Introduction. Summer camps are the most important form of organised leisure activity for children and adolescents, and their popularity has been increasing year after year. Providing tourism services for this group of recipients constitutes a big challenge for the organisers because they have to satisfy the expectations of not only the participants but also their parents. Hence, the aim of the study was to obtain data making it possible to estimate the degree of convergence between the needs of summer camp participants, their parents’ needs, and the tourism product, that is summer camp. Materials and methods. The study involved one hundred participants of a summer camp, aged 11-17 years. A diagnostic survey carried using direct interviews and questionnaires. The data have been processed and analysed statistically by means of Excel and Statistica v.12; a significance test was used for comparing two proportions at the significance level α = 0.05 (with p < α indicating the presence of statistically significant differences and p ≥ α indicating a lack of statistically significant differences). Results. The children of educated mothers participated in summer camps significantly more often than children of mothers with vocational education (p = 0.0115) and secondary education (p = 0.0422). When selecting the summer camp, most respondents (44.57%) paid attention to the degree of correspondence between the programme and their interests. The camps that were the most popular were sport summer camps (41.30%). Boys chose survival summer camps more often than girls (p = 0.0360) whereas girls preferred active and sailing summer camps (p = 0.006). The most attention in a summer camp was paid to the staff. Conclusions. When choosing a summer camp, children and parents pay special attention to whether or not the programme is rich in attractions; however, it is the staff that conditions positive emotions of the participants as well as their good memories. Organisers know how important the staff are and that they are the warrant of the summer camp’s success. The results presented confirm that preparing a summer camp offering requires much work and involvement because one must satisfy the expectations of both participants and parents, who use different criteria of camp evaluation.

IntroductionSport psychology researchers have been investigating different means and forms of exercises to better support coaches and scouts in recruitment, talent development (Sands and McNeal 2000), and also to enhance athletic performance . Most investigations are based on the assumption that psychological characteristics should be considered as important determinants of athletic performance and success (Smith and Christensen 1995). Recently researchers have made an effort to reveal the most relevant psychological characteristics and traits of elite athletes in different sport settings (Smith, Schitz, Smoll and Ptacek 1995).

The Institute of Sport Science at the University of West-Hungary, and the FH-Joanneum -together with students majoring in sport science -started a collaborative international research aimed at assessing the sport-specific skills of football players. The study closely monitored football players -from leagues at different levels -in terms of choice reaction time, short and long pass skills and agility skills with and without a ball. The most modern device available, the SmartSpeed -a photoelectric stopwatch system -was used for the measurements. The data obtained by the applied internationally standardized tests can be compared in terms of players from leagues at different levels. The study occurred using correlation analysis (p<0.05), and we used linear regression to compare laterality. The hypothesis that teams playing in higher league are significantly more successful in terms of the evaluated technical parameters has not been verified in all cases. There are interesting results regarding the differences between moving to the opposite side with and without ball. The results of the Hungarian team (N=97 players) can be compared to those of the Austrian and international players.

The aim of this study is to determine body composition (BC) factors that influence agility among adolescent soccer players (N=66), in U14, U16, U18 soccer teams of the Topola Sport Club. Agility tests (Dribbling test, Illinois test), Inbody 720 instrument measuring the BC, and Oxa Starter infrared timing gate instrument measuring speed the agility were used. Data are processed by the IBM SPSS Statistics 25 software. In addition to descriptive statistics, a correlation matrix analysis, linear regression and one-way analysis of variance (ANOVA) were also applied (p<0.05). A moderate correlation between agility with a ball and without a ball (r=0.595) is determined as well as between agility and BC parameters: Skeletal Muscle Mass, Intracellular Water Mass, Protein Mass, Mineral Mass (r=-0.453, -0.454, -0.453, -0.417, respectively) while the correlation between agility and Height, Mass, Body Mass Index, Right Leg Lean Mass, Left Leg Lean Mass, Extracellular Water Mass is less than moderate (r=-0.318, -0.329, -0.276, -0.332, -0.330, -0.374, respectively). A slight correlation is determined between agility with a ball and BC variables. No correlation was determined between Agility and Body Fat Mass. The influence of BC on agility is lower than expected. Using the scientific based approach and measurements, a complex exercise plan can be made for players and in this way young soccer players can be trained by using tailor-made and post-specified coaching methods.

Data measured in the U15–U19 age groups at the football academy were analyzed during the research (N = 70). These data comprised Body Mass, Height, YYIR1, 30 m running, FMS, and SLJ, Locomotor parameters using the 6-week averages of Catapult OptimEye S5, mechanical performance parameters. The players did well in the motoric tests: YYIR1 (M = 2155, SD = 311), 30 m (M = 4.34, SD = 0.26), and SLJ (M = 2.28, SD = 0.18), and the different age groups underwent dynamic improvement. The young soccer players ran 19,552m players ran 568, 298, and 97 m in the moderate-, high-, and sprint-intensity zones. The athletes’ maximum speed was M = 26.72 km/h (SD = 1.74). The differences between the age groups were justified statistically in each case.