In the previous article , we explored the characteristics of the 2019 ADCC tournament. Our initial data had match details, which provided us insight into how matches were won, trends among the different weight classes, the most successful fighters, and the top teams. I added player data to the mix to dive deeper into what goes into a successful run at ADCC. From that exploration, we learned: A fter the the initial analysis, I ran the data through various models to see how accurately the machine learning tools could predict a win or loss between fighters. Main Finding: With the available data, I can predict the win/loss result of a match between two fighters 70% of the time, or 7 out of 10 matches. If you follow the sport you know upsets occur. In comparison to collegiate wrestling, the seeding for wrestlers produces a predictive accuracy of 67% (as we saw in another article here in the blog). Why not 80 or 90% of the time? The Primary Culprit: A lack o
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.