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Applied Sciences
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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: 20 December 2024 | Viewed by 6294

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.

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Keywords

  • machine learning
  • sports
  • exercise

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Published Papers (3 papers)

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Research

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15 pages, 2940 KiB  
Article
Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling
by Diogo Duarte Carvalho, Márcio Fagundes Goethel, António J. Silva, João Paulo Vilas-Boas, David B. Pyne and Ricardo J. Fernandes
Appl. Sci. 2024, 14(12), 5218; https://doi.org/10.3390/app14125218 - 16 Jun 2024
Cited by 1 | Viewed by 1671
Abstract
Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a [...] Read more.
Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a combination of demographic, training, anthropometric, and biomechanical variables (inputs) through XAI. Forty-seven swimmers (16 males), after completing a training questionnaire (background and duration) and anthropometric assessment, performed, in a randomised order, a 25 m front crawl and three countermovement jumps, at maximal intensity. The predicted World Aquatics points (516 ± 159; mean ± SD) were highly correlated (r2 = 0.93) with the 529 ± 158 actual values. The duration of swimming training was the most important variable (95_SHAP), followed by the countermovement jump impulse (37_SHAP), both with a positive effect on performance. In contrast, a higher percentage of fat mass (21_SHAP) corresponded to lower World Aquatics points. Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance. Height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance. The XAI modelling highlights the usefulness of specific training, technical and physical testing, and anthropometric factors for monitoring swimmers. Full article
(This article belongs to the Special Issue Machine Learning in Sports: Practical Applications for Practitioners)
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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://doi.org/10.3390/app14083191 - 10 Apr 2024
Cited by 1 | Viewed by 1115
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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21 pages, 666 KiB  
Systematic Review
Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
by Tatiana Sampaio, João P. Oliveira, Daniel A. Marinho, Henrique P. Neiva and Jorge E. Morais
Appl. Sci. 2024, 14(13), 5517; https://doi.org/10.3390/app14135517 - 25 Jun 2024
Cited by 1 | Viewed by 2755
Abstract
(1) Background: Tennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: [...] Read more.
(1) Background: Tennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: Machine learning applications show promise in psychological state monitoring, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. Coaches can leverage wearable technologies for personalized psychological state monitoring, data-driven talent identification, and tactical insights for informed decision-making. (4) Conclusions: Machine learning offers coaches insights to refine coaching methodologies and optimize player performance in tennis. By integrating these insights, coaches can adapt to the demands of the sport by improving the players’ outcomes. As technology progresses, continued exploration of machine learning’s potential in tennis is warranted for further advancements in performance optimization. Full article
(This article belongs to the Special Issue Machine Learning in Sports: Practical Applications for Practitioners)
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