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Nicholas M. Watanabe, Stephen Shapiro, and Joris Drayer

collection, storage, transfer, and analysis of data have advanced the methods used in “Moneyball” to new heights and created more confidence in data’s ability to inform and predict. Indeed, big data analytics has created a surge in innovation throughout the sport industry, spawning many products and services

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Grace Yan, Dustin Steller, Nicholas M. Watanabe, and Nels Popp

). Media studies 2.0: A response . Interactions: Studies in Communication & Culture, 1 ( 1 ), 147 – 157 . George , G. , Osinga , E. , Lavie , D. , & Scott , B. ( 2016 ). Big data and data science methods for management research . Academy of Management Journal, 59 ( 5 ), 1493 – 1507 . doi

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Daniel Weimar, Brian P. Soebbing, and Pamela Wicker

processed at high velocity, are referred to as Big Data ( Murdoch & Detsky, 2013 ; Spaaij & Thiel, 2017 ). The availability of Big Data results in an increasing need of data analytics to adequately deal with the enormous amount of data at hand ( Baerg, 2017 ). Data analytics are important to transfer the

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Hua Gong, Nicholas M. Watanabe, Brian P. Soebbing, Matthew T. Brown, and Mark S. Nagel

research by utilizing a big data approach to measure consumer discussion and sentiments toward tanking on social media and then analyzing whether these factors are related to attendance demand at National Basketball Association (NBA) games. The adoption of big data in management research is an important

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JSM Journal of Sport Management 0888-4773 1543-270X 1 05 2021 35 3 10.1123/jsm.2021.35.issue-3 Special Issue: Big Data and Analytics in Sport Management Guest Editors: Nicholas Watanabe, Stephen Shapiro, and Joris Draye EDITORIAL 10.1123/jsm.2021-0067 ARTICLES 10.1123/jsm.2020-0147 10.1123/jsm.2020

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Brad Millington and Rob Millington

This paper explores the articulations of sport and ‘Big Data’—an important though to date understudied topic. That we have arrived at an ‘Age of Big Data’ is an increasingly accepted premise: the proliferation of tracking technologies, combined with the desire to record/monitor human activity, has radically amplified the volume and variety of data in circulation, as well as the velocity at which data move. Herein, we take initial steps toward addressing the implications of Big Data for sport (and vice versa), first by historicizing the relationship between sport and quantification and second by charting its contemporary manifestations. We then present four overlapping postulates on sport in the Age of Big Data. These go toward both showing and questioning the logic of ‘progress’ said to lie at the core of sport’s nascent statistical turn. We conclude with reflections on how a robust sociology of sport and Big Data might be achieved.

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Ryan Spalding

By Richard D. Cramer. Published 2019 by University of Nebraska Press , Lincoln, NE. $28.95 . 256 pp. ISBN 978-1-4962-1205-4 When Big Data Was Small is a memoir by Richard Cramer, one of the earliest and most influential people in the foundation and advancement of the field known as sport

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Gashaw Abeza

his native Poland; helping build out Google offices in Ann Arbor, Michigan; and building out the midmarket sales team for Google Canada. Lorenc sat with Gashaw Abeza to discuss Google and sport, the impact of COVID, and, mainly, big data in the sport industry. Abeza : Let’s start by discussing COVID

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Jos J. de Koning

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Rienk M.A. van der Slikke, Daan J.J. Bregman, Monique A.M. Berger, Annemarie M.H. de Witte, and Dirk-Jan (H.) E.J. Veeger

-to-use, large-scale, objective, and increasingly precise measurement of performance. Those benefits enable data science in adapted sports research that is traditionally characterized by small participant numbers. Such a big data approach with continued measurements in all conditions might offer an alternative