Championship Sports

The purpose of this research was to identify the factors affecting the development of championship swimming in Yazd, Iran from the perspective of coaches, experts, and managers. 123 experts in the field of swimming were randomly selected as the research sample, and a researcher-developed questionnaire was used for data collection. Data analysis was performed using a one-way analysis of variance and the Kruskal-Wallis test. The results showed that all coaches, experts, and managers had a consensus on improving the level of scientific and technical knowledge of coaches, controlling and monitoring the activities of coaches, finding talent using scientific and specialized methods, and establishing swimming schools, facilities, and equipment. Therefore, these factors were found to be the most important and effective factors in developing the championship swimming in Yazd province. 

The main purpose of this study was to provide a framework of effective components in Iranian championship sports with a data mining approach. The research method was qualitative. The advanced search was performed in the general framework of factors affecting the sports industry and development and integration with data-driven technologies. Based on the literature review, 15 frameworks in the field of sports and 13 frameworks for the development and integration of the sports industry with technology were identified. After the data analysis, a researcher-developed framework for the championship sports industry for the use of data-driven technologies and data mining was presented in three parts. In the first part, nine influential factors including athletes/champion teams, leagues/clubs, stadiums/sports venues, fans/spectators, brands, media, government, academic/research institutions, and technology companies, were identified. In the second part, a strategic plan based on the development and integration of sports industry with data-driven technologies and data mining was presented, which includes four stages: Identifying and selecting talents, pre-game and match preparation, in-game and match activities, and post-match and match analysis. All data-driven activities and data mining in the first and second sections were performed by the IBM data science analysis methodology presented in the third section. Then, a conceptual framework was provided to 7 experts and their opinions were collected through semi-structured interviews and focus group methods. This conceptual framework enables sports managers to plan for their organization and adopt appropriate strategies using data-driven technologies and data mining.