Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players' performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players' athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radialbasis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with R 2 scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies. INDEX TERMS Artificial intelligence, athletic performance, machine learning models, radial-basis function neural network.
Objective This study aimed to compare the anxiety and narcissism levels of different performance groups in female handball players. Methods A total of 59 athletes between the ages of 15 and 37 participated in the study, taking the first 4 places from the Turkish Republic of Northern Cyprus senior women handball 1st league in the 2017–2018 season. Wingate peak power (WPP), Wingate average power (WAP), handball agility test (HAST), 10 m speed (10S), 20 m speed (20S), 20m shuttle run (SR), hands on waist vertical jump (HEVJ), hands free vertical jumping (HFVJ) test, Beck anxiety scale (BAI), 5-factor narcissism scale—short form (FFNI-SF), and sociodemographic data form were used. The athletes were divided into upper performance (UPG) and lower performance groups (LPG) using the median value according to the results of the physical measurement tests (FST). Results It was determined that the anxiety level of the participants in the LPG group was higher than that in the UPG group. The narcissism level of the participants in the UPG group was found to be higher than that in the LPG group. The scores of consent seeking, arrogance, leader/authority, insecurity, claiming rights, exhibitionism, carelessness, lack of empathy, and adventurousness were higher than LPG. In the correlation analysis, a positive and low level of relationship between anxiety and 20S and a negative and low level of significant relationship between HEJV were found. It was observed that there was a positive and low level significant relationship between narcissism and WPP, HFJV, and HEJV. It has been revealed that anxiety and narcissism variables have a predictive effect on the physical performance average score. Conclusion The findings suggest that in female handball players, high levels of narcissism may affect the performance positively and high anxiety levels negatively. As a result of this study, it was revealed that anxiety and narcissism have a predictive effect on physical performance average score in women’s handball.
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