BJJ Data by Riggs

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

With the IBJJF's new seeding system at tournaments, we can now analyze the accuracy of fighter seeds and its ability to predict who will win a division. The fun in this is we get to see where upsets occur. What makes sports tournaments exciting is when an underdog comes out on top or a heavy favorite is able to win again and again.   Highlights: There were 541 upset matches compared to 639 correctly predicted victories at the IBJJF No Gi Pan. For adults divisions, there were 329 correctly predicted and 267 upsets. IBJJF seeding system has a lower predictive accuracy than collegiate wrestling The most upsets occurred at purple belt More upsets occurred in the Medium Heavy division than other weight classes The Masters 3 division had more upsets than victories More upsets occurred on Mat 3 than any other mat The female divisions had almost as many upsets as victories More upsets occurred on Sunday than Saturday Most upsets occurred by a fighter that was 4 seeds lower

First post on the blog - Fighter Rankings. Why is this important? Rankings are used for fighters to get a data-driven sense of their standings against other fighters at major tournaments. IBJJF now has a seeding system going into tournaments and rankings will increasingly play an important role in tournament brackets and thus outcomes. Looking only at the data also removes any bias or favoritism towards an individual team or athlete, as well as identifies hidden gems not at the forefront of the media. Fighter rankings are heavily weighted for their performance at major IBJJF tournaments and does not include other tournament venues. Methodology Fighter Rankings are defined as rankings based on tournament results, i.e. receiving a gold, silver, or bronze medal at an IBJJF tournament. I used the IBJJF Ranking system and collected results for each major tournament going back three years. Results will be posted twice a year, directly after the Gi World Championships an