 1255 6.94 797.0 377.0 7.14 \n", " Old La Honda (Bridge to Stop) 0.51 3.33 1255 6.53 750.0 377.0 7.14 \n", " Alma Mountain Charlie 0.53 3.12 875 5.89 503.0 280.0 5.31 \n", " Kings half way 0.46 2.89 820 6.28 543.0 284.0 5.37 \n", " Kings half way 0.50 2.89 820 5.78 500.0 284.0 5.37 \n", " Alpine Portola to top Joaquin 0.57 3.52 801 6.18 428.0 228.0 4.31 \n", " Alpine Portola to top Joaquin 0.58 3.52 801 6.07 421.0 228.0 4.31 \n", " Tunitas steep 0.27 1.20 599 4.44 676.0 499.0 9.45 \n", " Tunitas steep 0.25 1.20 599 4.80 730.0 499.0 9.45 \n", " Haskins 0.30 1.51 566 5.03 575.0 375.0 7.10 \n", " Haskins 0.31 1.51 566 4.87 557.0 375.0 7.10 \n", " Coe Second Switchback to flat 0.22 1.00 483 4.55 669.0 483.0 9.15 \n", " Lower Redwood Gulch 0.22 1.03 474 4.68 657.0 460.0 8.72 \n", " Alpine Willowbrook to Joaquin 0.29 2.27 461 7.83 485.0 203.0 3.85 \n", " Alpine Willowbrook to Joaquin 0.28 2.27 461 8.11 502.0 203.0 3.85 \n", " Lobitas Creek 0.20 0.96 430 4.80 655.0 448.0 8.48 \n", " Tunitas lower climb 0.22 1.30 421 5.91 583.0 324.0 6.13 \n", "\n", " kms meters \n", " 11.87 575.0 \n", " 6.31 468.0 \n", " 6.31 468.0 \n", " 5.36 383.0 \n", " 5.36 383.0 \n", " 5.02 267.0 \n", " 4.65 250.0 \n", " 4.65 250.0 \n", " 5.66 244.0 \n", " 5.66 244.0 \n", " 1.93 183.0 \n", " 1.93 183.0 \n", " 2.43 173.0 \n", " 2.43 173.0 \n", " 1.61 147.0 \n", " 1.66 144.0 \n", " 3.65 141.0 \n", " 3.65 141.0 \n", " 1.54 131.0 \n", " 2.09 128.0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'feet') # Biggest climbing segments" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlehoursmilesfeetmphvamfpmipctkmsmeters
Redwood Gulch hits0.060.181513.00767.0839.015.890.2946.0
Valparaiso steep0.040.181454.501105.0806.015.260.2944.0
Valparaiso steep0.050.181453.60884.0806.015.260.2944.0
Limantour steepest0.090.201592.22538.0795.015.060.3248.0
Joaquin0.100.332543.30774.0770.014.580.5377.0
Joaquin0.090.332543.67860.0770.014.580.5377.0
Entrance Way Hill Repeats0.020.10765.001158.0760.014.390.1623.0
Stirrup Wall0.060.171252.83635.0735.013.930.2738.0
Stirrup Wall0.080.171252.12476.0735.013.930.2738.0
Westridge 3min0.080.372404.62914.0649.012.290.6073.0
Westridge 3min0.090.372404.11813.0649.012.290.6073.0
Limantour Spit0.090.473035.221026.0645.012.210.7692.0
Klamath Dr.0.020.12776.001173.0642.012.150.1923.0
green valley kicker0.080.291783.62678.0614.011.620.4754.0
Redwood Gulch wall0.110.432583.91715.0600.011.360.6979.0
Paloma Climb0.020.14827.001250.0586.011.090.2325.0
Try not to fall back0.210.714103.38595.0577.010.941.14125.0
Westridge0.140.683854.86838.0566.010.721.09117.0
Westridge0.160.683854.25733.0566.010.721.09117.0
Stair Step0.090.321753.56593.0547.010.360.5153.0
