The ADCC Championship happens every two years and is considered the Olympics of submission grappling. The 2019 event concluded with a bang in September and crowned some new and some veteran champions. As an exercise in data science, I thought it would be fun to take the results and see how accurate a machine learning model could predict if a match would end in a win or loss. Since we already have the winners, I won't be predicting a future outcome, but instead exploring which characteristics of the matches and the fighters factor into the results. I'll explain the process as I go. Research Question: Can machine learning predict who wins and loses a match at ADCC? This is a two part series, and in this first article I explore the available data for relationships or associations between the different characteristics. In data science, this is where I clean the dataset and explore the statistics. This stage helps me to understand what the data says about the topic and uncovers...
I'm a Data Scientist by day, Jiu Jitsu student by night. BJJ Black Belt at Crazy 88 in Baltimore, MD. 9 years of competing at the IBJJF Masters World Champion = 5 Gold (2020 Brown, 2018 Purple, 2017 Blue, 2015 Blue Weight +Open), 3 Silver, 2 Bronze Open. 2019 IBJJF Adult World Medalist Purple Belt. As a data scientist, I work in computer vision, natural language processing, and geek out over sport stats. This blog brings a data-driven perspective to available player, match, and tournament data.