## 1 Introduction  
Load monitoring is used to ensure the best workload that optimizes physical fitness and sports performance while preventing athletes from injury or illness in soccer [1]. In this context, a true individualization of all training factors in team sports is mandatory for a better fitness-fatigue equilibrium through an individualized monitoring approach [2]. Meanwhile, the number of injuries [3] and the physical performance of soccer players continue to increase over the years [4]. For instance, in a 13-year longitudinal analysis, it was found that hamstring injuries have annually increased by 4% in professional soccer players [3]. Factors associated with load monitoring, such as player load, match frequency, playing style, team management, and the continuity of technical staff, have also influenced these trends [3]. On the other hand, it is well accepted that physical performance in soccer matches has continuously improved over the past years. For instance, total distance covered, high-intensity running and sprinting distances, and number of sprints increased by 2% (effect size (ES): 0.22), 30% (ES: 0.82), 35% (ES: 0.93), and 85% (ES: 1.46), respectively, across 7 seasons of the English Premier League (i.e., 2006-2007 compared to 2012-2013) [4]. However, the VO\({}_{\text{2 max}}\) of elite female [5] and male [6] soccer players has not changed between 1989 and 2007 for females and between 1989 and 2012 for males. This paradox of a similar aerobic fitness between decades, despite objectively increased match demands, highlights the need to select appropriate monitoring tools. Furthermore, considering that soccer is a complex sport in which players need to develop several physical capacities (e.g., acceleration, agility, endurance), the selection of the best tools to monitor the evolution of players' physical fitness over the season is required for better managing the complex balance between training, competition, recovery, and evaluation [7; 8; 9; 10].  
We are living in a time where technology has experienced a fast evolution in the sports arena [11]. This technological growth has enabled the 24 h monitoring of athletes on an individual basis [12], thus allowing large data sets, i.e., Big Data, to be gathered in sport settings. Careful analyses of these large datasets can enhance our knowledge in sportsscience and medicine, thus supporting the making-decision process for designing appropriate training and competitive strategies [13; 14]. However, some premises, which are known as the four Vs of Big Data, should be attained fromthese datasets for these purposes: (1) volume, (2) variety, (3) velocity, and (4) veracity [15]. Volume refers to the size of the datasets; variety refers to different data formats and data sources; velocity describes the speed at which data is generated and processed for analyses; and veracity refers to the accuracy, quality, relevance, uncertainty, reliability, and predictive values of the collected data [14; 15; 16; 17]. Focusing on veracity to identify the best monitoring tools and parameters in soccer is mandatory to identify the \"errors\" [18]. Furthermore, veracity is a preliminary step to effectively meet all these premises and, thus, optimize the applicability of Big Data to soccer with the best cost-to-benefit ratio. For instance, according to IBM [15], poor data quality generates an economic cost of around USD 3.1 trillion per year in the USA. If this value were the gross domestic product (GDP) of any country, it would be placed in the top 6 GDPs in the world.  
Therefore, this systematic review aims to evaluate data veracity for load monitoring in professional soccer. This information is important for a deep understanding of the tools and parameters used in load monitoring as well as the effective implementation of Big Data analyses in professional clubs.