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Resource-based view (RBV) theory (Barney, 1991; Barney and Mackey, 2016; Nagano, 2020) states that a firm’s tangible and intangible resources can represent a sustainable competitive advantage (SCA), a long-term competitive advantage that is extremely difficult to duplicate by another firm, when it meets four criteria (i.e. not imitable, are rare, valuable and not substitutable). In the context of this case, we believe there are three sources of SCA to be discussed using RBV – the major league soccer (MLS) team player roster, the use of artificial intelligence (AI) technologies to exploit this roster and the league’s single-entity structure: • MLS players: it has been widely acknowledged that a firm’s human resource talent, which includes professional soccer players (Omondi-Ochieng, 2019), can be a source of SCA. For example, from an RBV perspective, a player on the Los Angeles Galaxy roster: > cannot play for any other team in any other league at the same time (not imitable and are rare), > would already be a competitive player, as he is acquired to play in the highest professional league in the country (valuable) and > it would be almost impossible to find a clone player matching his exact talent characteristic (not substitutable) anywhere else. Of course, the roster mix of players must be managed by a capable coach who is able to exploit these resources and win championships (Szymanski et al., 2019). Therefore, it is the strategic human resource or talent management strategies of the professional soccer team roster that will enable a team to have the potential for an SCA (Maqueira et al., 2019). • Technology: technology can also be considered a source of SCA. However, this has been a source of contention. The argument is that technology is accessible to any firm that can afford to purchase it. Logically, any MLS team (or for that matter any professional soccer team) can acquire or build an AI system. For many observers, the only obvious constraint is financial resources. As we discuss in other parts of the case study, there is a fan-based assumption that what transpired in major league baseball (MLB) may repeat in the MLS. The movie Moneyball promoted the use of sabermetrics in baseball when making talent selection (as opposed to relying exclusively on scouts), which has now evolved into the norm of using technology-centered sports analytics across all MLB teams. In short, where is the advantage when every team uses technology for talent management? However, if that is the case, why are the MLB teams continuing to use AI and now the National Basketball Association (NBA), National Football League (NFL) and National Hockey League are following suit? We believe RBV theorists have already provided early insights: > “the exploitation of physical technology in a firm often involves the use of socially complex firm resources. Several firms may all possess the same physical technology, but only one of these firms may possess the social relations, cultural traditions, etc., to fully exploit this technology to implementing strategies…. and obtain a sustained competitive advantage from exploiting their physical technology more completely than other firms” (Barney, 1991, p. 110). • MLS League Single-Entity Structure: In contrast to other professional soccer leagues, the MLS has one distinct in-built edge – its ownership structure as a single entity, that is as one legal organization. All of the MLS teams are owned by the MLS, but with franchise operators. The centralization of operations provides the MLS with formidable economies of scale such as when investing in AI technologies for teams. Additionally, this ownership structure accords it leverage in negotiations for its inputs such as for player contracts. The MLS is the single employer of all its players, fully paying all salaries except those of the three marquees “designated players.” Collectively, this edge offers the MLS unparalleled fluidity and speed as a league when implementing changes, securing stakeholder buy-ins and adjusting for tailwinds. The “socially complex firm resources” is the unique talent composition of the professional soccer team and most critically its single entity structure. While every team can theoretically purchase an AI technology talent management system, its application entails use across 30 teams with a very different, complex and unique set of player talents. The MLS single-entity structure though is the resource that supplies the stability required for this human-machine (technology) symbioses to be fully accepted by stakeholders such as players and implemented with precision and speed across the entire league. So, there exists the potential for each MLS team (and the MLS as a league) to acquire SCA even when using “generic” AI technology, as long as other complex firm factors come into play.

This case relied on information that was widely reported within media, press interviews by MLS officials, announcements by various organizations, journal articles and publicly available information on MLS. All of the names and positions, in this case, are actual persons.

