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Moneyball: Another Promise of Digital Humanities?

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Politische Theorie im Film

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Abstract

The story of the film is relatively simple: a baseball manager hired by a low budget team, having to play against richer teams with bigger budget and thus, a wider choice to the best players. This manager turns to hiring an advisor, someone well versed in Big Data management.

The story though, deals with what may be one of the most profound changes in our times: the possibility of gathering, processing and storing enormous amounts of information to a degree and scale never before conceived.

The fact that digital data has reached this unimagined size has triggered what has been dubbed the Big Data Revolution. According to (Mayer-Schönberger and Cukier, Moneyball: The art of winning an unfair game, 2013) the amount of stored information in the world grows four times faster than the world economy, and the so-called data processing power, around nine times faster. Thus, “datifcation” of the world and everything in it is becoming a tangible reality in many realms.

The Social Sciences have witnessed previous epistemic promises of understanding the world through the sorting of empirical information. In fact, quantitative analysis previous to big data, had usually spawned from the idea of sampling collected data. This is but the refinement of a selected section of the information universe, under certain methodological pre-requisites. But if data gathering, processing and storage stops being a problem the whole epistemic proposal of understating reality is reversed from a causation principle, to a data correlation perspective. Nowadays, data is almost infinite. It is how we crunch it what is decisive.

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Notes

  1. 1.

    An idea not completely of his own, since it relied on theoretical assumptions from Feuerbach’s Essence of Christianity (1841), Marx in his Economic and Philosophic Manuscripts (1844) projects the idea of “entfremdung” towards the creative relation of the worker with his productive labor. The whole explanation and description of the capitalist mode of production is threefold: (a) Alienation of the worker from the product of his work; (b) Alienation of the worker from the act of producing; and (c) Alienation of the worker from himself (this in turns, Marx defines as his genre species or Gattungswesen. This in turn, is in itself completely revolutionary since the capitalist mode of production alters all former “traditional” modes of production.

  2. 2.

    This giant is mentioned in a number of plays from the fifth century BC, notably the Suppliants, Prometeus Bound by Aeschylus and the Phoenician Women by Euripides. The roman poet Ovid also mentions this particular figure in his Metamorphosis. This Panoptes is later slain by the god Hermes and his eyes, send by the goddess Hera to the feathers in a Peacock’s tail. The teaching may be that observation can be defeated or improved by accurate communication and the whole hermeneutic process of any empirical phenomena recorded.

  3. 3.

    See Chap. 7: The Labor-Process and the Process of Producing Surplus-Value in any translation of Das Kapital (2009) Anaconda Verlag Köln (nach der Ausgabe Berlin, Gustav Kiepenhauer Verlag, 1932), also in English: Capital A Critique of Political Economy, 2004, Penguin.

  4. 4.

    This and the so called 14 production principles can be found in his work “Administration industrielle et générale—prévoyance organisation—commandement, coordination—contrôle” (Industrial and General Administration) 1930. Translated by J.A. Coubrough, London: Sir Isaac Pitman & Sons.

  5. 5.

    See Chap. 7: The Labor-Process and the Process of Producing Surplus-Value in any translation of Das Kapital (2009) Anaconda Verlag Köln (nach der Ausgabe Berlin, Gustav Kiepenhauer Verlag, 1932), also in English: Capital A Critique of Political Economy, 2004, Penguin.

  6. 6.

    This and the so called 14 production principles can be found in his work “Administration industrielle et générale—prévoyance organisation—commandement, coordination—contrôle” (Industrial and General Administration) 1930.Translated by J.A. Coubrough, London: Sir Isaac Pitman & Sons.

  7. 7.

    Egon Zehnder (2014, p. 4) elaborates on the matter to produce what he calls the “Six Guiding Principles” for leaders to operate with Big Data, namely:

    1. 1.

      Analytics should start with business problems.

    2. 2.

      Analytics needs translators.

    3. 3.

      Analytics requires data scientists of different flavors.

    4. 4.

      The analytics team should help get the job done.

    5. 5.

      All leaders need a working knowledge of data science.

    6. 6.

      Analytics should have a seat at the top table.

    All six points assume that data has been gathered and that the most difficult point is to know how to make use of it. Data has therefore to be translated, and should be focused under several perspectives since in itself it is both neutral and meaningless.

  8. 8.

    I suggest Chap. 4 in Silver (2012) where he explains a case of excellent profit from big data use: the weather forecasting industry.

  9. 9.

    For more information I would recommend N. Negroponte’s book “Being Digital” (1996) most particularly the chapter dedicated to the DNA of Information.

  10. 10.

