Managing your Strava Starred Segments for Garmin Live Segments

Posted by & filed under Activities, Routes, Segment Details, Segment List.

In summer 2015 Garmin introduced Live Strava Segments and since then both Mio and Wahoo Fitness have also introduced the feature to their recent devices. Currently the Garmin devices are limited to bringing in just 100 of your starred Strava segments. This resulted in a number of VeloViewer users asking for a quicker way to manage their starred segments when planning trips away as they were going beyond that 100 segment limit. Strava kindly opened up their API for starring segments so I’ve added in the ability to star and unstar segments in every possible location in VeloViewer to make life a bit easier for you.

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Kinomap Video Integration for Strava Segments on VeloViewer

Posted by & filed under Climbs, Segment Details, Segment List.

Kinomap, the home of fully geolocated videos (videos tied into GPS data), are now automatically matching up the 1,000’s of videos uploaded to their site with Strava segments and VeloViewer has entered into a cross-Channel collaboration to connect those with your own data. You will now be able to filter your Segments List and the segments on your Activity and Route Details pages to discover those with Kinomap videos and of course watch the videos embedded in the VeloViewer Segment Details page. So whether you are wanting to relive the views from past rides or check out the roads of your next holiday then this new integration is here to help.

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Updated Athlete Segment Comparison Page

Posted by & filed under Segment List.

The athlete, segment comparison page on VeloViewer used to be limited to just allow you to compare your best efforts against other athletes who used VeloViewer. Well that is no longer the case! The updated version of the comparison page now allows you to compare yourself against any other athlete on Strava on your favourite segments.

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Finding Your Local Poggio/Mur de Huy/Zoncolon/etc

Posted by & filed under Climbs, Segment Details, Segment List.

We all can watch the Pros smashing up the classic climbs on TV and wouldn’t it be great to have a go yourself! But unfortunately not many of us have the luxury of having any of those climbs on our own doorstep. However, what you can easily do using VeloViewer is to find which of your local climbs is the most like one of the classic climbs and then compare or attempt to match your time with that of the Pros. Here’s how to do it.

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VeloViewer Score – How Do You Measure Up?

Posted by & filed under Segment List, Summary.

The VeloViewer Score is a composite measure providing a single value that represents your best Strava achievements. This value can then be used to benchmark yourself against any other VeloViewer user around the world. In this post I’ll explain how it is calculated and where your Score fits in against other VeloViewer users.

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Number of Tries now in Segment List

Posted by & filed under Segment List.

Not quite sure why I didn’t have this column in here from the start to be honest but its there now: the number of tries you’ve had on each segment, and of course you can order by it.

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Position Score – the Column Formerly Known as Position Percentile

Posted by & filed under Charts, Data, Rides List, Segment List, Summary.

Your leaderboard positions on Strava segments can be a bit of a badge of honour but the significance of each of those positions can vary wildly. If you are 6th placed out of 3000 riders then that is pretty good going but 1st place out of just 2 riders is less so. In steps an suggestion from a forum to include a position percentile column and corresponding graphs, a few minutes later and the first incarnation of the position percentile appeared.

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Bad Strava Elevation and Distance Data

Posted by & filed under Charts, Data, Segment List, Summary.

Your stats in VeloViewer are only as good as the data that is passed in from Strava, and around 1.5% (based on sample data I had a couple of months ago) of Strava segments seem to have bad data associated with them. The 2 main culprits are dodgy elevation data and non-matching distance data.

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