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Dharma, Pandit Putu
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IMPACT OF ANTHROPOMETRIC FACTOR ON SERVE VELOCITIES EVALUATION TENNIS MATCH AT THE 2019 US OPEN GRANDSLAM TOURNAMENT Dharma, Pandit Putu; Setijono, Hari; Mintarto, Edy
Journal of Sport Science and Education Vol 5, No 1 (2020)
Publisher : Fakultas Ilmu Olahraga, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jossae.v5n1.p48-56

Abstract

AbstractThe purpose of this study is to analyze  (a) anthropometric factor as multivariant regression model to predict the serves velocities of elite tennis player that compete in Roland Garros 2017.  The athropometric factor was describe as, player height, player weight, age, and the Body Mass Index (BMI) of player. (b) this study also determind the significant level from the model to predict the serves speed. The data were collected from MATCH DETAILS BY IBM SLAMTRACKER.  Results show that correlation between each independent variable (i.e, age, height, weight, BMI) to serves speed of the player respectively are -0.0094, 0.7457, 0.7135, and 0.1944. The data show us that variable Height and Weight have the most correlated predictors of tennis serves speed. More over the correlation level  (R2) from the model was 0.5767 or 57.67 % it indicate that the model can predict 57.67% the dependent variable, i.e serve speed and the other 42.33% was determind by the other factor not included in model. The present finding underline the importance of player height and weight to determind   the serves speed of the players that play in Roland Garros 2017.
Impact of Anthropometric Factor on Serve Velocities Evaluation Tennis Match at The 2019 US Open Grandslam Tournament Dharma, Pandit Putu; Setijono, Hari; Mintarto, Edy
Journal of Sport Science and Education Vol 5, No 1 (2020)
Publisher : Fakultas Ilmu Olahraga, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jossae.v5n1.p48-56

Abstract

The purposes of this study are to analyze (a) anthropometric factor as multivariant regression model to predict the serves velocities of elite tennis player that compete in US Open Grandslam 2019. The athropometric factor was describe as, player height, player weight, age, and the Body Mass Index (BMI) of player. (b) this study also determind the significant level from the model to predict the serves speed. The data were collected from MATCH DETAILS BY IBM SLAMTRACKER. Results show that correlation between each independent variable (i.e, age, height, weight, BMI) to serves speed of the player respectively are -0.0094, 0.7457, 0.7135, and 0.1944. The data show us that variable Height and Weight have the most correlated predictors of tennis serves speed. More over the correlation level (R2 ) from the model was 0.5767 or 57.67 % it indicates that the model can predict 57.67% the dependent variable, i.e serve speed and the other 42.33% was determind by the other factor not included in model. The present finding underlines the importance of player height and weight to determind the serves speed of the players that play in US Open Grandslam Tournament 2019.
Co-Authors Edy Mintarto, Edy Hari Setijono, Hari