Abstract:
Anthropometric measurements are generally used to determine and predict achievement in
different sports. An athlete’s anthropometric and physical characteristics may be an important
precondition for successful participation in any given sport. Further, anthropometric profiles
indicate whether the player would be suitable for the competition at the highest level in a specific
sport. The main aim of this study is to measure accuracy of the given classification algorithms.
Each and every data mining algorithm provides separate prediction accuracy details. This study
investigates an integration of four data mining algorithms (Naïve Bayes, Decision Trees, Random
Forest, and Support Vector Machine) and an Ensemble approach (bagging, boosting, and stacking).
In this research, Push up, Sit-up, Cardiac respect endurance, flexibility, agility, Height, Weight,
Upper Arm Relaxed Girth, Fore Arm Girth, Chest Girth, Wrist Girth, Waist Girth, Thigh Girth,
Calf Girth, Angle Girth, Acromial Radial Length, Radials lion Dactyl ion, Foot Length, body
composition, fore arm length, hand length, Explosive power, Breath hold in time, resting heart rate,
volume of oxygen, Force vital capacity, force explotaty volume in 1 second, Upper arm radius, and
Leg Length are used. In this paper, classification performance of these models has been evaluated
using Accuracy, Precision, Recall, F-Measure, MCC, ROC Area, PRC Area, Root Mean Squared
Error (RMSE) and Mean Absolute Error (MAE) etc. The results of this study indicated that SVM
was the most suitable algorithm to classify Anthropometric measurements of tennis players.
