# Skill Level Classification in Basketball Free-Throws Using a Single Inertial Sensor
## 4 Discussion
In prior basketball research, the focus has predominantly been on predicting postures or actions using deep learning algorithms rather than classifying players into skill groups, which limits the potential for providing feedback to improve basketball skills. For example, one study utilized a deep learning model to recognize basketball shooting maneuvers and achieved an average precision rate of 98.8% [25]. Another study employed a CNN-based algorithm to identify common basketball actions in video streams, attaining an accuracy rate of up to 95.6% [26]. In a third study [24], the authors recognized jump shots in a series of arbitrary basketball motion sequences using a CNN model applied to IMU signals in the time domain, obtaining recall and precision values over 0.97. Although these studies demonstrate high accuracy in posture or shot recognition, they do not offer additional bio-feedback information about the differences between varying levels of basketball performance. In contrast, our research aims to identify the key factors that contribute to different levels of basketball play, rather than focusing solely on individual shooting actions. Our primary recommendation for future research is the development of a biofeedback device that track the identified features of wrist joint movements related to FT shooting success. In doing so, it will allow real-time, externally focused feedback which has been shown to be optimal for motor learning in sports [35]. [PERSON] et al. (1998) have developed growing evidence to support the use of externally focused feedback, that is, feedback that focuses on the effect of the movement relative to the goal, manipulated through the apparatus. In the context of this topic, external feedback involves features such as wrist motion speed or acceleration, for example, that contribute to the outcome of an FT shot (in/miss). [PERSON] et al. [35] confirmed that externally focused feedback provided immediate after or during a trial is more effective for motor learning compared to delayed feedback. This parallels the findings of [PERSON] and [PERSON] [36], who additionally found that external feedback prompted smoother movement, lower errors, and skill improvementsthat were retained after the concurrent feedback was removed. Thus, there is a need to develop a biofeedback device for training FTs through utilizing machine learning, as this is a solution allowing instant motion analysis and feature extraction to provide real-time, external feedback. [PERSON] et al. set the foundation for this in their study, finding that data-analyzed feedback from a single wrist-worn inertial sensor could refine shooting kinematics and consistency, leading to improved accuracy in one semi-professional athlete shooting FTs [37]. A strength of this study was the inclusion of the professional athletes, who then proceeded to win the NBL championships in the same year and following year. Recruiting these participants highlights exemplary FT shooting and contributes to the strength of the classifier (88% accuracy). There are, however, limitations to this study. First, only two skill levels were included in the current classification, which does not accurately represent the spectrum of players, e.g., novice and semi-professional players. The classifier accuracy may be compromised when applied to a broader scope of athletes who may lie between recreational and professional levels. Additionally, this study had a relatively small sample size (n = 18). Ideally, we would expand the sample size; however, difficulties arose when recruiting participants due to the international prevalence of COVID-19. We could expect that an increase in participant numbers may further increase the robustness of the model. However, despite the small sample size, the algorithm has displayed success in skill level classification. Furthermore, due to the methodology of our study, we have only focused on one aspect of basketball shooting, that of FT shots.