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ABSTRACT
Prediction of Factors That Affect Sportsman Performance by Using Support
Vector Machines
The aim of this study is to predict aerobic performance level of a sportsman
by using Support Vector Machines (SVM), which was defined as a kind of
statistical learning system.
In this study; age, height, weight and test results belonging to Sportsmen
that had cardiopulmonary exercise tests at Çukurova University Sport Physiology
Laboratory between the years of 2003 and 2006 were used. According to the
exercise test protocol, velocity and slope were increased gradually and the
quantities of minute ventilation volume (VE), oxygen consumption (VO2), carbon
dioxide generation (VCO2), and heart rate were saved in a certain time interval. To
analyze data with SVM, software was developed by using MATLAB programming
language that makes high level technical computing.
The sportsmen have been separated in three groups, which were named as
train group, test group and predict group respectively. 17 of them were included in
the train group, 10 of them were included in the test group and 5 of them were
included in the predict group. It has been shown that exercise test data become
more stable by taking average of data and applying a curve-fitting algorithm to
data.
The decision function that was obtained after learning phase has been
applied to the datasets that belong to sportsmen who were included in the test and
predict group in turn in order. As a result, performance levels of all sportsmen
who were included in the test group have been predicted correctly.
SVM has successfully been used in a wide range of areas in analysis of many
problems. Also in this study, performance levels of sportsmen were predicted with
SVM successfully. Having an idea that this method can be applied in many
subjects on physical education and sports area, further studies are needed that use
SVM.
Key Words: Support Vector Machines, Sportsman Performance,
Sportsman Physiological Characteristics
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