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Sports leagues and media providers are constantly seeking new ways of improving the consumption experience of viewers. Several new technologies have arrived in the industry, but many have not proved financially viable. Among these new technologies is tracking technology, used to augment television coverage and for coaching enhancement. This has had mixed results. In this paper I argue that the emergence of Moneyball management practices in sport have created the supervening necessity (Winston, 1998) required to drive demand for player tracking technology in ice hockey. This technology is able to collect the data necessary to implement statistical analyses comparable to those used in professional baseball to cover media enhancement, coaching enhancement and Moneyball management.

In this study we examine how resource-constrained organizations can maneuver for competitive advantage in highly institutionalized fields. Unlike studies of institutional entrepreneurship, we investigate competitive maneuvering by an organization that is unable to alter either the regulative or normative institutions that characterize its field. Using the “Moneyball” phenomenon and recent changes in Major League Baseball as the basis for an intensive case study of entrepreneurial actions taken by the Oakland A’s, we found that the A’s were able to maneuver for advantage by using bricolage and refusing to enact baseball’s cognitive institutions, and that they continued succeeding despite ongoing resource constraints and rapid copying of their actions by other teams. These results contribute to our understanding of competitive maneuvering and change in institutionalized fields. Our findings expand the positioning of bricolage beyond its prior characterization as a tool used primarily by peripheral organizations in less institutionalized fields; our study suggests that bricolage may aid resource constrained participants (including the majority of entrepreneurial firms) to survive in a wider range of circumstances than previously believed.

Storytelling can be the difference between your data making a true contribution or remaining unheard. Because in order to move your stakeholders to act, they need to thoroughly understand why your data matters, and often on an emotional as well as a rational level. And for that, there is no more powerful tool than storytelling.

In this chapter, we'll apply the techniques of the most powerful story form of all, movies, to data slides, and in the process, make them easy to understand and believe in.

You'll read and see techniques and examples that will help you:

• Focus your data so it's quick and clear.

• Frame it in ways that feel tangible and relatable to your stakeholders.

• Make the reason why it matters more powerful so your stakeholders will be moved to act.

• How storytelling will become even more interesting in the age of machines.

Focus your data so it's quick and clear.

Frame it in ways that feel tangible and relatable to your stakeholders.

Make the reason why it matters more powerful so your stakeholders will be moved to act.

How storytelling will become even more interesting in the age of machines.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

One of the curious phenomena of publishing since the turn of the millennium has been the success of books in the business market that started out as being as far away from that sphere as it is possible to be. Moneyball by Michael Lewis was a huge hit, supporting as it did the use of data analysis rather than gut feel, despite the fact it centered on a plucky baseball team that ultimately never won anything. Similarly, other books that have looked at political empires, creativity, historical events and even episodes in fiction have garnered praise and sales in equal measure for the insights they purport to bring business leaders.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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.

Behavioral strategy completes the analyses of superior profitability by highlighting how non-economic, behavioral barriers generate an alternative source of strategic opportunities. Existing internal and external analysis frameworks fail to explain why strategic factors can be systematically mispriced and why large firms’ structural and resource advantage are regularly disrupted by entrepreneurs. We argue that the systematic biases documented in the behavioral and organizational sciences in fact illuminate an alternative source of competitive advantage. Strategists could develop superior insights into the value of resources and recognize factors that are either under- or overvalued while competitors remain blind to such possibilities. Our argument is illustrated by how three “underdogs” disrupted the incumbents in their industries by exploiting rivals’ predictable biases and blind spots. We conclude by discussing how our ideas can be generalized as an alternative, behavioral approach for strategy.

In baseball, a run scored on offense carries the same on-field (win) value as does a run prevented on defense. Both outcomes bear the same score margin implication. This presumption of unit equality is implicit in the Wins Above Replacement (WAR) measure, which treats units of offensive WAR (oWAR) and units of defensive WAR (dWAR) as perfectly substitutable toward win production. The purpose of this paper is to ask whether the salaries of Major League Baseball (MLB) players reveal such an equal valuation among MLB teams.

The authors examine the relationship between offensive output, defensive output and subsequent salary from free agency in MLB using a set of log-linear OLS, fixed effects regression specifications.

In general, estimated annual salary from free agency increases significantly and substantially with unit increases in a player's (prior season) wins above replacement WAR. Across specifications, the authors estimate a 42.5–43.4% increase in salary for year t for each additional unit of WAR in year t−1. The authors disaggregate WAR into offensive and defensive components (oWAR and dWAR) and estimate a 52.4–53.3 (4.8–7.2)% increase in salary for each additional unit of oWAR (dWAR).

The efficiency of the baseball labor market has been studied previously with mixed results. The novelty of the present study is its treatment of inputs not as positions or individual players but as the underlying offensive and defensive win production of players. The authors estimate free agency salary returns to (contract season) oWAR and dWAR in MLB to establish whether (to what extent) a salary premium for offensive output exists within MLB.