MLS started as a story of dreaming large and of quixotic adventure. Back in 1990, the founders of the MLS “sold” the league in exchange for the biggest prize in world soccer – the rights to host the 1994 Fédération Internationale de Football Association World Cup before they even wrote up the business plan. Today, the MLS is the highest-level professional men’s soccer league competition in the USA. That is a major achievement in just over 25-years, as the US hosts a large professional sports market. However, MLS has been unable to attract higher broadcasting value for its matches and break into the highest tier of international professional soccer. The key reason is that MLS matches are not deemed high quality content by broadcasters. To achieve higher quality matches requires many inputs such as soccer specific stadiums, growing the fan base, attracting key investors, league integrity and strong governance, all of which MLS has successfully achieved since its inception. However, attracting high quality playing talent is a critical input the MLS does not have because the league has repeatedly cautioned that it cannot afford them yet to ensure long-term financial sustainability. In fact, to guarantee this trade-off, the MLS is one of the only professional soccer leagues with an annual salary cap. So, the question is: how does MLS increase the quality of its matches (content) using relatively low cost (low quality) talent and still be able to demand higher broadcast revenues? One strategy is for the MLS to use AI playing technology to extract higher quality playing performance from its existing talent like other sports leagues have demonstrated, such as the NFL and NBA. To implement such a radical technology-centric strategy with its players requires the MLS to navigate associated issues such as human-machine symbioses, risking fan acceptance and even altering brand valuation.

The case is written and designed for a graduate-level (MBA) class or an upper-level undergraduate class in areas such as contemporary issues in management, human resource management, talent management, strategic management, sports management and sports marketing. The case is suitable for courses that discuss strategy, talent management, human resource management and brand strategy.

This chapter examines the shifting significance of data ownership and athlete rights as they pertain to the growth and expansion of the global sports gambling industry. It provides a nuanced overview of the ‘datafication’ of society, tracing how the omnipresent embrace of digital technologies has expediated new forms of organisational, political and corporate surveillance from which concerns over privacy, rights to ownership and the misuse of personal data arise. The chapter moves on to discuss how the extraction and trade of data has revolutionised how elite sport is performed, manufactured, broadcast and consumed, shedding critical light on the role of the gambling industry in the exchange of human data as a market commodity. These insights inform a series of socio-legal and ethical questions about the relationships between athlete data and the sports gambling industry for the purpose of signposting emerging issues and opportunities for critical sociological research and intervention.

In recent years, school districts have faced numerous questions surrounding accommodations of transgender students. Strong objections to accommodations have been voiced in public argument and litigation, primarily in the areas of athletics, bathrooms, and dress codes. As younger transgender students express their gender identity at school, however, the existing objections are weakened by considering the context of elementary rather than high school students. Greater numbers of young transgender students will likely encourage accommodation of trans students of all ages, as well as challenge the gender binary unconsciously taught in school.

There is a great deal of discussion involving Black male college athletes, particularly at Division I colleges and universities in revenue-generating sports. However, there is little discussion with regard to the unique role and complex challenges faced by Black male personnel who have the task of supporting student-athletes, especially athletes who are also Black males. The authors of the chapter extensively review the role of student-athlete academic support departments and the competencies needed to work in the profession. In addition, the chapter authors discuss how Black male identity can affect the Black male’s role in academic support positions.

Guided by self-presentation theory and social role theory, this study examines the different strategies elite female athletes used in personal branding on social media before and after becoming mothers. Scholars have investigated the authenticity of female athletes’ frontstage versus backstage representation on social media for branding purposes, but this study further expands on existing literature to review how female athletes would present themselves in the same realm once entering motherhood.

Through a content analysis, researchers evaluated whether there was a shift in three elite female athletes’ (Serena Williams, Allyson Felix and Skylar Diggins Smith) Instagram posts and captions one year before their pregnancy and one year after motherhood. A total of 732 posts were examined and were organized into six main categories: athletic, professional, promotional, personal, motherhood and dual identity.

Results revealed there was a difference in the self-presentation strategies used by the three female athletes on their social media pages. Specifically, the researchers confirmed the presence of a combined role of athlete and mother.

The findings support existing literature on the importance and the challenges of “balancing” a third identity of blending being both a mother and elite athlete as one. Yet, the findings challenge the previous notion that women cannot continue to perform at an elite level and manage the expectations that society institutes of being a “good mother.”