# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 6 Discussion  
The pervasiveness of smart applications has influenced all aspects of sports training. Studies show that the SST is and can be a determining force in transforming all of the four training phases (1.RQ). The most researched training phase was realization, as shown in Table 20. The realization phase is arguably one of the least complex stages where the SST approach can be utilized, because recording the actual data during training can be done mostly by the use of wearable devices. Some multi-purpose wearable devices can be used in multiple domains and can provide sensors for sleep tracking, heart rate monitoring, step tracking, accelerometer, gyroscope, GPS, etc. as presented in [5]. Evaluation phase research was mostly related to longitudinal studies where the training data was compared with the competition performances (e.g., [88, 107]). The research related to the training control phase was tightly interconnected with the realization training phase research, especially when measures were introduced to record training data (e.g., [72, 91, 112, 117]). The least research was related to the planning phase of the training, where the real human coaches still prevail. The planning phase research was related to creating training (exercise) planning g (e.g., [55, 56, 57, 61, 106, 126]), nutrition planning (e.g., [55, 151]), and full framework approaches (e.g., [78, 131, 145]), which were related to all training phases. Identifying (2.RQ ) the most SST supported sports research was presented previously on the Figure 5, where it was shown that the most research was done in the domains of soccer (12 papers [78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]), running (11 papers [64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), and weight lifting (9 papers [110, 111, 112, 113, 114, 115, 116, 117, 118]). The most utilized data analysis methods (3.RQ) were support vector machines (19), artificial neural networks (14), k-nearest neighbors (11), and random forest (11). Based on the taxonomy identified in Section 4, the most widely used methods were in the category of data mining as seen in Figure 6.  
Since SST research has experienced such a burst of growth in recent years, a number of papers presented numerous new approaches to sports training. We did not review any of the reviewed approaches in any of the training applications as TRL validation (4.RQ) level research. This may seem surprising, since a lot of applications have been widely used by the general public on Android devices, such as MyFitnessPal [157], Endomondo Running and Walking [158], Google Fit [159], and Apple ecosystem devices, such as Apple Health [160], all of them having over 10 million users. The problem is that such approaches do not share the algorithms and data analysis behind their analysis systems to maintain their competitive advantage. The highest level of TRL achieved was control and, two  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. ** & **Research** & **Focus** & **Results** & & **Training** & \\  & **SST Methods** & & & **P** & **R** & **C** & **E** \\ \hline
[153] & K-means clustering, Hierarchical Clustering & Proposal for an integrated Data Mining algorithm based on sports team match data and the generation of individual training regimes to increase their athletes’ stamma. & Proposed algorithm with necessary hardware and software equipment for actual use. planning, four realization, three control, and three evaluation research studies reached that level, as seen in Table 21.  
Since the majority of research done is still in the prototype phase, additional studies are needed in all SST stages and sports to progress towards the control and validation of the proposed ideas. The maturity of SST is very low for phases in sports where no research was found: