Sage Journals: Discover world-class research

Abstract

Due to the influence of Michael Lewis’s book and its film adaptation of the same title, ‘Moneyball’ is now a euphemism for using data analytics to generate insights. These texts perform important cultural explications of machine learning. Methodologically informed by critical discourse analysis, film studies, and cultural studies, this essay describes how the 2011 film in particular aestheticizes epistemological notions such as data framing, the semantic gap, and deep learning. Moneyball also proffers a view of analytics as Platonic knowledge, functioning ideologically alongside nerd archetypes and buddy-comedy conventions. The resultant duality between the Platonic and embodied, innervated by relations between visibility and invisibility, typifies the way people relate to Big Data and to the institutions that govern our digital lives in algorithmic culture. Moneyball performs cultural work by encouraging us to embrace data science while remaining alienated from technology and deferential to experts. Calls for technological literacy in the age of Big Data cannot underestimate the importance of cultural literacy.

Keywords

Algorithms in culture data literacy data politics data science machine learning nerds Platonism public understanding

Get full access to this article

View all access options for this article.

References

21 (2008) Directed by Robert Luketic. Los Angeles: Columbia Pictures Corporation.

A Beautiful Mind (2001) Directed by Ron Howard. Universal City: Universal Pictures Brothers.

A.I. Artificial Intelligence (2001) Directed by Steven Spielberg. Los Angeles: Warner Brothers.

aiwpadmin (2012) Editor Christopher Tellefsen on Moneyball. Studio Daily, 17 February. Available at: http://www.studiodaily.com/2012/02/editor-christopher-tellefsen-on-moneyball/ (accessed 29 January 2019).

Ames

(2018) Deconstructing the algorithmic sublime. Big Data & Society. Available at: https://doi.org/10.1177/2053951718779194 (accessed 2 February 2019).

Analytics Vidhya Content Team (2015) 10 must watch moves on data science and machine learning. Analytics Vidhya, 9 November. Available at: https://www.analyticsvidhya.com/blog/2015/11/10-watch-movies-data-science-machine-learning/ (accessed 1 July 2019).

Ananny

Crawford

(2018) Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society 20(3): 973–989.

Beer

(2017) The social power of algorithms. Information, Communication and Society 20(1): 1–13.

Blade Runner (1982) Directed by Ridley Scott. Los Angeles: Warner Brothers.

10.

Bryant

(2010) The revolutionary. ESPN, 15 January. Available at: http://www.espn.com/espn/otl/columns/story?columnist=bryant_howard&id=4357166 (accessed 7 June 2018).

11.

Cheney-Lippold

(2017) We Are Data: Algorithms and the Making of Our Digital Selves. New York: NYU Press.

12.

Cohan

(2012) Science needs more Moneyball. American Scientist 100(3): 182.

13.

Connor

(2018) Hollywood Math and Aftermath: The Economic Image the Digital Recession. New York: Bloomsbury Academic.

14.

Craggs

(2010) My uncomfortable encounter with an angry Joe Morgan. Deadspin, 9 November. Available at: https://deadspin.com/5685456/my-uncomfortable-encounter-with-an-angry-joe-morgan (accessed 7 June 2018).

15.

Deleuze

(1992) Postscript on the societies of control. October 59: 3–7.

16.

Delistraty

(2016) Michael Lewis and the narrative nonfiction formula. Los Angeles Review of Books, 9 December. Available at: https://lareviewofbooks.org/article/michael-lewis-narrative-nonfiction-formula (accessed 17 February 2019).

17.

Eglash

(2002) Race, sex, and nerds: From black geeks to Asian American hipsters. Social Text 20(2): 49–64.

18.

Ex Machina (2014) Directed by Alex Garland. New York: A24.

19.

Foucault

(1984) What is an author? In: Paul

(ed) The Foucault Reader. New York: Pantheon Books, pp. 101–120.

20.

Goldstein

Rainey

(2009) The big picture. Los Angeles Times, 30 June. Available at: http://latimesblogs.latimes.com/the_big_picture/2009/06/sonys-amy-pascal-speaks-out-about-moneyball.html (accessed 7 June 2018).

21.

Haraway

(1988) Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies 14(3): 575–599.

22.

Hayles

(1999) How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago: University of Chicago Press.

23.

Hirsch

(2011) The Beauty of Short Hops: How Chance and Circumstance Confound the Moneyball Approach to Baseball. Jefferson: McFarland and Company.

24.

Hruby

Simpson

Stevens

(2011) The ‘Moneyball’ effect: Are sabermetrics good for sports? The Atlantic, 2 September. Available at: https://www.theatlantic.com/entertainment/archive/2011/09/the-moneyball-effect-are-sabermetrics-good-for-sports/244453/ (accessed 7 June 2018).

25.

Kendall

(1999) Nerd nation: Images of nerds in US popular culture. International Journal of Cultural Studies 2(2): 260–283.

26.

Kendall

(2011) “White and nerdy”: Computers, race, and the nerd stereotype. The Journal of Popular Culture 44(3): 505–524.

27.