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" ], "text/plain": [ " title hours miles feet mph vam fpmi pct \\\n", " Redwood Gulch hits 0.06 0.18 151 3.00 767.0 839.0 15.89 \n", " Valparaiso steep 0.04 0.18 145 4.50 1105.0 806.0 15.26 \n", " Valparaiso steep 0.05 0.18 145 3.60 884.0 806.0 15.26 \n", " Limantour steepest 0.09 0.20 159 2.22 538.0 795.0 15.06 \n", " Joaquin 0.10 0.33 254 3.30 774.0 770.0 14.58 \n", " Joaquin 0.09 0.33 254 3.67 860.0 770.0 14.58 \n", " Entrance Way Hill Repeats 0.02 0.10 76 5.00 1158.0 760.0 14.39 \n", " Stirrup Wall 0.06 0.17 125 2.83 635.0 735.0 13.93 \n", " Stirrup Wall 0.08 0.17 125 2.12 476.0 735.0 13.93 \n", " Westridge 3min 0.08 0.37 240 4.62 914.0 649.0 12.29 \n", " Westridge 3min 0.09 0.37 240 4.11 813.0 649.0 12.29 \n", " Limantour Spit 0.09 0.47 303 5.22 1026.0 645.0 12.21 \n", " Klamath Dr. 0.02 0.12 77 6.00 1173.0 642.0 12.15 \n", " green valley kicker 0.08 0.29 178 3.62 678.0 614.0 11.62 \n", " Redwood Gulch wall 0.11 0.43 258 3.91 715.0 600.0 11.36 \n", " Paloma Climb 0.02 0.14 82 7.00 1250.0 586.0 11.09 \n", " Try not to fall back 0.21 0.71 410 3.38 595.0 577.0 10.94 \n", " Westridge 0.14 0.68 385 4.86 838.0 566.0 10.72 \n", " Westridge 0.16 0.68 385 4.25 733.0 566.0 10.72 \n", " Stair Step 0.09 0.32 175 3.56 593.0 547.0 10.36 \n", "\n", " kms meters \n", " 0.29 46.0 \n", " 0.29 44.0 \n", " 0.29 44.0 \n", " 0.32 48.0 \n", " 0.53 77.0 \n", " 0.53 77.0 \n", " 0.16 23.0 \n", " 0.27 38.0 \n", " 0.27 38.0 \n", " 0.60 73.0 \n", " 0.60 73.0 \n", " 0.76 92.0 \n", " 0.19 23.0 \n", " 0.47 54.0 \n", " 0.69 79.0 \n", " 0.23 25.0 \n", " 1.14 125.0 \n", " 1.09 117.0 \n", " 1.09 117.0 \n", " 0.51 53.0 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(segments, 'pct') # Steepest climbs" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateyeartitlehoursmilesfeetmphvamfpmipctkmsmeters
Fri, 1/9/20122012Otago Rail Trail Century7.87102.41228613.0189.022.00.42164.78697.0
Sat, 5/7/20222022Wine Country Century6.65100.26525315.08241.052.00.99161.321601.0
Thu, 6/14/20122012Coyote Creek Century with Juliet8.14100.07151312.2957.015.00.29161.01461.0
Sat, 5/13/20172017Morgan Hill iCare Classic7.46100.05459613.41188.046.00.87160.981401.0
Sat, 3/9/20242024Millbrae / San Bruno / Sawyer Camp Trail / Bay...8.1298.52489612.13184.050.00.94158.521492.0
Fri, 9/20/20242024Santa Rosa + Michael J Fox6.2893.33362814.86176.039.00.74150.171106.0
Sat, 5/12/20182018ICare Classic, Morgan Hill6.8091.29416013.42186.046.00.86146.891268.0
Sat, 5/6/20172017Wine Country Century7.2689.49524612.33220.059.01.11143.991599.0
Fri, 8/10/20182018Bike Ride Northwest Day 66.2484.70438013.57214.052.00.98136.281335.0
Fri, 2/28/20202020Sawyer Camp Trail6.4184.43344813.17164.041.00.77135.851051.0
Sun, 8/25/20242024Petaluma–Santa Rosa + Napa5.2284.26296616.14173.035.00.67135.57904.0
Sat, 6/1/20242024OLH / Old Haul / Loma Mar / Pescadero / Tunita...7.8481.70731410.42284.090.01.70131.462229.0
Wed, 6/7/20232023Los Altos7.0581.54211011.5791.026.00.49131.20643.0
Sun, 8/30/20202020Los Gatos6.3680.92210012.72101.026.00.49130.20640.0
Sat, 9/17/20222022San Gregorio / Tunitas6.5680.53601512.28279.075.01.41129.571833.0
Sat, 10/1/20162016Half Moon Bay overnight campout7.5180.07603910.66245.075.01.43128.831841.0
