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Vincent Berthet

Publications and source records attributed to Vincent Berthet.

2 recordsLinked to original sources

Improving MMA judging with consensus scoring: A Statistical analysis of MMA bouts from 2003 to 2023

Boxing and MMA have a longstanding issue with judging, as evidenced by frequent controversial decisions. Like boxing, MMA bouts are scored following the 10-Point Must System, by which judges score each round individually. In the present study, we compare the performance of two methods of round scores aggregation: the standard method (aggregation over rounds and then over judges) and an alternative method known as consensus scoring (aggregation over judges and then over rounds). For that purpose, we conducted a statistical analysis of 4,129 MMA bouts that went to decision between 2003 and 2023. Our findings indicate that standard and consensus scoring yield the same result in 97.53% of the bouts. Noteworthy, this percentage may be underestimated, as judges may not always score each round individually; instead, they may balance their scores across rounds. Regarding the bouts in which the two methods disagree, the outcome of consensus scoring aligns more with the opinion of the fans (48.96%) than that of standard scoring (43.75%). This results from the fact that consensus scoring can counteract the impact of an incorrect score (especially a 10-8 score) given by a judge in a specific round. From a cost-benefit perspective, we argue that state commissions should consider consensus scoring in MMA as an alternative to standard scoring as it can avoid controversial decisions while it does not imply a major change in the judging system.

physics.soc-ph

FightTracker: Real-time predictive analytics for Mixed Martial Arts bouts

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).

stat.AP