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Top1. Introduction
In the present day, finding and hiring talented workers has become one of the top priorities for many businesses. Inefficient hiring practices have a negative repercussion on any organization, and might impose considerable loses, both in terms of money and time. As pointed out by Davenport et al. (2010), those companies that are capable of attracting and retaining the best talented people are among the most competitive ones. According to Harris et al. (2011), in a globalized and highly competitive environment most organizations should start using data to measure and improve the contribution of their human resources (HR) to their performance.
In the sports sector, Baker et al. (2017), De Bosscher and De Rycke (2017) and Hanlon et al. (2014) state that there is an increasing interest in understanding the costs and benefits of initiatives for early identification of talented players, as well as in unleashing the factors that influence athletes’ development. These authors also affirm that, while traditional statistical analysis was focused on match variables, such us goals scored o players’ position on the field, recent advances in sports analytics are focused on more complex issues like talent acquisition. As pointed out by Gerrard (2017), the 2011 film ‘Moneyball’1 highlighted the possibilities of analytics as a competitive strategy, particularly for small-market teams with relatively limited resources. Following Fried and Mumcu (2016), many coaches employ data on habits and performance indicators to assess the potential of their players. In fact, it is possible to use data to: (i) evaluate players’ performance; (ii) rank players (Pappalardo et al., 2019); (iii) estimate the value of players in transfer markets (Kim et al., 2019); (iv) locate the best position that a player can occupy in the field; or (v) forecast players’ goal scoring performance in the next season (Apostolou and Tjortjis, 2019). As illustrated in Figure 1, adapted from 21st Club2, clubs can use data to analyze the impact of a new player on the team’s overall performance level.
The sports industry is being transformed by data analytics in the following dimensions: (i) at clubs level, e.g., soccer clubs like Liverpool, Barcelona3,
Figure 1.
Player recommendation based on data analytics (adapted from 21st Club)
Arsenal, Manchester City, or Milan are among the ones that already use data analysis to improve performance, analyze rivals, prevent injuries, optimize the management of the transfer market, and also the acquisition of new talent; (ii) regarding new entrants in data management and analysis, new platforms for data analysis and management appear to provide services to clubs, such as Wiscout4 and Scisports5; (iii) as regards as new entrants in data capture and generation, large companies such as Intel have launched the creation of wearable Internet-of-Things devices, which are capable of capturing players’ information in real time6; (iv) with respect to transformation of sports management professionals, new official university degrees dedicated to sports management have emerged, most of them with a special emphasis on data science; and (v) a transformation of sports enthusiasts, who have begun to consume complex data, both for their own digitization process as well as for their leisure and fun when using online betting applications.