Data mining for psychological profiling of track and field athletes and runners

Abstract

Psychological factors in sports have been widely studied in scientific literature. However, only a few studies have used data mining techniques for athletic profile analysis. The main goal of this study was to analyze motivation, self-confidence, flow, and psychological skills in athletics to build differentiated profiles through clustering techniques. The sample size was 470 participants (ages 14–70 years old; M = 32.1; SD = 13.5). The Sports Motivation Scale (SMS), Task and Ego Orientation in Sport Questionnaire (TEOSQ), Self-confidence in Sport Questionnaire (CACD), Flow Dispositional Scale-2 (FDS-2), and Psychological Inventory of Sport Performance (IPED) were used to analyze the psychological profile of the sample. A data clustering analysis was carried out to check the study’s purpose. Results show different behavior patterns according to specific profiles. Similarly, there have been differences between men and women, online and face-to-face participants, federated athletes and runners, categories, or sports disciplines. In conclusion, the understanding of each athlete’s psychological profile is essential to improve his/her performance. The results of this study could be used to implement changes and adjustments in athlete psychological training to run several intervention programs that focus on each group’s needs.