The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.