<![CDATA[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.]]>
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