[AISWorld] Special Issue on Analytics and Machine Learning in Sports Industry

[Asamoah, Daniel A]

Apologies for cross-posting
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CFP Topic: Special Issue on Analytics and Machine Learning in Sports Industry

Journal: Artificial Intelligence in Business (AI in Business)

Guest Co-Editors:
Daniel Asamoah, Ph.D., Associate Professor, Information Systems and Supply Chain Management, Wright State University, Dayton, OH, USA
Nana Baah Gyan, Ph.D., Lecturer, Central University, Miotso, Ghana
Vishal Shah, Ph.D., Central Michigan University, Mount Pleasant, Michigan
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Introduction:
Data analytics in the sports business industry is not new. In recent times, however, analytics has been applied at a far greater latitude to all facets and kinds of business in sport. Applications encompass but are by no means limited to player management, injury recovery, player fitness, player evaluation, and game-day strategies. Analytics is also applied to sports-associated business models regarding contracts, advertisement, and franchise management. This Article Collection serves three main purposes:

  *   Explore both practical and theoretical research about the use of machine learning and artificial intelligence (ML/AI) to advance sports business in general.
  *   Identify challenges and bottlenecks in sports management that can be addressed with data analytics. Issues may come from all stakeholders' perspective including athletes, coaches, team owners/managers, media, financiers etc.
  *   Explore opportunities to leverage ML/AI for all types of sports and its business management.
We encourage papers that relate to either individual or group sports. Papers may also be on a single sport or multiple sport disciplines.

Sample topics of interest for this special issue include but are not limited to:

  *   Sports injuries
  *   Player rotation
  *   Player performance
  *   Visualization in sports
  *   Game day strategies
  *   Fans participation and involvement
  *   Player recruitment, evaluation and management
  *   Sports revenue management (ticket pricing, season ticket sales, etc.)
  *   Contract negotiation
  *   Identification of fair and optimal rankings of teams (especially important in college sports--used for football and basketball rankings for end of season playoff picks)
  *   Prediction and management of spectator attendance
  *   Prediction of the game results/outcomes (wins, spread, etc.) especially important in betting/gaming/gambling
This article collection welcomes diverse article types, including Original Research, Reviews, Hypothesis & Theory papers, Application papers, and Perspective Papers. Upon consultation with the Editors, we may also include, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.
Submission may not be under review at any other journal while it is under review at the Frontiers in Artificial Intelligence journal, within the section of AI in Business, and it may not have been previously published in its current form or accepted for publication in a journal. Presentations at conferences, appearances in conference proceedings, and working papers posted online are typically not considered as previous publication, and such submissions are welcomed as long as they fit any article type allowed in the journal. Authors may also consider expanding their conference papers by adding novel content with respect to previous versions. We encourage that you incorporate comments from previous presentations into your final submission for review.
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Link to submission page:
https://www.frontiersin.org/research-topics/10817/analytics-and-machine-learning-in-sports-industry

Format/style of submission:
Papers should follow the author guidelines in the following link:
https://www.frontiersin.org/journals/artificial-intelligence/sections/ai-in-business#author-guidelines

Important dates to consider:

-          Manuscript submission due: December 10th, 2019

-          Publication: May, 2020
For more questions, contact us at daniel.asamoah at wright.edu<mailto:daniel.asamoah at wright.edu>.
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Daniel A. Asamoah, Ph.D.
Associate Professor of Management Information Systems
201 Rike Hall, Raj Soin College of Business
Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435
Office: 937-775-2295 Fax: 937-775-3545
Web: https://people.wright.edu/daniel.asamoah
Blog: http://blogs.wright.edu/learn/daniel-asamoah/
[RSCOB_AACSB_ABET_CAC]

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