Jabbr a finalist at prestigious MIT Sports Analytics Conference

Mason & Thomas in front of their poster at MIT Sloan in 2026
Mason duBoef (left, Jabbr) and Thomas Romeas (right, INS Québec) in front of their research poster at MIT Sloan 2026.
Mason presenting the research at MIT Sloan in 2026
Mason duBoef preseneting the research at MIT Sloan 2026.

A research paper by our long-time research intern, Mason duBoef, and our CEO, Allan Svejstrup, has been selected as 2026 "Research Paper Finalists" at the MIT Sloan Sports Analytics Conference (SSAC26). The MIT SSAC is one of the most prestigious sports analytics conferences in the world and highly selective. Out of nearly 200 submissions, Jabbr's research paper was selected as 1 of only 7 papers to present at the conference's main track.

Co-authored with the Quebec National Institute of Sport (INS Québec), our paper "Interpretable Prediction and Large-Scale Analysis of Judging in Professional Boxing" addresses boxing's long standing issue with subjective scoring. We analyzed 7,323 rounds of professional boxing, extracting stats using our DeepStrike AI, and then mapped these round-by-round statistics to judges' scorecards. In doing this, we were able to put numbers and facts on arguably boxing's most critical and subjective aspect: scoring.

We see this paper as a milestone for the history books. Combat sports have for millennia relied on human subjective evaluation for scoring fights, introducing controversy and dissatisfaction with unfairness. For the first time, a major study has demonstrated that computer-vision AI can score combat sports matches to a level comparable with high-level professional judges' subjective opinion-based scoring, but in a manner that is fully transparent, reproducible, and guaranteed to be free of unwanted biases.

At Jabbr, we're incredibly proud of this research progress and excited to share it with the world of sports at MIT SSAC26.

Example of Jabbr's Judge Scorecard AI Prediction.
Example of Jabbr's Judge Scorecard AI Prediction.

Our "Judge Scorecard Predictions" can also inform coaches and boxers specific performance metrics that most strongly drive scoring decisions. This tool will eventually guide how fighters train, how corners coach, and how governing bodies think about scoring for the future.

We're excited to debut this feature in our plug-and-play camera system, and see how the boxing world uses it to learn what it truly means to win a fight.

To a better future for combat sports.

Jabbr is building AI-powered camera systems and analytics for combat sports — join the waitlist.