Mon, 10/5/20202020Half way around the bay on bay trail6.4480.0554112.4326.07.00.13128.80165.0
Sun, 6/21/20202020Sawyer Camp Trail6.5979.78173812.1180.022.00.41128.37530.0
Thu, 1/5/20122012Tekapo Lake to Omarama New Zealand5.4679.42214514.55120.027.00.51127.79654.0
Tue, 8/7/20182018Bike Ride Northwest Day 36.1878.96509212.78251.064.01.22127.051552.0
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" ], "text/plain": [ " date year title \\\n", " Fri, 1/9/2012 2012 Otago Rail Trail Century \n", " Sat, 5/7/2022 2022 Wine Country Century \n", " Thu, 6/14/2012 2012 Coyote Creek Century with Juliet \n", " Sat, 5/13/2017 2017 Morgan Hill iCare Classic \n", " Sat, 3/9/2024 2024 Millbrae / San Bruno / Sawyer Camp Trail / Bay... \n", " Fri, 9/20/2024 2024 Santa Rosa + Michael J Fox \n", " Sat, 5/12/2018 2018 ICare Classic, Morgan Hill \n", " Sat, 5/6/2017 2017 Wine Country Century \n", " Fri, 8/10/2018 2018 Bike Ride Northwest Day 6 \n", " Fri, 2/28/2020 2020 Sawyer Camp Trail \n", " Sun, 8/25/2024 2024 Petaluma–Santa Rosa + Napa \n", " Sat, 6/1/2024 2024 OLH / Old Haul / Loma Mar / Pescadero / Tunita... \n", " Wed, 6/7/2023 2023 Los Altos \n", " Sun, 8/30/2020 2020 Los Gatos \n", " Sat, 9/17/2022 2022 San Gregorio / Tunitas \n", " Sat, 10/1/2016 2016 Half Moon Bay overnight campout \n", " Mon, 10/5/2020 2020 Half way around the bay on bay trail \n", " Sun, 6/21/2020 2020 Sawyer Camp Trail \n", " Thu, 1/5/2012 2012 Tekapo Lake to Omarama New Zealand \n", " Tue, 8/7/2018 2018 Bike Ride Northwest Day 3 \n", "\n", " hours miles feet mph vam fpmi pct kms meters \n", " 7.87 102.41 2286 13.01 89.0 22.0 0.42 164.78 697.0 \n", " 6.65 100.26 5253 15.08 241.0 52.0 0.99 161.32 1601.0 \n", " 8.14 100.07 1513 12.29 57.0 15.0 0.29 161.01 461.0 \n", " 7.46 100.05 4596 13.41 188.0 46.0 0.87 160.98 1401.0 \n", " 8.12 98.52 4896 12.13 184.0 50.0 0.94 158.52 1492.0 \n", " 6.28 93.33 3628 14.86 176.0 39.0 0.74 150.17 1106.0 \n", " 6.80 91.29 4160 13.42 186.0 46.0 0.86 146.89 1268.0 \n", " 7.26 89.49 5246 12.33 220.0 59.0 1.11 143.99 1599.0 \n", " 6.24 84.70 4380 13.57 214.0 52.0 0.98 136.28 1335.0 \n", " 6.41 84.43 3448 13.17 164.0 41.0 0.77 135.85 1051.0 \n", " 5.22 84.26 2966 16.14 173.0 35.0 0.67 135.57 904.0 \n", " 7.84 81.70 7314 10.42 284.0 90.0 1.70 131.46 2229.0 \n", " 7.05 81.54 2110 11.57 91.0 26.0 0.49 131.20 643.0 \n", " 6.36 80.92 2100 12.72 101.0 26.0 0.49 130.20 640.0 \n", " 6.56 80.53 6015 12.28 279.0 75.0 1.41 129.57 1833.0 \n", " 7.51 80.07 6039 10.66 245.0 75.0 1.43 128.83 1841.0 \n", " 6.44 80.05 541 12.43 26.0 7.0 0.13 128.80 165.0 \n", " 6.59 79.78 1738 12.11 80.0 22.0 0.41 128.37 530.0 \n", " 5.46 79.42 2145 14.55 120.0 27.0 0.51 127.79 654.0 \n", " 6.18 78.96 5092 12.78 251.0 64.0 1.22 127.05 1552.0 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top(rides, 'miles') # Longest rides" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false }, "nbformat": 4, "nbformat_minor": 4 }