Version 1
: Received: 2 August 2023 / Approved: 3 August 2023 / Online: 4 August 2023 (08:16:40 CEST)
Version 2
: Received: 12 August 2023 / Approved: 14 August 2023 / Online: 15 August 2023 (09:04:45 CEST)
Version 3
: Received: 12 October 2023 / Approved: 13 October 2023 / Online: 16 October 2023 (08:35:18 CEST)
How to cite:
Bucea Manea Tonis, R.; Savin, P.S.; Talaghir, L.; Mindrescu, V. Machine Learning Tools Applied for Training Optimization in Handball. Preprints2023, 2023080372. https://doi.org/10.20944/preprints202308.0372.v2
Bucea Manea Tonis, R.; Savin, P.S.; Talaghir, L.; Mindrescu, V. Machine Learning Tools Applied for Training Optimization in Handball. Preprints 2023, 2023080372. https://doi.org/10.20944/preprints202308.0372.v2
Bucea Manea Tonis, R.; Savin, P.S.; Talaghir, L.; Mindrescu, V. Machine Learning Tools Applied for Training Optimization in Handball. Preprints2023, 2023080372. https://doi.org/10.20944/preprints202308.0372.v2
APA Style
Bucea Manea Tonis, R., Savin, P.S., Talaghir, L., & Mindrescu, V. (2023). Machine Learning Tools Applied for Training Optimization in Handball. Preprints. https://doi.org/10.20944/preprints202308.0372.v2
Chicago/Turabian Style
Bucea Manea Tonis, R., Laurentiu-Gabriel Talaghir and Veronica Mindrescu. 2023 "Machine Learning Tools Applied for Training Optimization in Handball" Preprints. https://doi.org/10.20944/preprints202308.0372.v2
Abstract
Analytics have become increasingly popular among sports in recent years, providing valuable information regarding physical and mental health. Many athletes have found that analytics during training can help to improve their performance, reduce the risk of injury, and enhance their overall well-being. This paper aims to improve the results of handball players by applying a method for measuring the influence of different trials on aggregate performance calculated per each athlete in order to qualify for the Olympic Games. By separating the isolated action of each trial, the result is an additional influence caused by the interaction of factors or the simultaneous action. That might explain why the neuromuscular feedback loop is utterly necessary to perform handball motor actions. Further on, the ML analysis can help identify areas for improvement, optimize training programs, and enhance overall team performance.
Social Sciences, Tourism, Leisure, Sport and Hospitality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
15 August 2023
Commenter:
Radu BUCEA-MANEA-ȚONIȘ
Commenter's Conflict of Interests:
Author
Comment:
We added the whole data source along with the factorial (fact program) intermediate results and the ML extrapolated results (Bayes) based on CR-T's previous analysis. We also detailed the conditions of the experiment like the number of national team players, the number of trials for each test (acceleration, aerobic, and jumping) per athlete, and the final selected team for the Games. The language was proofread by native English speakers.
Commenter: Radu BUCEA-MANEA-ȚONIȘ
Commenter's Conflict of Interests: Author