5 ms·

Mining Strava's cycling data

27 points by ocfnash 12y ago

nl 12y ago
Interesting. You should link to the segments you are talking about. (Edit: I see you did now, sorry. Edit2: Wow, Ryan Sherlock has that KOM in Ireland and the KOM on Old La Honda in California. Nice work by a non-Protour rider!) Amongst the questions that I thought it might be interesting to attack are the following.. I think you'll find many people are using Strava data to attack the problem of developing a model for maximal power output over time. Veloclinc[1] and DJConnel[2] are probably the best places to start for reading about this. The "cleaning data" section surprised me. In my experience maybe 10% of (manually inspected) efforts have significant problems, but you seem to have thrown away a lot more than that on Col du Tourmalet and Alpe d’Huez. Stocking Lane looks roughly in the order I'd expect. Any idea what is going on there? it is likely that a person will cycle a 15% gradient towards the end of a climb more slowly than the 15% near the beginning. Not really. Judging your maximum speed on a climb is a significant problem and it goes both ways (especially on faster cyclists). Hence the "Chris Froome looking at his stem" meme[3] - he is notorious for climbing based on power output (which maximises your overall velocity). Regarding altitude, you are best to look at the device the data came from. Phones are notorious for getting altitude data wrong, whereas Garmin devices are pretty good. Using Google data is mixed: the gradient of the road changes significantly depending what line you take, and that isn't replicated in Google's data. Regarding your power equation, you realize Strava already does this (as power estimates), right? [4] is a pretty decent Javascript version. The problem is that it is very weight dependant, and getting an exact weight for a bike+rider+clothes is pretty hard from random Strava data. [1] Hard to read, but eg: http://veloclinic.tumblr.com/post/85194606798/ward-smith-cp-vs-wko4-ftp-if-you-use-the-correct http://veloclinic.tumblr.com/post/85194606798/ward-smith-cp-... [2] eg: http://djconnel.blogspot.com.au/2014/05/numerical-testing-of-maximal-power.html http://djconnel.blogspot.com.au/2014/05/numerical-testing-of... [3] http://chrisfroomelookingatstems.tumblr.com/ http://chrisfroomelookingatstems.tumblr.com/ [4] http://www.kreuzotter.de/english/espeed.htm http://www.kreuzotter.de/english/espeed.htm
- nl 12y ago
  Regarding the speed over time thing for climbs, here's a good example. This is a local 1.3 km climb at an average of 12%: http://www.strava.com/segments/872359?filter=overall http://www.strava.com/segments/872359?filter=overall The equal KOM used a power meter to keep his power as flat as possible. You can see how his speed varied with variations in the gradiant more than the distance: http://www.strava.com/activities/114411950/analysis http://www.strava.com/activities/114411950/analysis
- thrownaway2424 12y ago
  Android vs. Garmin altitude data seems like an irrelevant distinction, as Strava keeps and maintains forever the altitude profile of the very first person who rode from A to B, no matter how ridiculous the data. On the route I ride most often, up Tunnel Rd in Oakland, California, it's a climb of about 1000 feet over about 4 miles, depending on where you reckon the start and end, but on the whole length it's just monotonically uphill. There are no down grades. But the Strava data is all over the place. It goes up and down on small scales constantly. It says there's a -47% grade at one point. The profile looks like the business end of a backsaw, when in reality it's just up. To make any sense of this data requires substantial low-pass filtering over their altitude data. I don't know why Strava doesn't try to clean this up.
  - nl 12y ago
    I think the Strava Streams API call[1] can include the altitude data. Haven't tried the v3 API though so I'm sit sure, but the documentation indicates it is there. [1] http://strava.github.io/api/v3/streams/ http://strava.github.io/api/v3/streams/
  - paulmach 12y ago
    Using the API you can get both the Segment altitude data and the Segment Effort altitude data. The segment data is what's displayed on the website and can be bad. But the Segment Effort data would be a subset of the data from the activity. So you can get many many versions of the altitude for the segment and do any type of analysis you wish.
keithg 12y ago
If you guys are interested in data-mining Strava, here is some interesting info on their metro project and how they are using it to affect change. http://blog.strava.com/arent-we-all-people-for-bikes-7783/ http://blog.strava.com/arent-we-all-people-for-bikes-7783/ http://metro.strava.com/ http://metro.strava.com/

