## 4 Discussion
### Needs, Limitations, and Potential of Big Data in Professional Soccer  
There is limited information regarding the last three metrics of veracity. Bias was reported as absolute values in only two studies [127; 129], and, in one study, it was reported as relative values ranging between 2.0-10.0%, with the SEE ranging between 4.0-22.0% for total and sprint distances between 19.8-25.2 and above 25.2 km/h, recorded with EPTS [133]. Precision ranged between 3.0-10.0% for urine metabolomic parameters in a single study [128]. In view of the definition itself, these three metrics are important for appropriately understanding veracity. Therefore, more studies with the use of these metrics in professional soccer athletes are warranted as well as more studies with professional female soccer players, who represented only 5% of the samples in the included studies. Moreover, 94% of the included articles are observational studies, and among the experimental study designs, none were randomized controlled trials. Despite the difficulty of carrying out experimental studies in professional soccer settings, we suggest that more of these studies are warranted to increase the veracity of monitoring tools and their associated parameters. The approach applied in this systematic review is based on one of the 4 Vs of Big Data. However, the other Vs also have an impact on soccer and should be the focus of future research. In this regard, the volume that describes the magnitude of the data [14] and is usually measured in terabytes (i.e., 10\({}^{12}\)), petabytes (i.e., 10\({}^{15}\)), zettabytes (i.e., 10\({}^{21}\)), and even yottabytes (i.e., 10\({}^{24}\)) [15] has increased exponentially, with a 300-fold increase in the last 15 years, from 2005 to 2020, reaching 40 zettabytes (i.e., 37.3 trillion gigabytes; 10\({}^{9}\)) [15]. Therefore, this unprecedented volume of data is overwhelming, thus increasing the risks of it not being fully used to inform the practice [13]. For example, a dataset from a Bundesliga season resulted in 400 gigabytes of tracking data [14]. Regarding this, variety refers to the heterogeneity of data, i.e., to different data formats and sources distinguished, which can be differentiated among structured (e.g., relational data), semi-structured, (e.g., XML data), and unstructured data (e.g., emails, pictures, videos, or social networking data) [14; 15; 16; 17]. Thus, in soccer, data variety refers to position, video, fitness, training, skill performance, and health data [14]. Moreover, velocity describes the speed at which novel data is generated, processed, and analyzed [14; 15; 16; 17]. Specifically for soccer, the velocity may vary between real-time streams from physiological and positional data to stored data tonotational analyses during training and competition [14]. All these three key concepts characterizing Big Data are highly relevant and should be considered with the suggestion by [PERSON] and [PERSON] [23], who have stated that Big Data can only have value when its veracity can be established and, thereby, the information quality confirmed. To date, Big Data has been reported in the scientific literature of soccer in relation to tactical analysis only [14; 146] but not in relation to load monitoring. Although data can be considered big based on the number of V2019s, there are no universal benchmarks for the number of Vs [14; 15; 16; 17; 18; 21; 23; 146; 147]. Therefore, the current systematic review establishes the initial basis for the implementation of Big Data approaches for load monitoring in professional soccer while identifying the strengths and limitations of the current evidence before moving on to the real applications of Big Data: data management, which involves processes and supporting technologies to acquire and store data and to prepare and retrieve it for analysis, and analytics, which refers to the techniques used to analyze and acquire intelligence from Big Data [146; 147]).