Hamstring and quadriceps muscles are essential for the performance of athletes in various sport branches. Hamstring muscles control running activities and stabilize the knee during turns or tackles, while quadriceps muscles play an important role in jumping and kicking. Although hamstring and quadriceps muscle strength in athletes can be accurately measured using isokinetic dynamometry, practical difficulties, such as the requirement of nonportable and costly equipment as well as a long period of measurement time, motivate the researcher to predict hamstring and quadriceps muscle strength using promising machine-learning methods. The purpose of this study is to build prediction models for estimating the hamstring and quadriceps muscle strength of college-aged athletes using a support vector machine (SVM). The data set included 75 athletes selected from the College of Physical Education and Sport, Gazi University, Turkey. The predictor variables of sex, age, height, weight, body mass index, and sport branch were utilized to build the hamstring and quadriceps muscle strength prediction models for various types of training methods. The generalization error of the prediction models was calculated by carrying out 10-fold cross-validation, and the prediction errors were evaluated using several performance metrics. For comparison purposes, prediction models based on a radial basis function neural network (RBFNN) and single decision tree (SDT) were also developed. The results reveal that the SVM-based hamstring and quadriceps strength prediction models significantly outperform the RBFNN-based and SDT-based models and can be safely utilized to produce predictions regarding new data with acceptable accuracy.

Maximal oxygen uptake (VO2max) refers to the maximal amount of oxygen that an individual can utilize during intense or maximal exercise. VO2max plays a significant role in sport science, education and research. The direct measurement of VO2max is time-consuming, requires expensive laboratory equipment and trained staff. Because of these disadvantages of direct measurement, numerous VO2max prediction models for a variety of subject groups have been developed. The purpose of this study is to develop new Multiple Linear Regression based on VO2max prediction models for Turkish college students by using physiological and questionnaire variables. The dataset includes the data of 62 (28 females and 34 males) students, ranging in age from 18 to 27 years, from the College of Physical Education and Sports Science at Gazi University. Seven different models consisting of the predictor variables gender, age, weight, height, Perceived Functional Ability scores (PFA-1 and PFA-2), and Physical Activity Rating score (PA-R) have been used to predict VO2max. The performance of the prediction models has been evaluated by calculating their standard error of estimates (SEE's) and multiple correlation coefficients (R's). The prediction model including Gender, Age, Height, Weight, PFA-1 and PAR yields the lowest SEE with 5.14 mL.kg-1.min-1 and highest R with 0.93. It can be concluded that in situations where it is difficult to measure VO2max, the given model with MLR equation can be used to predict the VO2max of college students with acceptable error rates.

This study was conducted to evaluate the jump performance of youth basketball players according to their sport ages. 26 male basketball players (14.1±1.6 year) who participated in the study were divided into two groups of sport ages of 4 and below (≤4) and 6 and above (≥6). The group with sports ages ≤4 consisted of 12 male basketball players with a height of 162±2.56 cm, a body weight of 51.4±3.04 kg, a body mass index of 19.4±0.74 kg/m². The other group with sports ages ≥6 consisted of 14 male basketball players with a height of 155.9±1.98 cm, a body weight of 45.7±1.85 kg, a body mass index of 18.8±0.69 kg/m. All basketball players’ squat jump (SJ) and countermovement jump (CMJ) were measured (Optojump Microgate Bolzano, Italy). The Mann Whitney U test was used to determine whether there were differences between groups in terms of T flighttimes and jump heights. Statistically significant level of p<0.05 was accepted. As a result of the study, no statistically significant difference was observed between the sport ages and SJ and CMJ splashes. In this respect, it can be considered that the Jump performance does not develop in parallel with the training age, and that the jump ability of this cause may be more related to motor skill and ability than the training age.

