{"id":140,"date":"2018-12-03T19:47:48","date_gmt":"2018-12-04T01:47:48","guid":{"rendered":"https:\/\/puckpossessed.com\/?p=140"},"modified":"2018-12-03T20:00:03","modified_gmt":"2018-12-04T02:00:03","slug":"puck-possessed-7","status":"publish","type":"post","link":"https:\/\/biathlonanalytics.com\/TEST\/puck-possessed-7\/","title":{"rendered":"Puck Possessed #7"},"content":{"rendered":"<h2>\n<p>Power play success expressed through the time it takes to score a PP goal<\/p>\n<\/h2>\n<p>Power play success is typically shown as a percentage: number of power play goals as a percentage of the number of power play opportunities. Nothing wrong with that. But I was curious to see how long a team typically takes to score during the 120 seconds of power play. Yeah, big caveat; see below in the Data section. And pretty soon I realized it would not be as easy as I initially thought. You&#8217;d think I would have figured that out by now&#8230;<\/p>\n<h3>THE DATA<\/h3>\n<p>I wanted to use data from the NHL site, since I was (and still am) not aware of any data source available with the kind of information I needed. So I did a couple of things (code available on <a href=\"https:\/\/github.com\/rjweise\/PlayByPlayHTMfile\">my github page<\/a>):<\/p>\n<ul>\n<li>created R-scripts to scrape NHL&#8217;s play-by-play JSON files for every play and it&#8217;s time of occurrence in the game<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/11\/Screen-Shot-2018-11-26-at-10.50.03-PM-300x130.png\" alt=\"\" width=\"302\" height=\"131\" \/>;<\/li>\n<li>created R-scripts to scrape NHL&#8217;s game summary HTM(L) files to get team strength for goals, who was on the ice, and penalty details<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/11\/Screen-Shot-2018-11-26-at-10.48.07-PM-300x206.png\" alt=\"\" width=\"300\" height=\"206\" \/>;<\/li>\n<li>Loaded all this data into a SQLite database (if you are looking for a lightweight SQL environment, hosted freely and accessible from any machine, I highly recommend checking out\u00a0<a href=\"https:\/\/sqlitebrowser.org\/\">https:\/\/sqlitebrowser.org\/<\/a>) and wrote some SQL scripts to bring things together so to speak (with some help from Google Sheets, believe it or not);<\/li>\n<li>Loaded the data into Tableau Public, made a number of calculated fields to select the PP goals meeting the criteria mentioned further down, and exported the resulting chart into a stand-alone data source;<\/li>\n<li>Got the NHL Power play summary data for the selected time frame and combined that with the above mentioned data set<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/11\/Screen-Shot-2018-11-26-at-10.49.34-PM-300x71.png\" alt=\"\" width=\"300\" height=\"71\" \/>;<\/li>\n<li>I filtered out only power plays for minor penalties (120 seconds) and with a one-man-advantage to,\u00a0 you know, compare pucks to pucks equally. The time frame is the 2017-2018 season and the first 307 games of the 2018-2019 season (no promises to update this at the end of the season);<\/li>\n<li>Time to look at the data in Tableau! Quite frankly, looking at the data visually made me think for a minute; I assumed that scoring quickly into the power play is a good thing. But looking at the Penguins here, they took more time and perhaps wore out their opponents more, still scoring on the power play.\u00a0<\/li>\n<\/ul>\n<h3>RESULTS<\/h3>\n<p>So the more I was working with the data, the more I wondered what values would be considered good vs. bad. Not that I think that teams could\/would delay scoring on a PP, but is it more beneficial to score at 1:59 of the PP, using up as much time of the 2 minutes as possible and wearing out the opponent, or is scoring 10 seconds into the PP a mental blow to the opposing team, having a bigger impact than the physical wearing out?<\/p>\n<p>I posted a poll on twitter to find out general opinion, and although I appreciate seven folks taking the time to respond, the sample size is a bit small to come to a conclusive answer. And even still, it would have been pretty much even:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/11\/2018-11-28_14-29-27-221x300.jpg\" alt=\"\" width=\"221\" height=\"300\" \/><\/p>\n<p>The issue goes beyond one page for the first time, as I am showing traditional PP success with a feature to look at team&#8217;s PP shot locations, this alternative view of PP success, and how they relate or compare.was not going to fit everything I wanted to show on one page.<\/p>\n<p><strong>Page 1<\/strong> shows the traditional power play percentage by power play opportunities and power play goals;<\/p>\n<p><strong>Page 2<\/strong> shows all goals scored in the specified time frame on one-man advantages after minor penalties, called power play efficiency. The main conclusion that these type of goals seem to be randomly and widely spread over the 2 minutes of the power play. The team averages are then displayed, and looked in more detail (all team averages are between 50 and 68 seconds in the power play) to better see the differences;<\/p>\n<p><strong>Page 3<\/strong> shows the combination of power play percentage and efficiency.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/12\/Screen-Shot-2018-12-03-at-6.48.02-PM-229x300.png\" alt=\"\" width=\"229\" height=\"300\" \/><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/12\/Screen-Shot-2018-12-03-at-6.48.19-PM-229x300.png\" alt=\"\" width=\"229\" height=\"300\" \/><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/puckpossessed.com\/wp-content\/uploads\/2018\/12\/Screen-Shot-2018-12-03-at-6.48.57-PM-229x300.png\" alt=\"\" width=\"229\" height=\"300\" \/><\/p>\n<p>The interactive version can be found on <a href=\"https:\/\/public.tableau.com\/profile\/rj7974#!\/vizhome\/PuckPossessed7\/p1\">my Tableau Public page<\/a>, as usual.<\/p>\n<p>\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>More to follow!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Power play success expressed through the time it takes to score a PP goal Power play success is typically shown as a percentage: number of power play goals as a percentage of the number of power play opportunities. Nothing wrong with that. But I was curious to see how long a team typically takes to&hellip;&nbsp;<a href=\"https:\/\/biathlonanalytics.com\/TEST\/puck-possessed-7\/\" rel=\"bookmark\">Read More &raquo;<span class=\"screen-reader-text\">Puck Possessed #7<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[4],"tags":[],"class_list":["post-140","post","type-post","status-publish","format-standard","hentry","category-puck-possessed-hockey"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/posts\/140","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/comments?post=140"}],"version-history":[{"count":11,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/posts\/140\/revisions"}],"predecessor-version":[{"id":177,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/posts\/140\/revisions\/177"}],"wp:attachment":[{"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/media?parent=140"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/categories?post=140"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biathlonanalytics.com\/TEST\/wp-json\/wp\/v2\/tags?post=140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}