{"id":160652,"date":"2017-08-07T11:21:38","date_gmt":"2017-08-07T11:21:38","guid":{"rendered":"https:\/\/www.welovecycling.com\/wide\/?p=160652"},"modified":"2017-08-07T11:44:01","modified_gmt":"2017-08-07T11:44:01","slug":"internet-things-machine-learning-changed-le-tour","status":"publish","type":"post","link":"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/","title":{"rendered":"How the Internet of Things and Machine Learning Changed Le Tour"},"content":{"rendered":"<p><strong>Using a black stick under the saddle, every rider of the Tour can be analysed in detail. Most of the collected data can be instantly shown on the screens.<\/strong><br \/>\n<!--more--><\/p>\n<p>If you\u2019ve been watching the Tour de France this year, you\u2019ve probably noticed a suspicious black stick protruding from the end of the saddle of each rider and speculated what were they good for. You may also wonder where all the constant stream of data on your TV screen comes from. Both are connected as they are parts of the Dimension Data system providing TV viewers with astonishing details about all riders of the tour.<\/p>\n<div class=\"embed-controller-wrapper\">\n<blockquote class=\"twitter-tweet\" data-width=\"500\" data-dnt=\"true\">\n<p lang=\"en\" dir=\"ltr\">Greipel (LTS) was the fastest on the final rundown to the Champs-\u00c9lys\u00e9es but it wasn&#39;t enough to overcome Groenewegen (TLJ).<a href=\"https:\/\/twitter.com\/hashtag\/TDFdata?src=hash&amp;ref_src=twsrc%5Etfw\">#TDFdata<\/a> <a href=\"https:\/\/t.co\/Bhe5uBtAik\">pic.twitter.com\/Bhe5uBtAik<\/a><\/p>\n<p>&mdash; letourdata (@letourdata) <a href=\"https:\/\/twitter.com\/letourdata\/status\/889176309536108544?ref_src=twsrc%5Etfw\">July 23, 2017<\/a><\/p><\/blockquote>\n<p><script async data-src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/div>\n<p>The banana peel-sized black sticks are tiny transmitters, collecting and sending off real-time data from riders during the race. Introduced at the Crit\u00e9rium du Dauphin\u00e9 in 2015, the device has been refined and upgraded and can now offer a wide spectrum of data about riders including speed, position, and much more. In the past, there were tiny mobile phones hidden in the seat posts, used for similar purpose, yet with far less efficiency and accuracy. The system is not the official time tracker which is a small blue pod placed at the rear end of each frame.<\/p>\n<div class=\"embed-controller-wrapper\">\n<blockquote class=\"twitter-tweet\" data-width=\"500\" data-dnt=\"true\">\n<p lang=\"en\" dir=\"ltr\"><a href=\"https:\/\/twitter.com\/hashtag\/TDF2017?src=hash&amp;ref_src=twsrc%5Etfw\">#TDF2017<\/a> featured more slopes of more than 15% gradient than ever: brutal for the riders, but delightful for the spectators.<a href=\"https:\/\/twitter.com\/hashtag\/TDFdata?src=hash&amp;ref_src=twsrc%5Etfw\">#TDFdata<\/a> <a href=\"https:\/\/t.co\/XBGlEPxwwX\">pic.twitter.com\/XBGlEPxwwX<\/a><\/p>\n<p>&mdash; letourdata (@letourdata) <a href=\"https:\/\/twitter.com\/letourdata\/status\/889460725256736768?ref_src=twsrc%5Etfw\">July 24, 2017<\/a><\/p><\/blockquote>\n<p><script async data-src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/div>\n<p>The original idea was and still is to provide TV audience with the most relevant details and enhance their experience, as well as recording speed, gradient, surrounding temperature and, primarily, the accurate position of each rider for calculating time gaps between them or realising composition of the groups. The teams are responsible for recharging the small batteries that last for about three days of racing. Obviously, most teams keep recharging them before every upcoming stage to ensure everything goes well.<\/p>\n<div class=\"embed-controller-wrapper\">\n<blockquote class=\"twitter-tweet\" data-width=\"500\" data-dnt=\"true\">\n<p lang=\"en\" dir=\"ltr\">Bardet lost 5.2&quot; per km over Froome (SKY).