A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness
Abstract Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental tou...
Saved in:
Main Authors: | Xin Zhang, Zhikang Lin, Song Gu |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-87794-y |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The impact of precompetition state on athletic performance among track and field athletes using machine learning
by: Yuting Zhang, et al.
Published: (2025-02-01) -
Evaluation of Athletic Identity in Elite Fencers
by: Yusuf Barsbuğa
Published: (2021-08-01) -
Engagement and Follower Growth: Social Media Strategies of Swiss Athletes at the Paris Olympics
by: Larssyn Staley, et al.
Published: (2025-01-01) -
Mental health challenges faced by professional athletes
by: Dawid Szczepanek, et al.
Published: (2025-02-01) -
A narrative review on the role of cognition, nutrition and energy availability in athletes of competitive sports to combat RED-S
by: Subalatha M., et al.
Published: (2025-01-01)