Mining the Strava Data

Moved to here. Have a nice day 😁

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5 thoughts on “Mining the Strava Data

  1. Nice article, though way above my mathematical knowledge.
    I’m trying to detect cheating myself at http://www.komdefender.com
    The problem I’ve found is that outside the United States, altitude data tends to be much less accurate, so trying to detect abnormal speeds based on anything but significant altitude changes tends to be problematic.
    It’s also difficult because the Strava API’s don’t let you see all of a riders activity data – just a segment at a time.
    Good luck with your analysis if you keep it up.

    MartinT

  2. Thanks Martin.

    Your KOMDefender website looks really cool. It’s a shame about the full activity streams not being available. (I’m surprised they don’t make this clearer in the docs here http://strava.github.io/api/v3/streams/#activity).

    Incidentally the noisy altitude data is annoying but it should be possible to mitigate the issue significantly with a bit of effort. You may well be employing such techniques already but if not and you have the time/motivation I would suggest using variance reduction techniques to combine the estimates of altitude at a given distance along a segment from many/all segment efforts for that segment (e.g., in simplest case, just simple average or maybe median, interpolated). That could be a one-off (or at least infrequent) calculation that you would do for each segment and then you would discard the altitude data for a given segment effort and just use the private altitude series that you have calculated. Alternatively Google Elevation API may do the trick.

    Anyway, thanks again and good luck.

  3. Nice work Oliver. Know that view well also :). Beautiful Wiclow mountains. Been playing around with the Strava api myself as a project to learn web dev. I decided to tackle integrating weather info with segment leader-boards as I’m convinced all my KOM’s were stolen by massive tailwinds. Only joking :), but weather does have a huge effect on leader-board times. Thank you for posting the piece on power calculations as It gave me plenty of fuel for thought. I think I’ll code this into my app, also factoring in the wind which I’m also capturing.
    I’ve added a link to a segment I’m sure you’ve ridden many times showing integrated weather info.
    http://www.peaksp.com/Segments/Leaderboard/678029/Sallygap-Eastwards

    Padraig

  4. Thanks Padraig, I’m glad you found my words interesting. I do indeed know that segment.

    Yes, the weather has a huge effect on times. So much so that I expect the top KOM times were generally executed under very favourable conditions. Fortunately, one of the bonuses of being an average (at best) cyclist like myself is that those impressive times are too far away for me to worry about!

    Anyway good luck with your own investigations of Strava’s API. There’s a wealth of data there.

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