Version 1
: Received: 31 May 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (07:37:07 CEST)
How to cite:
Malikov, D.; Kim, J. Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types. Preprints2023, 2023060052. https://doi.org/10.20944/preprints202306.0052.v1
Malikov, D.; Kim, J. Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types. Preprints 2023, 2023060052. https://doi.org/10.20944/preprints202306.0052.v1
Malikov, D.; Kim, J. Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types. Preprints2023, 2023060052. https://doi.org/10.20944/preprints202306.0052.v1
APA Style
Malikov, D., & Kim, J. (2023). Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types. Preprints. https://doi.org/10.20944/preprints202306.0052.v1
Chicago/Turabian Style
Malikov, D. and Jaeho Kim. 2023 "Comparative Evaluation of Machine Learning Models for Predicting Soccer Injury Types" Preprints. https://doi.org/10.20944/preprints202306.0052.v1
Abstract
Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. Supervised classification machine learning models are applied in this paper. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.
Keywords
Soccer; data analysis; soccer injury type; classification machine learning models
Subject
Engineering, Other
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.