    Closer to the possibilities of a “biopolitical” design in daily life, Big Data platforms are now able to synthesize unstructured data and applications from the fields of Sociology, Psychology, Political Sciences etc. Due to the unprecedented power in working with almost unimaginable amounts of information at very fast speed, this is becoming possible even in fields formerly thought distant from statistical models such as Social Networks or how individuals connect, influence, and are influenced by a network of friends, peers and acquaintances. Please see Behavioral Economics: why rational choice theory may not answer some of the world’s pressing economic problem and Language and Semantics: combination of computer science, mathematics and linguistics, Accessed on 27 April 2015, URL: http://www.xaxis.com/es/blog/view/a-deep-dive-into-big-data-intelligence#sthash.MlEkxh5h.V8nAolR6.dpuf.

  11. 11.

    It is not the purpose of this chapter to elaborate too much on probabilistic but the reader should be reminded that Bayesian analysis is on theoretical terms, the only possible refutation (if flawed) to the induction problem presented by Hume. In his paper “An Essay toward solving a problem in the doctrine of chances” (1763). Bayes is probably the first thinker to correlate probability as a rational factor, considering the mathematical condition that the more any phenomena occurs, under certain regular conditions, the more it is likely to occur (the curve never reaches 100 % possibility anyway). This is antithesis to the Humean approach which demonstrates the inability to rationally and logically predict the future from a sequence formed in the past. Big Data, and the chances to deepen our correlation of variables almost to unhuman levels, nears the percentage to which reality could be predicted and in turn, manipulated.

  12. 12.

    Arendt does not use this word. She calls the new working force brought by Robert McNamara to write the report, the “problems solvers”. However, the idea of technocracy is implicit in their actions and world view as described by Arendt. Also, the fact that someone coming from the private industry such as McNamara would consider these working profiles, may be consistent with a mensurable and product oriented vision of the world. Etymologically the word is a neologism from two ancient Greek words Techné + Kratos, the former being the practical use or application of scientific knowledge and the later the force or power derived from it.

  13. 13.

    Arendt summarizes her fear of this possible future as follows: “Men who act to the extent that they feel themselves to be the masters of their own future, will forever be tempted to make themselves masters of the past too” (Arendt 1972, p. 12). In a different order of ideas, the people Orwell envisioned as re-writing history to satisfy the will of big brother, could today be resumed in one who, with enough data command, could reshape our whole idea of the past with basically one click. Though exaggerated, this is theoretically possible.

  14. 14.

    It is beyond the scope of this chapter to elaborate on several theories that consider how close Big Data can be to Biopolitics. A valid example may be what Foucault terms ‘biopower’, that is, the form of power being directed to the biological existence of individuals and populations, or what he calls man-as-species-body. This can be manipulated through Big Data in, for example, Biometrics (see Pugliese 2012, Biometrics: Bodies, Technologies, Biopolitics).

  15. 15.

    In a report not about Big Data as a Myth but about the myths people tend to develop towards Big Data, the Greenbiz Group directly says “No matter how ‘big’” an incomplete or otherwise flawed set of data cannot provide actionable information. “The proper analytics can be developed only with a clear understanding of the quality and quantity of available data” (Davies 2014, p. 3). In this case, the “understanding comes from “giving sense to Data, which is to apply a methodology in order to get a result”. Again, Kolakowski’s observation of our need to create patterns where there are none, is present”.

  16. 16.

    The Sabermetrics Manifesto by David Grabiner (Accessed on 27 April 2015, URL: http://seanlahman.com/baseball-archive/sabermetrics/sabermetric-manifesto/).

  17. 17.

    We could resume Laplace’s idea more or less in the following terms: “given a perfect knowledge of the present conditions, we should be able to make perfect predictions”. This complements the Bayesian perspective at least theoretically. But although we know since Parmenides that “perfect knowledge” (episteme) is a desideratum not attainable, it could be argued that having a growing volume of knowledge over the present enhances our chances to get near any possible perfection. Forecasts and predictions in every field, in other words, should become more accurate the more information is accumulated and crunched (we should bear in mind that crunched information through any methodology we may have used, becomes in turn, more raw data for further analysis).

  18. 18.

    For Baseball statistics see: http://mlb.mlb.com/stats/sortable.jsp#elem=%5Bobject+Object%5D&tab_level=child&click_text=Sortable+Player+hitting&game_type='R'&season=2014&season_type=ANY&league_code='MLB'&sectionType=sp&statType=hitting&page=1&ts=1420391944673, accessed on 27 April 2015.

  19. 19.

    Spiegel Online. Accessed on 27 April 2015, URL: http://www.spiegel.de/international/business/big-data-enables-companies-and-researchers-to-look-into-the-future-a-899964.html.

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  1. Münster, Deutschland

    Dr. Ivo Hernandez

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  1. Westfälische Wilhelms-Universität Münster, Münster, Germany

    Ulrich Hamenstädt

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Hernandez, I. (2016). Moneyball: Another Promise of Digital Humanities?. In: Hamenstädt, U. (eds) Politische Theorie im Film. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-07206-3_6

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  • DOI: https://doi.org/10.1007/978-3-658-07206-3_6

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