Identifying performance patterns in professional mixed martial arts: An exploratory data approach - Qasthalani, Muhammad; Maulana, Ahmad; Amelia, Bonita (UIR Press, 2025)
Background: Mixed martial arts (MMA) performance depends on the interaction of physical, technical, and tactical factors. While prior studies often examined these elements separately, few have analyzed how physical attributes relate to performance metrics in an integrated framework. This gap, in contrast to the growing use of analytics in other sports, limits data-driven insights for optimizing UFC training and strategy. Research Objectives: This study aimed to investigate performance patterns among UFC fighters using Exploratory Data Analysis (EDA), with a focus on the relationships between physical characteristics and technical performance indicators. Methods: A dataset of 4,111 UFC fighters was analyzed across 18 variables, encompassing physical attributes (e.g., height, weight, reach) and performance metrics (e.g., striking accuracy, takedown success, submission attempts). EDA techniques, including descriptive statistics, correlation analysis, and data visualization, were applied to identify patterns and summarize key characteristics. Finding/Results: Takedown accuracy and takedown defense were moderately correlated, suggesting an interdependence between offensive and defensive grappling skills. However, most associations between physical traits and performance outcomes, such as height and total wins, were weak, indicating that physical attributes alone are insufficient to predict success. Observations on stance effectiveness and standout fighters offered illustrative but non-generalizable insights. Conclusion: This study demonstrates the utility of EDA as a foundational tool for uncovering patterns in MMA performance. While limited in inferential scope, the findings provide preliminary guidance for coaches and analysts to design evidence-based training strategies. Future research should integrate psychological, contextual, and opponent-based data to develop more comprehensive predictive models for combat sports performance.