Physical fitness is a necessary component for daily activities. Measurement of physical activity is essential for determining physical fitness rate. This study aims to develop new prediction models for predicting the physical fitness of Turkish secondary school students by using multiple linear regression (MLR). The datasets comprise data of various number of subjects according to the target variables including the test scores of the 30m speed, 20m stage run, balance and hand-grip (right/left). The predictor variables used to develop the prediction models are gender, age, body mass index (BMI), body fat, number of curl-up and push-ups in 30 seconds. Eight physical fitness prediction models for each target have been created with the predictor variables listed above. The performance of the prediction models has been calculated by using standard error of estimate (SEE). The results show that MLR-based prediction models can be safely used to predict the physical fitness of Turkish secondary school students.Keywords: Physical fitness, multiple linear regression, machine learning, validation.

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The purpose of this study is to examine the correlation between leg power and balance performance in elite wrestlers and injury history. In the research group, there are 18 elite freestyle male wrestlers at the ages of 24.27 ± 3.18 years, with a height of 171.86 ± 5.44 cm and a body weight of 79.27 ± 11.16 kg. Information on the injury history of the athletes’ upper legs for the past year was collected via interviews with the club’s physiotherapist. Laboratory tests to measure performance assessed height, body weight, Y balance and isokinetic leg strength. Data obtained from the study are presented as mean and standard deviation. The test of normality was carried out by the Shapiro-Wilk test. The Pearson Correlation Test was performed for all parameters with normal distribution, and significance level was accepted as p < 0.05. It was found that there is a relationship between the wrestlers’ right leg ratio and hamstring strength and injury history. However, there is no statistically significant relationship between left leg hamstring, quadriceps, ratio, right leg quadriceps, or right and left leg balance performance, and injury history. The resulting data shows that the proportioning between hamstring and quadriceps muscles in freestyle wrestlers’ upper leg strength values is not ideal. This finding provides evidence that injury risk increases with the additional impact of loss of strength.

The aim of this study is eff ect of ballistic warm-up on isokinetic strength, balance and some parameters in male elite freestyle wrestlers. Thirteen elite freestyle wrestlers at the age of 20.15±2.11 yrs, with 174.54±7.14 cm height and 81.67±15.36 kg weight participated in the study. Measurements were performed two diff erent warm-up protocols. Running protocol at submaximal level on the treadmill for 10 minutes was applied for every wrestler. Ballistic Warm-up protocol involved 13 diff erent movements for multi-muscle groups lasting for 10 minutes. Flexibility, speed, agility, balance, hand grip and isokinetic leg strength parameters were measured. Wilcoxon Signed Rank test was performed to fi nd the diff erence between the protocols. Consequently, diff erences were found in fl exibility, right hand grip strength, right posteromedial and posterolateral balance, left posteromedial and posterolateral balance, left and right hamstring and quadriceps strength parameters. Ballistic warm-up protocol can be more eff ective in many parameters, especially strength compared to ordinary warm-up.

Aerobic endurance describes the ability of the body's cardio-respiratory system to perform physical activity for an extended period of time and resist fatigue. Standard tests to determine aerobic endurance involves measuring the maximum volume of oxygen (VO 2 max) an athlete uses up while exercising at maximal capacity. Given that the tests of direct measurement of VO 2 max needs expensive equipment, a great deal of time, and trained staff with expertise, many researchers have attempted to find indirect and simpler ways of predicting VO 2 max based on prediction equations. The aim of this study is to establish new prediction equations for estimating the VO 2 max from gender, age, height, weight, body mass index (BMİ), maximal heart rate (HRmax) and test time (TT) for college-aged students in Turkey. Particularly, 18 students from the College of Physical Education and Sports at Gazi University volunteered for this study. Gender has been used as a common predictor variable in all prediction models. By using different combinations of the rest of predictor variables together with the common predictor variable, twelve VO 2 max prediction equations have been established with the help of Multiple Linear Regression (MLR). The performance of the prediction equations have been evaluated using two well-known metrics, namely standard error of estimate (SEE) and multiple correlation coefficient (R). The results reveal that the regression equation, VO 2 max =-(12.