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Applied Sciences
Special Issues
Machine Learning in Sports: Practical Applications for Practitioners
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Machine Learning in Sports: Practical Applications for Practitioners

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 639

Special Issue Editor

Dr. Jorge Morais

E-Mail Website
Guest Editor
Department of Sport Sciences, Instituto Politecnico de Braganca, 5300-252 Braganca, Portugal
Interests: sports; performance; biomechanics; swimming; underwater cameras; drag; independent living; accidental falls; weighing devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The practical applications of machine learning in sports are diverse and encompass multiple domains. Researchers can utilize these techniques to evaluate performance metrics, fine-tune training regimes, and identify areas for improvement. Furthermore, machine learning can assist in injury prevention, player scouting, talent identification, and optimization of game plans based on opponent analysis. Thus, this is a new trend from which athletes and coaches may gain new insights for performance improvement.

This Special Issue invites researchers, data scientists, and practitioners from a wide range of disciplines to contribute original research, reviews, and practical case studies that demonstrate the application of machine learning in sports. Both theoretical and practical submissions are welcome, with the objective of showcasing advances in the field and providing practical insights for those involved in the sporting industry.

Prof. Dr. Jorge E. Morais
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • sports
  • exercise

Published Papers (1 paper)

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Research

12 pages, 1692 KiB  
Article
A Hierarchy of Variables That Influence the Force–Velocity Profile of Acrobatic Gymnasts: A Tool Based on Artificial Intelligence
by Isaura Leite, Márcio Goethel, Pedro Fonseca, João Paulo Vilas-Boas, Lurdes Ávila-Carvalho, Luis Mochizuki and Filipe Conceição
Appl. Sci. 2024, 14(8), 3191; https://0-doi-org.brum.beds.ac.uk/10.3390/app14083191 - 10 Apr 2024
Viewed by 376
Abstract
Jumping performance is considered an overall indicator of gymnastics ability. Acrobatic Gymnastics involves base and top gymnasts, considering the type of training that is performed and the distinct anthropometric traits of each gymnast. This work aims to investigate a hierarchy of variables that [...] Read more.
Jumping performance is considered an overall indicator of gymnastics ability. Acrobatic Gymnastics involves base and top gymnasts, considering the type of training that is performed and the distinct anthropometric traits of each gymnast. This work aims to investigate a hierarchy of variables that influence the force–velocity (F-V) profile of top and base acrobatic gymnasts through a deep artificial neural network model. Twenty-eight first division and elite acrobatic gymnasts (eleven tops and seventeen bases) performed two evaluations to assess the F-V profile during the Countermovement Jump and its mechanical variables, using My Jump 2 (a total of 56 evaluations). A training background survey and anthropometric assessments were conducted. The final model (R = 0.97) showed that the F-V imbalance (F-Vimb) increases with higher force and decreases with higher maximal power, fat percentage, velocity, and height. Coaches should prioritize the development of force, followed by maximal power, and velocity for the optimization of gymnasts’ F-Vimb. For training planning, the influences of body mass and push-off height are higher for the bases, and the influences of years of practice and competition level are higher for the tops. Full article
(This article belongs to the Special Issue Machine Learning in Sports: Practical Applications for Practitioners)
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