This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.
Louis Passfield and James G. Hopker
Patrick Kealy, Yatin Shastri, Francisco Battistini, Tyler Durrell, Jeong Huh and Nola Agha
Bloomberg Sports uses sports analytics to create advanced decision tools for professional teams and fantasy sports users. Their success in both the business and consumer markets stems from vital partnerships with Major League Baseball Advanced Media (MLBAM), Yahoo!, ESPN, and CBSSports. In a period of increased domestic competition, Bloomberg Sports is searching for the most appropriate international growth strategy. This expansion effort recently was aided by a new joint venture with IMG. By analyzing the market, industry, competitors, and products, important decisions can be made to help Bloomberg Sports expand and grow.
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.
Sport Teams’ Mobile Apps Brandi Watkins * Regina Lewis * 9 2014 7 3 399 416 10.1123/IJSC.2014-0009 Book Review The Comprehensive Guide to Careers in Sports (2nd ed.) Paul M. Pedersen 9 2014 7 3 417 419 10.1123/IJSC.2014-0055 Conference Review The MIT Sloan Sports Analytics Conference Michael
Khaya Morris-Binelli, Sean Müller and Peter Fadde
use of sports analytics, which is the use of predictive statistics to answer questions in sport, such as which players a team should recruit and how players are likely to perform under game situations ( Rees, Rakes, & Deane, 2015 ). A branch of sports analytics unique to baseball is sabermetrics
Editorial Is There Risk in Exercise Testing of Athletes? Carl Foster 8 2017 12 7 849 850 10.1123/ijspp.2017-0290 Invited Brief Review A Mine of Information: Can Sports Analytics Provide Wisdom From Your Data? Louis Passfield * James G. Hopker * 8 2017 12 7 851 855 10.1123/ijspp.2016
Youri Geurkink, Gilles Vandewiele, Maarten Lievens, Filip de Turck, Femke Ongenae, Stijn P.J. Matthys, Jan Boone and Jan G. Bourgois
the session Rate of Perceived Exertion (sRPE) . Paper presented at: Machine learning and data mining for sports analytics ECML/PKDD ; 2017 ; Skopje, Macedonia . 27. Bartlett JD , O’Connor F , Pitchford N , Torres-Ronda L , Robertson SJ . Relationships between internal and external
Rob Gray, Anders Orn and Tim Woodman
sets of studies bring up an important issue that, to our knowledge, has not been previously studied: How does information about an opponent’s tendencies affect potential pressure-induced performance errors? With the recent proliferation of the use of sports analytics in most sports, teams have begun
Andrea Nicolò, Marco Montini, Michele Girardi, Francesco Felici, Ilenia Bazzucchi and Massimo Sacchetti
: a new target to HIIT . J Physiol . 2016 ; 594 ( 24 ): 7169 – 7170 . PubMed ID: 27976397 doi:10.1113/JP273466 10.1113/JP273466 27976397 21. Passfield L , Hopker JG . A mine of information: can sports analytics provide wisdom from your data? Int J Sports Physiol Perform . 2017 ; 12 ( 7