<br \/>Average speeds:<br \/>Froome (SKY) 47.6km\/h<br \/>Uran (CDT) 46.9km\/h<br \/>Bardet (ALM) 44.6km\/h<a href=\"https:\/\/twitter.com\/hashtag\/TDF2017?src=hash&amp;ref_src=twsrc%5Etfw\">#TDF2017<\/a> <a href=\"https:\/\/twitter.com\/hashtag\/TDFdata?src=hash&amp;ref_src=twsrc%5Etfw\">#TDFdata<\/a> <a href=\"https:\/\/t.co\/89RSQGEYdq\">pic.twitter.com\/89RSQGEYdq<\/a><\/p>\n<p>&mdash; letourdata (@letourdata) <a href=\"https:\/\/twitter.com\/letourdata\/status\/888790531576406016?ref_src=twsrc%5Etfw\">July 22, 2017<\/a><\/p><\/blockquote>\n<p><script async data-src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/div>\n<p>And how is the data delivered? The devices attached to the seats of the bikes will automatically connect with the nearby helicopters or aircrafts used for broadcasting TV images. From here the data is send to Dimension Data\u2019s trucks and the raw material is transformed into relevant facts that can be presented in easy, comprehensible tabs and graphs.<\/p>\n<lite-youtube videoid=MkbkXgOcbwE params=\"enablejsapi=1\" style=\"background-image: url('https:\/\/i.ytimg.com\/vi_webp\/MkbkXgOcbwE\/maxresdefault.webp');\"><\/lite-youtube>\n<p>Once the data is gained, artificial intelligence will analyse the profile of each competitor to the utmost detail. What is just an exciting experience for the TV viewers, can actually help to improve both the riders\u2019 behaviour and the teams\u2019 tactics. According to the collected facts, processed through advanced digital analytics platform, teams get the whole picture of their riders, including their performance during the sprint, climbs or time trials. The results are also being used for calculating finishing time of the stage winner, which is important for setting the time limit in which all riders must reach the finishing line without being disqualified. After each race, there are also many graphs published where you can find interesting fact concerning the timing of small groups\u2019 breakaways or cooperation within the teams.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Using a black stick under the saddle, every rider of the Tour can be analysed in detail. Most of the [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":160654,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[329],"tags":[845,1169],"global-categories":[],"class_list":["post-160652","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-road-cycling","tag-tdf","tag-tour-de-france-2017"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.3 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How the Internet of Things and Machine Learning Changed Le Tour - \u0160koda We Love Cycling<\/title>\n<meta name=\"description\" content=\"Using a black stick under the saddle, every rider of the Tour can be analysed in detail. Most of the collected data can be instantly shown on the screens.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How the Internet of Things and Machine Learning Changed Le Tour\" \/>\n<meta property=\"og:description\" content=\"Using a black stick under the saddle, every rider of the Tour can be analysed in detail. Most of the collected data can be instantly shown on the screens.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/\" \/>\n<meta property=\"og:site_name\" content=\"\u0160koda We Love Cycling\" \/>\n<meta property=\"article:published_time\" content=\"2017-08-07T11:21:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2017-08-07T11:44:01+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/d2p6e6u75xmxt8.cloudfront.net\/2\/2017\/07\/cover-dimension-data.webp\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Adam Marsal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/\",\"url\":\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/\",\"name\":\"How the Internet of Things and Machine Learning Changed Le Tour - \u0160koda We Love Cycling\",\"isPartOf\":{\"@id\":\"https:\/\/www.welovecycling.com\/wide\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.welovecycling.com\/wide\/2017\/08\/07\/internet-things-machine-learning-changed-le-tour\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/d2p6e6u75xmxt8.cloudfront.net\/2\/2017\/07\/cover-dimension-data.webp\",\"datePublished\":\"2017-08-07T11:21:38+00:00\",\"dateModified\":\"2017-08-07T11:44:01+00:00\",\"author\":{\"@id\":\"\"},\"description\":\"Using a black stick under the saddle, every rider of the Tour can be analysed in detail. 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