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N. David Pifer

By Daniel Memmert and Dominik Raabe. Published in 2018 by Routledge (174 pgs.) ISBN: 978-0-8153-8154-9 (hardback) Source: Routledge (Taylor & Francis Group) $150.00 (hardback); $40.95 (paperback); $20.48 (ebook) From the onset, Data Analytics in Football describes itself as a resource

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David Pierce and Geoffre Sherman

Students are placed into a consulting role with SPT, a sport marketing agency hired to help a sports organization create a new strategy for video content creation on social media. Students are provided a large data set in Tableau with analytics that hold the key to increasing the team’s engagement and views of videos on social media. Can your students find the insights in the data to drive a new video strategy for social media? Can they turn those insights into a creative content plan that will engage and win fans in the future? Students will have the opportunity to demonstrate creativity and innovation, data-based decision making, and digital literacy.

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Patrick Ward, Johann Windt and Thomas Kempton

The application of scientific principles to inform practice has become increasingly common in professional sports, with increasing numbers of sport scientists operating in this area. The authors believe that in addition to domain-specific expertise, effective sport scientists working in professional sport should be able to develop systematic analysis frameworks to enhance performance in their organization. Although statistical analysis is critical to this process, it depends on proper data collection, integration, and storage. The purpose of this commentary is to discuss the opportunity for sport-science professionals to contribute beyond their domain-specific expertise and apply these principles in a business-intelligence function to support decision makers across the organization. The decision-support model aims to improve both the efficiency and the effectiveness of decisions and comprises 3 areas: data collection and organization, analytic models to drive insight, and interface and communication of information. In addition to developing frameworks for managing data systems, the authors suggest that sport scientists’ grounding in scientific thinking and statistics positions them to assist in the development of robust decision-making processes across the organization. Furthermore, sport scientists can audit the outcomes of decisions made by the organization. By tracking outcomes, a feedback loop can be established to identify the types of decisions that are being made well and the situations where poor decisions persist. The authors have proposed that sport scientists can contribute to the broader success of professional sporting organizations by promoting decision-support services that incorporate data collection, analysis, and communication.

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

The question of how and why users engage in sport digital communication endures. In this study, structuration theory is employed to examine how social-media users exercise preferences in the creation of content as they respond to a variety of macrolevel factors pertaining to college football—the type of game, team strength, conference membership, market characteristics, etc. Through hierarchical regression analysis, the results indicate that the presence and timing of college football games, as well as team strength and game outcome, are significant determinants for the patterns of online content generation. As such, the study advances the theoretical, methodological, and managerial inquiry of user-generated content on sport social-media platforms through a Big Data analytics approach.

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William A. Sands, Ashley A. Kavanaugh, Steven R. Murray, Jeni R. McNeal and Monèm Jemni

Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in “big data” analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.

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Dan M. Cooper

Children are the most naturally physically active human beings; reduced physical activity is a cardinal sign of childhood disease, and exercise testing provides mechanistic insights into health and disease that are often hidden when the child is at rest. The physical inactivity epidemic is leading to increased disease risk in children and, eventually, in adults in unprecedented ways. Cardiopulmonary exercise testing (CPET) biomarkers are used to assess disease severity, progress, and response to therapy across an expanding range of childhood diseases and conditions. There is mounting data that fitness in children tracks across the life span and may prove to be an early, modifiable indicator of cardiovascular disease risk later in life. Despite these factors, CPET has failed to fulfill its promise in child health research and clinical practice. A major barrier to more accurate and effective clinical use of CPET in children is that data analytics and testing protocols have failed to keep pace with enabling technologies and computing capacity. As a consequence, biomarkers of fitness and physical activity have yet to be widely incorporated into translational research and clinical practice in child health. In this review, the author re-examines some of the long-held assumptions that mold CPET in children. In particular, the author suggests that current testing strategies that rely predominantly on maximal exercise may, inadvertently, obfuscate novel and clinically useful insights that can be gleaned from more comprehensive data analytics. New pathways to discovery may emanate from the simple recognition that the physiological journey that human beings undertake in response to the challenge of exercise may be far more important than the elusive destination of maximal or peak effort.

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-0023 CASE STUDY 2 Using Data Analytics to Create a Digital Strategy That Drives Engagement and Views on Social Media David Pierce * Geoffre Sherman * 01 01 2020 9 S1 S9 S12 10.1123/cssm.2019-0028 cssm.2019-0028 CASE STUDY 3 California Streamin’: Developing an Integrated Social Media Strategy to Attract

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: The Sociology of Survival and Success in Organized Team Sports Emma S. Ariyo 25 10 2019 2019 1 11 2019 33 6 572 573 10.1123/jsm.2019-0307 jsm.2019-0307 Data Analytics in Football: Positional Data Collection, Modeling, and Analysis N. David Pifer 1 11 2019 33 6 574 574 10.1123/jsm.2019-0308 jsm

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Lynley Ingerson and Michael L. Naraine

, Franchise Management, and Human Resource Management. The final part of the arrangement is to then attract and recruit managers at the second tier in the areas of Membership and Sales, Community Engagement, Stadium Management, and Data Analytics. Donelly feels the company is a good match with the Braves and

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Gashaw Abeza

way, somehow, for data analytics and to be informed from that and repurpose our strategies, when necessary. In our content production, for example, we had independent content-producing departments such as TV broadcast, website, social media, and a game-day (our in-game production) operation. We