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-...
[2] eg: http://djconnel.blogspot.com.au/2014/05/numerical-testing-of...
[3] http://chrisfroomelookingatstems.tumblr.com/
[4] http://www.kreuzotter.de/english/espeed.htm
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-...
[2] eg: http://djconnel.blogspot.com.au/2014/05/numerical-testing-of...
[3] http://chrisfroomelookingatstems.tumblr.com/
[4] http://www.kreuzotter.de/english/espeed.htm