Data mining for psychological profiling of track and field athletes and runners
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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1518468/full |
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| author | Cristina Sanz-Fernández José Luis Pastrana Brincones Julen Castellano Rafael E. Reigal Garrido Diego Arvizu-Lozoya Antonio Hernández-Mendo Verónica Morales-Sánchez |
| author_facet | Cristina Sanz-Fernández José Luis Pastrana Brincones Julen Castellano Rafael E. Reigal Garrido Diego Arvizu-Lozoya Antonio Hernández-Mendo Verónica Morales-Sánchez |
| author_sort | Cristina Sanz-Fernández |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-53336138ecc7434781d8ba30d7c7b4a4 |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-53336138ecc7434781d8ba30d7c7b4a42025-01-30T16:11:16ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-01-011510.3389/fpsyg.2024.15184681518468Data mining for psychological profiling of track and field athletes and runnersCristina Sanz-Fernández0José Luis Pastrana Brincones1Julen Castellano2Rafael E. Reigal Garrido3Diego Arvizu-Lozoya4Antonio Hernández-Mendo5Verónica Morales-Sánchez6Department of Education Sciences, Universidad de La Rioja, Logroño, SpainDepartment of Social Psychology, Social Work and Social Services and Social Antrophology, Universidad de Málaga, Málaga, SpainDepartment of Physical and Sport Education, Universidad del País Vasco, Vitoria-Gasteiz, SpainDepartment of Social Psychology, Social Work and Social Services and Social Antrophology, Universidad de Málaga, Málaga, SpainUniversidad Autónoma de Nuevo León, San Nicolás de los Garza, MexicoDepartment of Social Psychology, Social Work and Social Services and Social Antrophology, Universidad de Málaga, Málaga, SpainDepartment of Social Psychology, Social Work and Social Services and Social Antrophology, Universidad de Málaga, Málaga, SpainPsychological 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1518468/fulltrack and fielddata miningclusteringmixed methodspecialties |
| spellingShingle | Cristina Sanz-Fernández José Luis Pastrana Brincones Julen Castellano Rafael E. Reigal Garrido Diego Arvizu-Lozoya Antonio Hernández-Mendo Verónica Morales-Sánchez Data mining for psychological profiling of track and field athletes and runners Frontiers in Psychology track and field data mining clustering mixed method specialties |
| title | Data mining for psychological profiling of track and field athletes and runners |
| title_full | Data mining for psychological profiling of track and field athletes and runners |
| title_fullStr | Data mining for psychological profiling of track and field athletes and runners |
| title_full_unstemmed | Data mining for psychological profiling of track and field athletes and runners |
| title_short | Data mining for psychological profiling of track and field athletes and runners |
| title_sort | data mining for psychological profiling of track and field athletes and runners |
| topic | track and field data mining clustering mixed method specialties |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1518468/full |
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