FightTracker: Real-time predictive analytics for Mixed Martial Arts bouts
Abstract
Mixed martial arts (MMA) has been one of the fastest-growing sports in recent years and has become a mainstream sport on the global stage. The growth of MMA has been driven by the Ultimate Fighting Championship (UFC), which is currently the largest MMA promotion organization in the world. However, data collection and statistical modeling in MMA are still in their infancy. We developed FightTracker, a data-driven solution that delivers real-time predictions for UFC fights. We first conducted regression analyses on the data provided by the UFC and MMA Decisions and built two predictive models of UFC fight outcomes. One model predicts the judges' majority score by round while the other predicts whether the red fighter will win the fight or not in 3-round fights that go beyond the second round (53% of all UFC fights). Both models use in-round fight statistics as explanatory variables and achieve 80% accuracy. We then designed an R shiny app that delivers these two predictions in real-time based on the ESPN live data. This information is valuable for fans, coaches, athletes, and especially bettors. Indeed, a live betting strategy based on FightTracker proved to generate large profits over an 8-week period against the bookmaker Unibet (90.17% ROI).
Keywords
Cite
@article{arxiv.2312.11067,
title = {FightTracker: Real-time predictive analytics for Mixed Martial Arts bouts},
author = {Vincent Berthet},
journal= {arXiv preprint arXiv:2312.11067},
year = {2026}
}
Comments
This paper reported that our predictive tool, FightTracker, generated large profits over an 8-week period against the bookmaker Unibet (90.17% ROI). New analyses over a much longer period show substantially lower performance. Because the original result may be misleading, we request withdrawal of the preprint