27 points by ocfnash 12y ago

nl 12y ago

Interesting. You should link to the segments you are talking about. (Edit: I see you did now, sorry. Edit2: Wow, Ryan Sherlock has that KOM in Ireland and the KOM on Old La Honda in California. Nice work by a non-Protour rider!) Amongst the questions that I thought it might be interesting to attack are the following.. I think you'll find many people are using Strava data to attack the problem of developing a model for maximal power output over time. Veloclinc[1] and DJConnel[2] are probably the best places to start for reading about this. The "cleaning data" section surprised me. In my experience maybe 10% of (manually inspected) efforts have significant problems, but you seem to have thrown away a lot more than that on Col du Tourmalet and Alpe d’Huez. Stocking Lane looks roughly in the order I'd expect. Any idea what is going on there? it is likely that a person will cycle a 15% gradient towards the end of a climb more slowly than the 15% near the beginning. Not really. Judging your maximum speed on a climb is a significant problem and it goes both ways (especially on faster cyclists). Hence the "Chris Froome looking at his stem" meme[3] - he is notorious for climbing based on power output (which maximises your overall velocity). Regarding altitude, you are best to look at the device the data came from. Phones are notorious for getting altitude data wrong, whereas Garmin devices are pretty good. Using Google data is mixed: the gradient of the road changes significantly depending what line you take, and that isn't replicated in Google's data. Regarding your power equation, you realize Strava already does this (as power estimates), right? [4] is a pretty decent Javascript version. The problem is that it is very weight dependant, and getting an exact weight for a bike+rider+clothes is pretty hard from random Strava data. [1] Hard to read, but eg: http://veloclinic.tumblr.com/post/85194606798/ward-smith-cp-vs-wko4-ftp-if-you-use-the-correct http://veloclinic.tumblr.com/post/85194606798/ward-smith-cp-... [2] eg: http://djconnel.blogspot.com.au/2014/05/numerical-testing-of-maximal-power.html http://djconnel.blogspot.com.au/2014/05/numerical-testing-of... [3] http://chrisfroomelookingatstems.tumblr.com/ http://chrisfroomelookingatstems.tumblr.com/ [4] http://www.kreuzotter.de/english/espeed.htm http://www.kreuzotter.de/english/espeed.htm

nl 12y ago
Regarding the speed over time thing for climbs, here's a good example. This is a local 1.3 km climb at an average of 12%: http://www.strava.com/segments/872359?filter=overall http://www.strava.com/segments/872359?filter=overall The equal KOM used a power meter to keep his power as flat as possible. You can see how his speed varied with variations in the gradiant more than the distance: http://www.strava.com/activities/114411950/analysis http://www.strava.com/activities/114411950/analysis
thrownaway2424 12y ago
Android vs. Garmin altitude data seems like an irrelevant distinction, as Strava keeps and maintains forever the altitude profile of the very first person who rode from A to B, no matter how ridiculous the data. On the route I ride most often, up Tunnel Rd in Oakland, California, it's a climb of about 1000 feet over about 4 miles, depending on where you reckon the start and end, but on the whole length it's just monotonically uphill. There are no down grades. But the Strava data is all over the place. It goes up and down on small scales constantly. It says there's a -47% grade at one point. The profile looks like the business end of a backsaw, when in reality it's just up. To make any sense of this data requires substantial low-pass filtering over their altitude data. I don't know why Strava doesn't try to clean this up.
- nl 12y ago
  I think the Strava Streams API call[1] can include the altitude data. Haven't tried the v3 API though so I'm sit sure, but the documentation indicates it is there. [1] http://strava.github.io/api/v3/streams/ http://strava.github.io/api/v3/streams/
- paulmach 12y ago
  Using the API you can get both the Segment altitude data and the Segment Effort altitude data. The segment data is what's displayed on the website and can be bad. But the Segment Effort data would be a subset of the data from the activity. So you can get many many versions of the altitude for the segment and do any type of analysis you wish.

keithg 12y ago

If you guys are interested in data-mining Strava, here is some interesting info on their metro project and how they are using it to affect change. http://blog.strava.com/arent-we-all-people-for-bikes-7783/ http://blog.strava.com/arent-we-all-people-for-bikes-7783/ http://metro.strava.com/ http://metro.strava.com/