{"id":63,"date":"2021-08-15T20:18:34","date_gmt":"2021-08-15T20:18:34","guid":{"rendered":"https:\/\/rainbowit.net\/themes\/inbio\/?post_type=rainbow_projects&#038;p=63"},"modified":"2024-08-30T01:05:30","modified_gmt":"2024-08-30T01:05:30","slug":"cincinnati-reds-analytics-hackathon","status":"publish","type":"rainbow_projects","link":"https:\/\/daveyount.com\/?rainbow_projects=cincinnati-reds-analytics-hackathon","title":{"rendered":"Cincinnati Reds Analytics Hackathon"},"content":{"rendered":"<p>This study introduces an innovative approach to quantify pitcher fatigue, leveraging the rich data provided by the Statcast system. By focusing on detailed pitch-by-pitch data, we aim to uncover how fatigue affects various pitching metrics, potentially impacting a pitcher&#8217;s performance within a game. To complement our analytical framework, we also created a Shiny application, enabling a dynamic exploration of pitcher performance data. This tool allows for interactive analysis, offering users the ability to examine and identify pitchers who may benefit from role adjustments based on their fatigue profiles and performance metrics. Integrating this application into our study enriches the decision-making process, providing a practical platform for applying our findings to real-world baseball strategy and pitcher management.<\/p><figure class=\"wp-block-gallery has-nested-images columns-2 is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\"><figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"422\" data-id=\"5440\" src=\"https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-1024x422.png\" alt=\"\" class=\"wp-image-5440\" srcset=\"https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-1024x422.png 1024w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-300x124.png 300w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-768x316.png 768w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-1536x632.png 1536w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-2048x843.png 2048w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample2-600x247.png 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Screen One<\/figcaption><\/figure>\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"652\" data-id=\"5437\" src=\"https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-1024x652.png\" alt=\"\" class=\"wp-image-5437\" srcset=\"https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-1024x652.png 1024w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-300x191.png 300w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-768x489.png 768w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-1536x978.png 1536w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-2048x1304.png 2048w, https:\/\/daveyount.com\/wp-content\/uploads\/2021\/08\/appexample3-600x382.png 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Screen Two<\/figcaption><\/figure><\/figure><p>Special thanks to my hackathon teammates Shane Hauck, Jack Hinde, Anthony Vicinanzo, and Jacob Weber!<\/p>","protected":false},"excerpt":{"rendered":"<p>This study introduces an innovative approach to quantify pitcher fatigue, leveraging the rich data provided by the Statcast system. By focusing on detailed pitch-by-pitch data, we aim to uncover how fatigue affects various pitching metrics, potentially impacting a pitcher&#8217;s performance within a game. To complement our analytical framework, we also created a Shiny application, enabling [&hellip;]<\/p>\n","protected":false},"featured_media":5437,"menu_order":0,"template":"","rainbow_projects_category":[35],"class_list":["post-63","rainbow_projects","type-rainbow_projects","status-publish","has-post-thumbnail","hentry","rainbow_projects_category-gallery"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cincinnati Reds Analytics Hackathon - DaveYount.com<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/daveyount.com\/?rainbow_projects=cincinnati-reds-analytics-hackathon\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cincinnati Reds Analytics Hackathon - DaveYount.com\" \/>\n<meta property=\"og:description\" content=\"This study introduces an innovative approach to quantify pitcher fatigue, leveraging the rich data provided by the Statcast system. By focusing on detailed pitch-by-pitch data, we aim to uncover how fatigue affects various pitching metrics, potentially impacting a pitcher&#8217;s performance within a game. 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