Abstract:

The strength of hamstring and quadriceps muscles plays an important role for athletes and sportspeople in determining their performance. The purpose of this study is to predict the hamstring and quadriceps muscle strength using Multiple Linear Regression (MLR). The dataset used for this study includes the data of 70 athletes consisting of the features gender, sports branch, height, weight and age, as well as the hamstring and quadriceps muscle strength values measured with two types of activities (static training and classic training) used as the target variables. MLR has been used for the development of prediction models using different types of validation options including cross-validation and random percentage data split. The Root Mean Square Error (RMSE) has been utilized as the main error metric for evaluating the performance of the prediction models. The RMSE values of the prediction models range between 14.91 and 32.41 Nm, showing that in addition to machine learning methods, MLR can also be used for predicting the hamstring and quadriceps muscle strength with acceptable error rates.

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