Exploring Pattern Mining with FCM Algorithm for Predicting Female Athlete Behaviour in Sports Analytics

Abstract

In the changing field of sports analytics, examining and predicting athlete behaviour is crucial for improving performance and reaching the best results. As data analysis becomes more important in sports, using pattern mining methods is critical to discovering meaningful patterns and trends in the large datasets used in sports analysis. This study proposes a Performance Optimization Framework for Female Athletes (POFFA) that explores the field of sports analytics to examine how pattern mining methods, explicitly using the Flexible Fuzzy C-Means (FFCM) algorithm, can help predict female athlete behaviour. This study aims to offer detailed insights into behavioural patterns that can help improve coaching techniques, training schedules, and performance improvement procedures by concentrating on female athletes. The study intends to provide a deep understanding of female athlete behaviour and game dynamics using the FCM approach, known for its effectiveness in dealing with complex, sparse, multi-objective optimization difficulties. The results of this study can enhance the field of sports analysis by providing detailed insights into the behavioural patterns displayed by female players in many sports categories. By combining pattern mining methods with the FCM algorithm, this study introduces a thorough process for examining and predicting athlete behaviour in sports analytics. In the end, the results of this study have practical significance for coaches, trainers, and sports professionals, offering helpful advice for improving the performance of female athletes in competitive sports settings. By using data-driven insights from pattern mining analyses, individuals in the sports business can improve their plans and interventions to enhance female athletes' athletic potential and overall performance results.