Kennedy

Hill

(2018) The feeling of numbers: Emotions in everyday engagements with data and their visualization. Sociology 52(4): 830–848.

28.

Latour

(2014) Anti-zoom. In: Studio

(ed) Contact (exhibition catalogue). Paris: Flammarion.

29.

Lewis

(2003a) Moneyball: The Art of Winning an Unfair Game. New York: W.W. Norton and Company.

30.

Lewis

(2003b) The trading desk. The New York Times Magazine, 30 March. Available at: https://nyti.ms/2kzAbcA (accessed 5 February 2019).

31.

Lewis

(2010) The Big Short: Inside the Doomsday Machine. New York: W.W. Norton and Company.

32.

Lewis

(2014) Flash Boys: A Wall Street Revolt. New York: W.W. Norton and Company.

33.

Lewis

(2017) The Undoing Project: A Friendship that Changed Our Minds. New York: W.W. Norton and Company.

34.

Lohr

(2012) The age of big data. The New York Times, 11 February. Available at: https://nyti.ms/2jCFuqJ (accessed 20 December 2018).

35.

Luhnow

De Smet

Webb

(2018) How the Houston Astros are winning through advanced analytics. McKinsey Quarterly, June. Available at: http://bit.ly/2Ic9gLn (accessed 1 July 2019).

36.

McQuillan

(2018) Data science as machinic Neoplatonism. Philosophy and Technology 31(2): 253–272.

37.

Michael

Lupton

(2016) Toward a manifesto for the ‘public understanding of big data. Public Understanding of Science 25(1): 104–116.

38.

Minority Report (2002) Directed by Steven Spielberg. USA: 20th Century Fox and Dreamworks Studios.

39.

Moneyball (2011) Directed by Bennett Miller. Los Angeles: Columbia Pictures Corporation.

40.

Mosco

(2004) The Digital Sublime: Myth, Power, and Cyberspace. Cambridge: MIT Press.

41.

Murphy

(2012) Below the line: Making the cut on ‘Moneyball. The Carpetbagger: The Awards Season Blog of The New York Times, 8 February. Available at: https://carpetbagger.blogs.nytimes.com/2012/02/08/below-the-line-making-the-cut-on-moneyball/ (accessed 27 January 2019).

42.

Nugent

(2008) Field guide to the nerd: It’s all geek to me. Psychology Today 41(4): 39–40.

43.

Pasquale

(2015) The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge: Harvard University Press.

44.

Plato (2005) Phaedrus. London: Penguin.

45.

Reany

(n.d.) ‘Top 15 Machine Learning Films. Big Cloud. Available at: https://bigcloud.io/blog-and-news/big-clouds-top-15-machine-learning-films/ (accessed 1 July 2019).

46.

Roberts

(2019) Behind the Screen: Content Moderation in the Shadows of Social Media. New Haven: Yale University Press.

47.

Roose

Kang

Frenkel

(2018) Zuckerberg gets a crash course in charm: Will congress care? The New York Times, 8 April. Available at: https://nyti.ms/2uUdgz7 (accessed 16 February 2019).

48.

Rosenhouse

(2009) The Monty Hall Problem: The Remarkable Story of Math’s Most Contentious Brain Teaser. New York: Oxford University Press.

49.

Schumpeter (2012) In praise of misfits. The Economist 403(8787): 84.

50.

Sherman

(2012) Rethinking America’s pastime: The Paul Depodesta story. The Harvard Crimson, 5 May. Available at: https://www.thecrimson.com/article/2012/5/5/feature-paul-depodesta-040412/ (accessed 2 February 2019).

51.

Siegel

(2008) Columbia pitches ‘Moneyball’ to Pitt. Variety, 16 October. Available at: https://variety.com/2008/film/markets-festivals/columbia-pitches-moneyball-to-pitt-1117994175/ (accessed 7 June 2018).

52.

Smith

(2018) 10 must-watch movies on data science and machine learning. Big Data Made Simple, 1 October. Available at: https://bigdata-madesimple.com/10-must-watch-movies-data-science-machine-learning/ (accessed 1 July 2019).

53.

The Big Short (2015) Directed by Adam McKay. Los Angeles: Paramount Pictures.

54.

The Imitation Game (2014) Directed by Morten Tyldum. New York: The Weinstein Company.

55.

The Matrix (1999) Directed by the Wachovskis. Los Angeles: Warner Brothers.

56.

The Social Network (2010) Directed by David Fincher. Los Angeles: Columbia Pictures Corporation.

57.

Thomas

Nafus

Sherman

(2018) Algorithms as fetish: Faith and possibility in algorithmic work. Big Data & Society. Available at: https://doi.org/10.1177/2053951717751552 (accessed 2 February 2019).

58.

Vargas

(2010) The face of Facebook. The New Yorker, 20 September. Available at: https://www.newyorker.com/magazine/2010/09/20/the-face-of-facebook (accessed 20 February 2019).

59.

Woo

(2018) Getting a Life: The Social Worlds of Geek Culture. Quebec: McGill-Queen’s University Press.

‘How can you not be romantic about baseball?’ Or how we are platonic about data

Abstract

Keywords

Get full access to this article

References