The aim of this study is to investigate 8 weeks Thera-Band trainings' effects on male swimmers' 100 m freestyle swimming performance.The study group is created by 20 (n = 20) licenced male athletes that had trained at least 3 days in a week and have been active in swimming sport at least 3 years in Gebze Genclerbirligi Swimming Club 20 (n = 20). Athletes were divided into experiment group (n = 10) and control group (n = 10) randomly. Training programme was applied to the study group for 55-60 minutes for 3 days on alternate days and times when the club does not have swimming training. 12 different Thera-Band trainings were applied for 40-45 mins and each set was 15 minutes.Mann-Whitney U test was used to analyze differences between groups and Wilcoxon signed rank test was applied for analyzing the differences of intra-groups. SPSS 21.0 Statistics package software was used for statistical analyzes. The results show that there are no significantly differences between experimental group's and control group's post test results. (p>0.05). Statistically significant differences are found as a result of intra-group comparison of the experimental group's pre-test and post-test results (p<0.05).Depending on the results obtained after reviewing the literature, it can be concluded that Thera-Band training is effective on the performance improvement of swimmers aged 13-15 years.

Bu makale çalışmasının verileri Kocaeli Üniversitesi Sosyal ve Beşeri Bilimler Etik Kurulu'nun 16.09.2019-E68832 tarih ve sayılı etik kurul onayı sonrasında toplanmıştır.

The revolution of big data has also affected the area of sports analytics. Many big companies have started to see the benefits of combining sports analytics and big data to make a profit. Aggregating and processing big sport data from different sources becomes challenging if we rely on central processing techniques, which hurts the accuracy and the timeliness of the information. Distributed systems come to the rescue as a solution to these problems and the MapReduce paradigm is promising for large-scale data analytics. In this study, we present a big data architecture based on Docker containers in Apache Spark. We demonstrate the architecture on four data-intensive case studies including structured analysis, streaming, machine learning methods, and graph-based analysis in sport analytics, showing ease of use.

No abstract

In this study, it was aimed to investigate the relationship between burnout levels and job satisfaction of sports facilities managers. The universe of the research is composed of sports facility managers who are active in 2019 in the Istanbul region and actively functioning in 2019, and the sample is composed of 126 sports facility managers who participated in the research in a random and voluntary manner. A questionnaire including demographic information form, Minnesota Job Satisfaction Scale and Maslach Burnout Inventory was applied to the participants. Among the burnout subscales, the level of emotional exhaustion is low, the personal accomplishment subscale score is high, and the depersonalization subscale is medium. It was determined that the job satisfaction of the participants was high according to the job satisfaction total scores. There is a strong positive correlation between burnout and burnout subdimensions (p <0.05, β = 0.664, β = 0.878, β = 0.444). There is a strong positive relationship between job satisfaction and job satisfaction sub-dimensions (p <0.05, β = 0.917, β = 0.909). There was no statistically significant relationship between job satisfaction subscales and burnout subscales (p> 0.05). The relationship between total burnout score and job satisfaction total scores was not statistically significant (p> 0.05). As a result, there was no statistically significant relationship between burnout and job satisfaction.