A Conceptual Framework for the Generation of Adaptive Training Plans in Sports Coaching

Zahran, Laila; El-Beltagy, Mohamed A.; Saleh, Mohamed

doi:10.1007/978-3-030-31129-2_62

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Thanks to this kind of solution, a person can constantly monitor their health. On the other hand, in [ 25 ], a ML-based framework for training plan generation in coaching was proposed. In the sport jargon, a person working on transfers and statistics is called a scout.…”

Section: Related Work and Existing Solutionsmentioning

confidence: 99%

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Ćwiklinski

Giełczyk

Choraś

2021

Entropy

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Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

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Section: Related Work and Existing Solutionsmentioning

confidence: 99%

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Ćwiklinski

Giełczyk

Choraś

2021

Entropy

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“…Zahran et al [22] proposes a conceptual framework for generating adaptive weekly sports training plans based on internal training load, physiological constraints, and behavioral-change features. The drawback is that the approach has not been used in practice outside of the preexisting training data used to construct it.…”

Section: A Sport Training Generationmentioning

confidence: 99%

A Modified Evolutionary Algorithm for Generating the Cycling Training Routes

Rajšp

Fister

2022

IEEE Access

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We present a novel method for generating cycling training routes from geographical property graphs based on an Evolutionary Algorithm. The algorithm operators of crossover and mutation are adjusted for use in the Property Graph domain. Data fusion of geographical data from the OpenStreetMap, EU-DEM digital surface model, and existing training records is performed as a basis of the intersections-paths' property graph. The proposed approach allows route generation based on their starting and ending points in the property graph and their distance and ascent. A property graph of all intersections and cycling roads is shown and generated for the regions of Podravje and Pomurje. The property graph used in the proposed algorithm's feasibility demonstration is shown. This is done by presenting four different cases of routes that were generated with our algorithm. The algorithm allows for generating classic cycling routes of A to B nature, routes with more than two fixed points, as well as cyclical training routes. The research is concluded by offering further directions on route generation research.

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“…The purpose of this paper is to improve the efficiency of the current sports training management. Besides, this paper also targets on developing the scientific and effective sports training plans and training methods in line with college students (Zahran et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Recognition Method of Sports Training Based on Intelligent Information Platform

Yao

Wang

2023

International Journal of E-Collaboration

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With the wide development of sports nowadays, many kinds of sport applications have appeared and changed the living styles of people all around the world. There are many factors that are important for sports, such as the environment and exercise. At present, there are still some shortcomings in sports training management all around the world. For example, the unscientific is unclear, the training purpose is not strong, and the assistant decision-making means are backward. In this way, these issues jointly make the sport training process difficult. In this way, this paper applies the multi-agent technology to the system, designs an intelligent information sports training management and recognition method based on intelligent agents, and carries on the concrete implementation in certain environments. The corresponding experimental results indicate that the accuracy and intelligence of the training recognition system can be improved greatly and effectively.

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A Conceptual Framework for the Generation of Adaptive Training Plans in Sports Coaching

Cited by 8 publications

References 13 publications

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

A Modified Evolutionary Algorithm for Generating the Cycling Training Routes

Recognition Method of Sports Training Based on Intelligent Information Platform

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