Abstract:
Anthropometric measurements are generally used to determine and predict achievement in different sports. An athlete’s anthropometric and physical characteristics may perform 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. Recently, more researches has been carried out on Sport Data mining. This study
was conducted as three parts. In first part it proposes a visualization approach to identify most
suitable sport for beginners using data mining and anthropometric profiles. Here it proposes a
clustering based approach and applies a spatial clustering technique called the Spherical Associated Keyword Space, which projects clustering result from a three-dimensional sphere to a
two-dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach
has proved the effectiveness of clustering results. In the second part of the research, researcher
describes and analyses accuracy by comparing K-means and Expectation-Maximization algorithms. Here the most affected attributes were selected using WEKA ranking method. Various
sizes of Anthropometric measurements data sets were used to evaluate the algorithms and end of
this section K-mean achieve highest accuracy rate. In the final part, where its aim is to measure
accuracy of the given classification algorithms, prediction accuracy details of each and every
data mining algorithm recorded separately. This study integrates four data mining algorithms
(Na¨ıve Bayes, Decision Trees, Random Forest, and Support Vector Machine) and an Ensemble
approach (bagging, boosting, and stacking). In this part Anthropometric measurements of the
tennis players are used and 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). Here for this, SVM is proposed as the most
suitable algorithm to classify Anthropometric measurements of tennis players. Considering
the entire research, researcher propose three different approaches to analyse Anthropometric
measurements. Here it suggest to develop a sport selection algorithms using Anthropometric
measurements as a future work.
