Akademik Veri Yönetim Sistemi
APPLIED ARTIFICIAL INTELLIGENCE, cilt.35, sa.5, ss.393-402, 2021 (SCI-Expanded)
The aim of this study is to estimate the teams' league rankings at the end of the season by using different parameters peculiar to soccer with artificial neural networks (ANNs). In this study, the values belonging to stealing the ball, number of passes (pass on target, forward pass, and pass before goal scoring), number of possessions during the match, attack time resulting in the goal scoring and number of shots in 1140 competitions played in 2015/2016, 2016/2017, and 2017/2018 England Premier League seasons have been evaluated. Season ranking in the 2017/2018 season has been estimated by analyzing the data in the first two seasons (2015/2016, 2016/2017). All data have been separated randomly for training and test. League ranking has been modeled numerically as 0 and 1. Because the generated value is between 0 and 1, the league ranking has been obtained by multiplying this value by 100 for a trained network. Thanks to the ANN model developed by training and testing according to the findings, the training, validation, test, and all regression values of the English Premier League have been obtained as 0.99779, 0.98123, 0.96981, and 0.98769, respectively. With respect to this result, it has been seen that number of shot, stealing the ball, attack time, and number of possessions parameters are determinant in team ranking at the end of the season along with the other parameters in the England Premier League. We think that analyzing matches with the ANN model provides fast and objective results for team managers, trainers, athletes, and betting shops.