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Selecting Forward Players in a Football Team using Artificial Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 28 |
| Year of Publication: 2020 |
| Authors: Abraham E. Evwiekpaefe, Emmanuel Bitrus, Fiyinfoluwa Ajakaiye |
10.5120/ijca2020920298
|
Abraham E. Evwiekpaefe, Emmanuel Bitrus, Fiyinfoluwa Ajakaiye . Selecting Forward Players in a Football Team using Artificial Neural Networks. International Journal of Computer Applications. 176, 28 ( Jun 2020), 8-13. DOI=10.5120/ijca2020920298
Abstract
The success of any football team lies in the performance of its players. Determining the best player among a pool of players is a very difficult task. The purpose of this research is to assess the performance skills of forward football players in a football game. To conduct this research, players were randomly selected from different teams across Europe based on their play positions. One hundred (100) forward players were selected for the analysis. Performance analysis was conducted using Artificial Neural Networks (ANN) Multilayer Perception and compared with the J48 classifier. A model based on the ANN Multilayer Perception was trained and developed using secondary data collected from the online Complete Dataset of the FIFA 2017/2018 football season. The analysis was done with the aid of the WEKA data mining tool. The results show that the Multilayer Perception classification had a better performance than the J48 classification.
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