In this study, it was aimed to examine the effect of a core training program that was applied on football players on some performance parameters. In total, 40 football players, aged between 18 and 24 years old, who regularly trained in football and were from various amateur football teams participated: 20 athletes in the training group and 20 athletes in the control group. It was taken the pre-test measurements of the athletes’ vertical jump, 30-m speed, agility, and flexibility; after the 6-week core training program, which was applied three days a-week, and it was taken the post-test measurements of the athletes. The training group applied the core training in addition to football training for 6-week, whereas the participants in the control group did not apply any training program other than their ongoing football training. It was used the SPSS 22 statistics program to evaluate the data and Shapiro-Wilk test to determine the normality distribution of the data. Owing to the normal distribution of the data, it was used a paired t-test to compare the pre-test and post-test values within the groups and accepted the confidence interval for statistical processes as p < 0.05. It was found a statistically significant difference in the vertical jump pre-test and post-test values of the training group (p < 0.05). In the control group, there was no statistically significant difference in the vertical jump pre-test and post-test values (p > 0.05). It was found a statistically significant difference in the 30-m speed pre-test and post-test values of the training group (p < 0.05). In the control group, there was no statistically significant difference in the 30-m speed pre-test and post-test values (p > 0.05). It was found a statistically significant difference in the agility pre-test and post-test values of the training group (p < 0.05). In the control group, no statistically significant difference was found in the agility pre-test and post-test values (p > 0.05). Considering the in-group flexibility pre-test and post-test comparisons, a statistically significant difference was found in the flexibility pre-test and post-test values of the training group (p < 0.05). In the control group, there was no statistically significant difference in flexibility pre-test and post-test values (p > 0.05). Based on the results of the present research, the 6-week core training program that was applied to football players improved the performance of vertical jump, 30-m speed, agility, and flexibility.

Bu çalışmanın amacı yüksek şiddetli interval antrenmanın genç futbolcularda çeviklik, sürat ve aerobik performans üzerine etkisini incelemektir. Çalışmaya deney grubu (n:10, yaş ort:19,65±0,51) ve kontrol grubu (n:10, yaş ort:18,88±0,62) olmak üzere 20 genç futbolcu katılmıştır. Çalışmada deney grubu normal futbol antrenmanlarına ek olarak haftada 3 kez yüksek şiddetli interval antrenman programını 7 hafta yapmıştır. Kontrol grubu ise sadece normal futbol antrenmanlarına devam etmiştir. Çalışmada ön test son test olarak proagility çeviklik testi, 30 metre sürat testi ve Yo-Yo aralıklı koşu testi uygulanmıştır. İstatistiksel hesaplamalar SPSS-20 paket programıyla yapılmış ve güven aralığı p<0,05 olarak kabul edilmiştir. Yapılan bu çalışmada deney grubunun sürat ve Yo-Yo aralıklı koşu testi özelliklerinin ön test son test karşılaştırmalarında son test değerlerinin istatistiksel anlamda daha iyi olduğu görülmektedir (p<0,05). Deney grubunun çeviklik ön test son test değerlerinde ise bir faklılık gözlenmemiştir (p>0,05). Kontrol grubunun ön test son test sürat ve çeviklik özelliklerinde bir farklılık bulunmazken (p>0,05) Yo-Yo aralıklı koşu testinde ise son test verileri anlamlı bir şekilde artış göstermiştir (p<0,05). Sonuç olarak yüksek şiddetli interval antrenman programlarının genç futbolcularda sürat ve aerobik dayanıklılığın bir göstergesi olan koşu mesafesini artırdığı görülmüştür.

This study, which included 50 young amateur male footballers aged between 16 and 18, aims to compare certain physical and performance parameters of young football players based on positions. Based on their positions, the footballers were divided into two groups as “central” and “wing” positioned players. The body composition, anaerobic power, speed and flexibility values have been determined using field tests. The data were analyzed using SPSS 22 statistics program. Shapiro-Wilk test was used to determine the normality distribution of the data. The independent t-test was used because of the normal distribution of the data. Confidence interval for statistical processes was considered to be p < 0.05. The average values and comparison results obtained from the physical parameters of the central and wing players have showed that there is a statistically significant difference between central and wing players in terms of height (cm), body weight (kg), fat mass (kg) and lean mass (kg) parameters (p < 0.05), whereas there is no statistically significant difference in age (years) and fat rate (%) parameters (p > 0.05). The average values and comparison results obtained from the physical parameters of the central and wing players have showed that there is a statistically significant difference between central and wing players in terms of vertical jump (cm), 30-m sprint (s) and anaerobic power (kgm/sec) parameters (p < 0.05), whereas there is no statistically significant difference in 10-m sprint (s) and flexibility (cm) parameters (p > 0.05). The results of this study showed that, in terms of physical parameters, the height, body weight, fat mass and lean mass values of the central players were statistically higher than the wing players. In terms of performance parameters, the vertical jump and 30-m sprint performance of the wing players were found to be statistically better than the central players, while the anaerobic power values of the central players were found to be statistically higher than the wing players.

The fact that football is watched with interest all over the world and the economic power of football, which is increasing day by day thanks to the ardent fans of football, brings with it the astronomical wages of footballers. Since the early twenties, the level of satisfaction of professional football players, who have incomes that their peers cannot even imagine, have been ignored due to the money they earn, and has not been questioned much. This study was conducted to evaluate the job satisfaction levels of professional football team players in the Central Anatolia Region. The universe of the research, which was organized in accordance with the descriptive research model, consisted of approximately 384 football players in the professional football teams in the Central Anatolia Region in the 2016-17 season, while the sample of the study consisted of a total of 158 football players in the relevant universe. In the study, data were collected through the job satisfaction scale developed by Balcı (1985) and consisting of 34 items. The scale was adapted to the study group by taking expert opinions. In the statistical analysis of the data, arithmetic means, variance analysis in determining the differences, and Cronbach Alpha coefficients were used for the reliability of the data collection tool. While no significant difference was found in any sub-dimension according to the variables of "educational status, length of service in the football sector and the position played", there were significant differences according to the variables of "age, length of service in the team played and the number of matches played in a season". As a result, it has been seen that the job satisfaction of professional football players differs according to demographic variables, professional football players have the highest satisfaction from the job quality sub-dimension, and the lowest satisfaction from the development opportunities sub-dimension.

In this paper, we will present an approach to visualizing arbitrary relational database contents in the form of object graphs via the World Wide Web. The focus is on the relationships between the datasets rather than on the data itself. The tool allows definition of different node types representing the datasets and edges representing the foreign keys and relationship tables in the database schema. Each node type has a label, and optionally a short description and a user definable image associated with it. The information for these fields can be extracted 1 : 1 from the corresponding database tables or otherwise be aggregated from different tables. Along the edges, it is possible to navigate through the content of the database. At any time, exactly one node represents the center of the object graph. Starting from this graph, edges and other nodes down to a user-definable depth n are visualized. The depth n may vary along different node and edge types, so that it is possible to customize the representation of the object graph. The graphical representation of arbitrary database contents has been of great help to us. In addition to using it in the initial application area, we intend to use it in some other areas we had left unconsidered. From these, we can infer a number of suggestions as to how to improve our tool and make it more universal.