ShotQuality
Rafael Padilla is a seasoned Machine Learning Engineer with extensive experience in machine learning and computer vision, currently working at Hugging Face from June 2023 to December 2023. Prior to this role, Rafael served as a Machine Learning Researcher at COPPE / UFRJ from May 2017 to 2023 and contributed to machine learning R&D at Intuition Machines between October 2021 and November 2022. Rafael has also held positions as a Visiting Researcher at the University of South-Eastern Norway in 2018 and was involved in various projects at INDT from May 2011 to June 2016, focusing on algorithm development in computer vision and image processing. Additionally, Rafael's background includes roles in industrial engineering, software development, and technical evangelism, with an educational foundation comprising a PhD in Electrical Engineering from Universidade Federal do Rio de Janeiro, a Master's degree from Universidade Federal do Amazonas, and dual Bachelor's degrees in Electrical and Electronics Engineering and Computer Software Engineering.
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ShotQuality
The goal of ShotQuality is to dispel the luck of the game and deliver the expected results based on the accuracy of each shot’s quality, rather than just make or miss. There are over 90 different variables involved in calculating ShotQuality. Overall, we have over 60 NCAA D-1 teams subscribed including the likes of Auburn, Arizona, and StanfordUniversity