{"id":19223,"date":"2023-11-27T15:45:36","date_gmt":"2023-11-27T20:45:36","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=19223"},"modified":"2023-12-06T17:10:57","modified_gmt":"2023-12-06T22:10:57","slug":"probing-the-importance-of-faceoffs","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2023\/11\/probing-the-importance-of-faceoffs\/","title":{"rendered":"Probing the Importance of Faceoffs"},"content":{"rendered":"<figure id=\"attachment_19289\" class=\"wp-caption alignnone\" style=\"width: 1034px\"><a href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage.jpeg\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-19289 size-large\" src=\"https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage-1024x683.jpeg\" alt=\"Tad using a computer in an empty ice hockey rink.\" width=\"1024\" height=\"683\" srcset=\"https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage-1024x683.jpeg 1024w, https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage-300x200.jpeg 300w, https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage-768x512.jpeg 768w, https:\/\/engineering.jhu.edu\/magazine-archive\/wp-content\/uploads\/2023\/11\/Tab-hockey-collage.jpeg 1500w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-caption-text\">Tad Berkery<\/figcaption><\/figure>\n<p>Ice hockey analytics takes faceoffs into consideration, but current approaches don&#8217;t go much further than the idea that winning more faceoffs than losing is good for a team.<\/p>\n<p>\u201cHow much do faceoffs help teams to win or lose games?\u201d asks Tad Berkery, a senior <a href=\"https:\/\/www.cs.jhu.edu\/\" target=\"_blank\" rel=\"noopener\">computer science<\/a> major. \u201cExisting analytics haven\u2019t really mapped faceoffs to scoring outcomes or quantified the value of winning a faceoff.\u201d<\/p>\n<p>That\u2019s where his project comes in. Collaborating with a team of students from Hopkins Engineering\u2019s <a href=\"https:\/\/jhusportsanalytics.wordpress.com\/about\/\" target=\"_blank\" rel=\"noopener\">sports analytics research group<\/a>, Berkery has harnessed the power of AI and machine learning to determine the true value of the faceoff.<\/p>\n<p>The data set Berkery and the research group used considers more than 5.2 million plays from three hockey analytics sources: Evolving Hockey, Money Puck, and analyst Corey Sznajder.<\/p>\n<p>Berkery and his collaborators\u2014engineering student Max Stevens and recent graduate Justin Nam \u201923, and Syracuse University graduate Chase Seibold, under the guidance of Whiting School professors<a href=\"https:\/\/engineering.jhu.edu\/faculty\/anton-dahbura\/\" target=\"_blank\" rel=\"noopener\"> Anton Dahbura<\/a> and <a href=\"https:\/\/engineering.jhu.edu\/faculty\/donniell-fishkind\/\" target=\"_blank\" rel=\"noopener\">Donniell Fishkind<\/a>\u2014calculated that one faceoff is worth about .015 goals. While that may sound minuscule, with an average of 60 faceoffs per game, the statistic becomes significant.<\/p>\n<p>If a team wins six additional faceoffs per game, it could add 15 goals over the course of an 82-game season, Berkery says. Teams could use the findings for roster construction, he says.<\/p>\n<p>\u201cThe project is showcasing how faceoffs are an underpriced market inefficiency that can be exploited: faceoff performance can win games, and you can get people who are good at faceoffs for near league minimum salaries,\u201d he says. \u201cIt\u2019s an answer to the question of not only who\u2019s a good player, but also who\u2019s a good player that I can get on a below-market deal.\u201d<\/p>\n<p>The NHL has taken notice: He presented to one team this summer.<\/p>\n<p>Berkery has always had an interest in sports analytics. In high school, he worked with the University of Maryland softball team and sat in on graduate data science classes at George Mason University. He also created elaborate projections in fantasy football leagues\u2014a skill that won him several championships.<\/p>\n<p>Through the engineering analytics group, Berkery worked on a project with the Baltimore Ravens that analyzed how kickoffs can help win games, and another with the Baltimore Orioles on predicting hitters\u2019 performances.<\/p>\n<p>\u201cSports can be a game of bounces and a game of inches,\u201d Berkery says. \u201cThere\u2019s something really special about combining analytics with athletic talent and luck\u2014to elevate the game and potentially turn a would-be loss into a well-deserved win.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ice hockey analytics takes faceoffs into consideration, but current approaches don\u2019t go much further than the idea that winning more faceoffs than losing is good for a team.<\/p>\n","protected":false},"author":34,"featured_media":19289,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[25],"tags":[],"class_list":["post-19223","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-students","issue-winter-2024"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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