{"version":"1.0","provider_name":"Technology and Operations Management","provider_url":"https:\/\/d3.harvard.edu\/platform-rctom","author_name":"Significantly Correlated","author_url":"https:\/\/d3.harvard.edu\/platform-rctom\/author\/significantly-correlated\/","title":"Unlocking the power of STATS - Technology and Operations Management","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"6MfFo29FWh\"><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/unlocking-the-power-of-stats\/\">Unlocking the power of STATS<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/unlocking-the-power-of-stats\/embed\/#?secret=6MfFo29FWh\" width=\"600\" height=\"338\" title=\"&#8220;Unlocking the power of STATS&#8221; &#8212; Technology and Operations Management\" data-secret=\"6MfFo29FWh\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/d3.harvard.edu\/platform-rctom\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","thumbnail_url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Sports-in-data-analytics-2.png","thumbnail_width":1080,"thumbnail_height":383,"description":"In an arena where any slight edge could mean the difference between winning and losing, machine learning has never been more important in sports. As the industry becomes more and more inundated with data, sifting through information to create meaningful insights is paramount to the success of sports organizations around the world."}