This paper focuses on collecting a dataset that will be used for the development of biofeedback systems. The measurement setup included an IMU sensor and a motion capture system. We have collected recordings of hand movement in over 2000 dart throws. Our preliminary ML experiments employed a 1D convolutional neural network (1D-CNN) to classify dart throws based on throwing accuracy and throwing precision. Results indicate that the system can achieve up to 57% classification accuracy, suggesting that throw outcomes are influenced by factors not fully captured by current sensors. Notably, motion capture data improved accuracy predictions, likely due to its ability to capture hand direction, whereas IMU sensors alone sufficed for precision evaluation. These findings highlight the challenges of predicting throw outcomes without capturing variables like aiming and dart release points.