A learning-based system for predicting sport injuries

Liu, Guangying; Sun, Hua; Bai, Wanjian; Li, Hongmei; Ren, Zhigang; Zhang, Zhongde; Yu, Lei

doi:10.1051/matecconf/201818910008

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…It contained the data that were collected from 46 professional rugby league players throughout the 2015 NRL season, including some data collected by GPS (Global Positioning System). Similar approaches were presented in [ 13 , 14 ]. In these articles, the authors proposed their systems for predicting the possibility of the injury of the football players.…”

Section: Related Work and Existing Solutionsmentioning

confidence: 89%

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: 89%

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

Ćwiklinski

Giełczyk

Choraś

2021

Entropy

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Sports Injury Prediction System using Random Forest Classifier

Shringarpure,

Shetty,

Surve

et al. 2022

ITM Web Conf.

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One of the largest growing industries in the modern-day world is the sporting industry. Currently valued at around 500 billion USD, with a growth scope of exponential potential, its ability to attract investors is incredible. And just like any other investment. It is part andparcel of the investor’s fiscal responsibility to take good care of their assets. The biggest assets in the sporting industry are of course the players, and the greatest threat to said assets is injuries. We take into consideration said factors and deem it important to solve said issues, and understanding the money involved, the industry sides with us too. We seek to solve the said problemby taking into account all previous injury records and datasets of various players and predicting the kind, number, and severity of the injuries in the future. We seek to create a methodology for such prediction, which applies to all and any sports, being one of the only such models of its kind.

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