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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
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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:
- Abstract submission due: September 15th, 2019
- Manuscript submission due: December 10th, 2019
- Publication: May, 2020
For more questions, contact us at
daniel.asamoah@wright.edu<mailto:daniel.asamoah@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/
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