Since its inception, FightMetric has committed to advancing the analysis of Mixed Martial Arts with its own research and by allowing others to perform analysis on our vast database. To this end, we have created the Academic Access Program to grant a select group of academics and
researchers access to our data for their analysis. Please note that admission to the Academic Access Program is currently limited to graduate level work intended for publication in peer-reviewed journals. Please Contact Us to apply for the Academic Access Program.
Performance Evaluation and Favoritism: Evidence from Mixed Martial Arts
Paul Gift, Pepperdine University, Graziadio School of Business and Management
This paper investigates various types of bias and favoritism that may be present in the performance evaluations of state-licensed and state-selected judges for mixed martial arts (MMA) events. Using detailed fighter performance statistics, I analyze round-by-round scoring decisions for major MMA events held in Nevada and California from 2001-2012. Findings do not support hypotheses that judges favor title holders or disfavor fighters given point deductions, but there is support for bias towards heavy favorites and the fighter who won the previous round. Findings provide non-experimental support for possible anchoring and recency bias in a relatively opaque decision environment involving substantial complexity. The results also have strategic implications for MMA fighters and coaches as well as the athletic commissions that license and select the judges.
A Winning Smile? Smile Intensity, Physical Dominance, and Fighter Performance
Michael W. Kraus, University of Illinois, Urbana-Champaign & Teh-Way David Chen, University of California, Berkeley
The smile is perhaps the most widely studied facial expression of emotion and in this paper we examined its status as a sign of physical dominance. We reasoned, based on prior research, that prior to a physical confrontation, smiles are a nonverbal sign of reduced hostility and aggression, and thereby unintentionally communicate reduced physical dominance. Two studies provided evidence in support of this prediction: Study 1 found that professional fighters who smiled more in a pre-fight photograph taken facing their opponent performed more poorly during the fight relative to their less intensely smiling counterparts. In Study 2, untrained observers judged a fighter as less hostile and aggressive, and thereby less physically dominant when the fighters’ facial expression was manipulated to show a smiling expression relative to the same fighter displaying a neutral expression. Discussion focused on the reasons why smiles are associated with decreased physical dominance.
Aggression in Mixed Martial Arts: An Analysis of the Likelihood of Winning a Decision
Trevor Collier, University of Dayton, Andrew L. Johnson, Texas A&M, John Ruggiero, University of Dayton
Within the last decade, mixed martial arts has become one of the most popular sports worldwide. The Ultimate Fighting Championship is the largest and most successful organizations within the industry. In the United States, however, the sport is not sanctioned in all states because some politicians view the sport as too violent. The sport consists of many fighting forms and, unlike boxing, winning a decision requires judging in multiple facets including wrestling, boxing, kick boxing and jiu-jitsu. In this study, we estimate the likelihood of winning a decision in the Ultimate Fighting
Championship. Using data on individual fights, we estimate the probability of winning based on fighter characteristics. We emphasize power strikes as it relates to aggression to determine the likelihood of winning. Our results indicate that knockdowns, and damage inflicted are all statistically significant determinants of winning a fight and have the largest marginal effect of influencing judges decisions.
Predicting Outcomes of Mixed Martial Arts Fights With Novel Fight Variables
Jeremiah Douglas Johnson, University of Georgia
In this study, I attempt to forecast the win/loss outcomes of mixed martial arts bouts with fight data. Both basic ‘count’ variables and newer, constructed variables are considered. These novel measures are then used to predict wins and losses using a logistic regression model, and this model is compared to baseline models in terms of predictive ability. The final model contains both ‘count’ variables and constructed variables and is found to have significantly greater predictive ability than baseline models. Cross-validation and discriminant analysis confirm these results.
Fightnomics: The Hidden Numbers in Mixed Martial Arts and Why There's No Such Thing as a Fair Fight
Reed Kuhn, with Kelly Crigger
Fightnomics quantifies the underlying drivers of the world’s most exciting and fastest growing sport through deep analysis of Mixed Martial Arts (MMA) competition. Part Freakonomics and part Moneyball, Fightnomics is a statistical spotlight on the Ultimate Fighting Championship (UFC) and the fighters who compete in the Octagon. Does size matter? Is the Southpaw Advantage real for MMA? Is it better to be young or experienced in a fight? How is the UFC Tale of the Tape lying to us? What makes a strike significant? What about Ring Rust, Octagon Jitters, or the Home Cage Advantage? Just how accurate are betting odds? Theories about how MMA works get put to the test with a little bit of science, and a whole lot of numbers. Fightnomics is the deepest and most complete analysis to date of historical UFC data that answers common, yet hotly debated questions about the sport. The fight game will never quite look the same once you've learned what really matters in a cage fight, and even a few surprising things that don't.