## 4 Discussion
### Data Collection and Statistical Analysis Approaches  
Overhead throwing movements involve high rotational velocities and complex and increased joint kinetics at the shoulder, the combination of which makes it ideal for the utilization of 3D motion capture for analysis [19]. The most commonly used data collection method was infrared marker-based motion capture systems (Table 2). There are limitations as the process requires attachment of many markers correctly and firmly on the human body, this is time-consuming and requires skilled operators to collect accurate data. Due to the nature of overhead sporting motions (mostly performed at high speed), bandages or tapes were often used to minimize the number of markers falling off which could cause a diversion from their normal motion--reducing the ecological validity. Although markers are tracked accurately, skin artifact and wobbling mass movement can compromise the quality of data collected [44]. Fortunately, a handful of investigations have shown that marker-based motion capture is the current gold standard in motion analysis based on validation and high accuracy [21]. Meanwhile, the post-data processing can be very complex and tedious due to the segment definitions and mathematical constraints on the complex shoulder joint anatomy at different positions. An alternative method used in tracking overhead sporting movements that is cheaper and easier to set up is based on wearable sensors such as IMU. These are a small and lightweight system embedded with 3D accelerometers, gyroscopes, and magnetometers, which make IMU more accessible and quicker in terms of gathering and processing the kinetic and kinematic data [63; 64]. Despite IMU proving comparable to the gold standard accuracy of motion capture in certain circumstances, there is a phenomenon called IMU drift that stems from continuously integrated noise errors, and in turn could cause absolute IMU measurement error to increase over time [65; 66]. However, if continuous integration can be avoided or a consistent reference (i.e., position or velocity) can be used, this error can be avoided. On the other hand, the emerging marker-less 3D tracking technology could potentially ease the lab and field settings. However, the accuracy of these systems is not yet well validated. Ref. [19] identified similar baseball pitching kinematics between marker-based and marker-less motion capture system but variations still exist in the results between the two systems due to different methods in defining joint center locations. While a variety of 3D tracking technologies besides motion capture system are now commercially available, the preference of different approaches often depends on (but is not limited to) the testing objectives, cost, time consumed, complexity in the set-up, operator dependency, portability of the devices, and the ecological validity. Most studies reviewed in this paper relied on the investigation of joint kinematics at discrete time points (i.e., key instants, peak and summary values) and used standard analytic (linear) methods. However, overhead sporting motions consist of a continuous time series of kinematic data, therefore motion analysis based on discrete data may neglect potential valuable information throughout the entire movement. The discrete value itself does not represent the complete description of a single variable over time nor taking spatiotemporal confounding effect into consideration [67]. In the last decade, Statistical Parametric Mapping (SPM) has been used within the biomechanics and human movement science community for uni/multivariate time series data (i.e., kinematics and kinetics) [68; 69]. This method is advantageous for biomechanical analysis as it allows full-field non-direct hypotheses tests and visualization of the statistical results over time series [70]. Consequently, the field regions that significantly co-vary can be identified objectively, potentially facilitating better data interpretation.