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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.

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.

There has been a digital transformation of the sport industry that has resulted in an increase in the number of startups. Technological innovations derived from big data and social media have altered the way entrepreneurship is embedded in a sport context. This has influenced more technologically enabled sport startups that are driving change in the global economy. This chapter discusses the role of digitalization in changing existing business models and fostering a more entrepreneurial ecosystem. This includes focusing on technological innovations such as the impact of cloud computing and other data changes.

This chapter analyzes the aggregate performance of Home Run Derby competitors’ performance both before and after the Home Run Derby for the time period 1999–2013. Regression to the mean suggests that in general, those players with outstanding performances in the first half of the season will regress to the mean. The findings here are consistent with regression to the mean, and the mean performance along four key analytics is statistically significantly worse for the competitors. However, the winners’ mean performance both before and after the Home Run Derby are not statistically significantly different. Thus, the results are consistent with previous research, but the results also find so-called “winner and loser” effects in Major League Baseball.

The paper considers the phenomenon of Big Data through the work of Hannah Arendt on technology and on thinking. By exploring the nuance to Arendt’s critique of technology, and its relation to the social and political spheres of human activity, the paper presents a case for considering the richness of Arendt’s thought for approaching moral questions of Big Data. The paper argues that the nuances of Arendt’s writing contribute a sceptical, yet also hopeful lens to the moral potential of Big Data. The scepticism is due to the potential of big data to reduce humans to a calculable, and thus manipulatable entity. Such warnings are rife throughout Arendt’s oeuvre. The hope is found in the unique way that Arendt conceives of thinking, as having a conversation with oneself, unencumbered by ideological, or fixed accounts of how things are, in a manner which challenges preconceived notions of the self and world. If thinking can be aided by Big Data, then there is hope for Big Data to contribute to the project of natality that characterises Arendt’s understanding of social progress. Ultimately, the paper contends that Arendt’s definition of what constitutes thinking is the mediator to make sense of the morally ambivalence surrounding Big Data. By focussing on Arendt’s account of the moral value of thinking, the paper provides an evaluative framework for interrogating uses of Big Data.

The chapter explores the developments in work on the history of quantification and sport, explaining how quantification in sport is generally understood, and then establishing what a sociological approach offers to scholars interested in exploring new expressions of these developments in biometrics and Big Data. It then outlines some potential directions scholars might pursue to further develop knowledge of these developments in the context of sport.

The chapter synthesizes existing literature from the sociology of quantification, sport sociology and quantification, and Big Data to provide historical, contemporary, and future oriented assessments of sport and datafication.

By situating a discussion of Big Data and biometrics in the context of sport, this chapter argues for the value of a sociological approach to these areas. The chapter engages prior work as a way to move scholars to challenge the assumed epistemological and political power of numbers for the way we engage sport.

The chapter argues for a number of future areas of study that may push the boundaries of existing research in the area.

The chapter provides a survey of the literature on sport, analytics, and Big Data as an impetus for future research into the importance of a sociological approach to these areas in the context of sport.